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Title: SAFETY ANALYSES AT SIGNALIZED INTERSECTIONS CONSIDERING SPATIAL, TEMPORAL AND SITE CORRELATION.
Creator: Wang, Xuesong, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial,...
Show moreStatistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.
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Date Issued: 2006
Identifier: CFE0001497, ucf:47078
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001497

Title: EXAMINING ROUTE DIVERSION AND MULTIPLE RAMP METERING STRATEGIES FOR REDUCING REAL-TIME CRASH RISK ON URBAN FREEWAYS.
Creator: Gayah, Vikash, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Recent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway....
Show moreRecent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway. Because these models use the real-time traffic data, they are capable of calculating the respective crash risk values as the traffic flow changes along the freeway. The purpose of this study is to examine the potential of two Intelligent Transportation System strategies for reducing the crash risk along the freeway by changing the traffic flow parameters. The two ITS measures that are examined in this research are route diversion and ramp metering. Route diversion serves to change the traffic flow by keeping some vehicles from entering the freeway at one location and diverting them to another location where they may be more efficiently inserted into the freeway traffic stream. Ramp metering alters the traffic flow by delaying vehicles at the freeway on-ramps and only allowing a certain number of vehicles to enter at a time. The two strategies were tested by simulating a 36.25 mile section of the Interstate-4 network in the PARAMICS micro-simulation software. Various implementations of route diversion and ramp metering were then tested to determine not only the effects of each strategy but also how to best apply them to an urban freeway. Route diversion was found to decrease the overall rear-end and lane-change crash risk along the network at free-flow conditions to low levels of congestion. On average, the two crash risk measures were found to be reduced between the location where vehicles were diverted and the location where they were reinserted back into the network. However, a crash migration phenomenon was observed at higher levels of congestion as the crash risk would be greatly increased at the location where vehicles were reinserted back onto the network. Ramp metering in the downtown area was found to be beneficial during heavy congestion. Both coordinated and uncoordinated metering algorithms showed the potential to significantly decrease the crash risk at a network wide level. When the network is loaded with 100 percent of the vehicles the uncoordinated strategy performed the best at reducing the rear-end and lane-change crash risk values. The coordinated strategy was found to perform the best from a safety and operational perspective at moderate levels of congestion. Ramp metering also showed the potential for crash migration so care must be taken when implementing this strategy to ensure that drivers at certain locations are not put at unnecessary risk. When ramp metering is applied to the entire freeway network both the rear-end and lane-change crash risk is decreased further. ALINEA is found to be the best network-wide strategy at the 100 percent loading case while a combination of Zone and ALINEA provides the best safety results at the 90 percent loading case. It should also be noted that both route diversion and ramp metering were found to increase the overall network travel time. However, the best route diversion and ramp metering strategies were selected to ensure that the operational capabilities of the network were not sacrificed in order to increase the safety along the freeway. This was done by setting the maximum allowable travel time increase at 5% for any of the ITS strategies considered.
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Date Issued: 2006
Identifier: CFE0001437, ucf:47054
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001437

Title: A NEW APPROACH TO IDENTIFY THE EXPECTED CRASH PATTERNS BASED ON SIGNALIZED INTERSECTION SIZE AND ANALYSIS OF VEHICLE MOVEMENTS.
Creator: Salkapuram, Hari, Mohamed, Abdel-Aty, University of Central Florida
Abstract / Description: Analysis of intersection crashes is a significant area in traffic safety research. This study contributes to the area by identifying traffic-geometric characteristics and driver demographics that affect different types of crashes at signalized intersections. A simple methodology to estimate crash frequency at intersections based on the size of the intersection is also developed herein. First phase of this thesis used the crash frequency data from 1,335 signalized intersections obtained from...
Show moreAnalysis of intersection crashes is a significant area in traffic safety research. This study contributes to the area by identifying traffic-geometric characteristics and driver demographics that affect different types of crashes at signalized intersections. A simple methodology to estimate crash frequency at intersections based on the size of the intersection is also developed herein. First phase of this thesis used the crash frequency data from 1,335 signalized intersections obtained from six jurisdictions in Florida, namely, Brevard, Seminole, Dade, Orange, and Hillsborough Counties and the City of Orlando. Using these data a simple methodology has been developed to identify the expected number of crashes by type and severity at signalized intersections. Intersection size, based on the total number of lanes, was used as a factor that was simple to identify and a representative of many geometric and traffic characteristics of an intersection. The results from the analysis showed that crash frequency generally increased with the increased size of intersections but the rates of increase differed for different intersection types (i.e., Four-legged intersection with both streets two-way, Four-legged intersection with at least one street one-way, and T-intersections). The results also showed that the dominant type of crashes differed at these intersection types and severity of crashes was higher at the intersections with more conflict points and larger differential in speed limits between major and minor roads. The analysis may potentially be useful for traffic engineers for evaluating safety at signalized intersections in a simple and efficient manner. The findings in this analysis provide strong evidence that the patterns of crashes by type and severity vary with the size and type of intersections. Thus, in future analysis of crashes at intersections, the size and type of intersections should be considered to account for the effects of intersection characteristics on crash frequency. In the second phase, data (crash and intersection characteristics) obtained from individual jurisdictions are linked to the Department of Highway Safety and Motor Vehicles (DHSMV) database to include characteristics of the at-fault drivers involved in crashes. These crashes are analyzed using contingency tables and binary logistic regression models. This study categorizes crashes into three major types based on relative initial movement direction of the involved vehicles. These crash types are, 1) Initial movement in same direction (IMSD) crashes. This crash type includes rear end and sideswipe crashes because the involved vehicles for these crashes would be traveling in the same direction prior to the crash. 2) Initial movement in opposite direction (IMOD) crashes comprising left-turn and head on crashes. 3) Initial movement in perpendicular direction (IMPD) crashes, which include angle and right-turn crashes. Vehicles involved in these crashes would be traveling on different roadways that constitute the intersection. Using the crash, intersection, and at-fault driver characteristics for all crashes as inputs, three logistic regression models are developed. In the logistic regression analyses total number of through lanes at an intersection is used as a surrogate measure to AADT per lane and also intersection type is introduced as a 'predictor' of crash type. The binary logistic regression analyses indicated, among other results, that at intersections with one-way roads, adverse weather conditions, older drivers and/or female drivers increase the likelihood of being at-fault at IMOD crashes. Similar factors associated with other groups of crashes (i.e., IMSD and IMPD) are also identified. These findings from the study may be used to develop specialized training programs by zooming in onto problematic intersections/maneuvers.
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Date Issued: 2006
Identifier: CFE0001208, ucf:46954
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001208

Title: Implementation Strategies for Real-time Traffic Safety Improvements on Urban Freeways.
Creator: Dilmore, Jeremy, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: This research evaluates Intelligent Transportation System (ITS) implementation strategies to improve the safety of a freeway once a potential of a crash is detected. Among these strategies are Variable Speed Limit (VSL) and ramp metering. VSL are ITS devices that are commonly used to calm traffic in an attempt to relieve congestion and enhance throughput. With proper use, VSL can be more cost effective than adding more lanes. In addition to maximizing the capacity of a roadway, a different...
Show moreThis research evaluates Intelligent Transportation System (ITS) implementation strategies to improve the safety of a freeway once a potential of a crash is detected. Among these strategies are Variable Speed Limit (VSL) and ramp metering. VSL are ITS devices that are commonly used to calm traffic in an attempt to relieve congestion and enhance throughput. With proper use, VSL can be more cost effective than adding more lanes. In addition to maximizing the capacity of a roadway, a different aspect of VSL can be realized by the potential of improving traffic safety. Through the use of multiple microscopic traffic simulations, best practices can be determined, and a final recommendation can be made. Ramp metering is a method to control the amount of traffic flow entering from on-ramps to achieve a better efficiency of the freeway. It can also have a potential benefit in improving the safety of the freeway. This thesis pursues the goal of a best-case implementation of VSL. Two loading scenarios, a fully loaded case (90% of ramp maximums) and an off-peak loading case (60% of ramp maximums), at multiple stations with multiple implementation methods are strategically attempted until a best-case implementation is found. The final recommendation for the off-peak loading is a 15 mph speed reduction for 2 miles upstream and a 15 mph increase in speed for the 2 miles downstream of the detector that shows a high crash potential. The speed change is to be implemented in 5 mph increments every 10 minutes. The recommended case is found to reduce relative crash potential from .065 to -.292, as measured by a high-speed crash prediction algorithm (Abdel-Aty et al. 2005). A possibility of crash migration to downstream and upstream locations was observed, however, the safety and efficiency benefits far outweigh the crash migration potential. No final recommendation is made for the use of VSL in the fully loaded case (low-speed case); however, ramp metering indicated a promising potential for safety improvement.
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Date Issued: 2005
Identifier: CFE0000339, ucf:46287
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000339

Title: EXAMINING DYNAMIC VARIABLE SPEED LIMIT STRATEGIES FOR THE REDUCTION OF REAL-TIME CRASH RISK ON FREEWAYS.
Creator: Cunningham, Ryan, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Recent research at the University of Central Florida involving crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of determining the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models are able to calculate the rear-end and lane-change crash risks along the freeway in real-time through the use of static information at various locations along the freeway as well as the real-time traffic data...
Show moreRecent research at the University of Central Florida involving crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of determining the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models are able to calculate the rear-end and lane-change crash risks along the freeway in real-time through the use of static information at various locations along the freeway as well as the real-time traffic data obtained by loop detectors. Since these models use real-time traffic data, they are capable of calculating rear-end and lane-change crash risk values as the traffic flow conditions are changing on the freeway. The objective of this study is to examine the potential benefits of variable speed limit implementation techniques for reducing the crash risk along the freeway. Variable speed limits is an ITS strategy that is typically used upstream of a queue in order to reduce the effects of congestion. By lowering the speeds of the vehicles approaching a queue, more time is given for the queue to dissipate from the front before it continues to grow from the back. This study uses variable speed limit strategies in a corridor-wide attempt to reduce rear-end and lane-change crash risks where speed differences between upstream and downstream vehicles are high. The idea of homogeneous speed zones was also introduced in this study to determine the distance over which variable speed limits should be implemented from a station of interest. This is unique since it is the first time a dynamic distance has been considered for variable speed limit implementation. Several VSL strategies were found to successfully reduce the rear-end and lane-change crash risks at low-volume traffic conditions (60% and 80% loading conditions). In every case, the most successful treatments involved the lowering of upstream speed limits by 5 mph and the raising of downstream speed limits by 5 mph. In the free-flow condition (60% loading), the best treatments involved the more liberal threshold for defining homogeneous speed zones (5 mph) and the more liberal implementation distance (entire speed zone), as well as a minimum time period of 10 minutes. This treatment was actually shown to significantly reduce the network travel time by 0.8%. It was also shown that this particular implementation strategy (lowering upstream, raising downstream) is wholly resistant to the effects of crash migration in the 60% loading scenario. In the condition approaching congestion (80% loading), the best treatment again involved the more liberal threshold for homogeneous speed zones (5 mph), yet the more conservative implementation distance (half the speed zone), along with a minimum time period of 5 minutes. This particular treatment arose as the best due to its unique capability to resist the increasing effects of crash migration in the 80% loading scenario. It was shown that the treatments implementing over half the speed zone were more robust against crash migration than other treatments. The best treatment exemplified the greatest benefit in reduced sections and the greatest resistance to crash migration in other sections. In the 80% loading scenario, the best treatment increased the network travel time by less than 0.4%, which is deemed acceptable. No treatment was found to successfully reduce the rear-end and lane-change crash risks in the congested traffic condition (90% loading). This is attributed to the fact that, in the congested state, the speed of vehicles is subject to the surrounding traffic conditions and not to the posted speed limit. Therefore, changing the posted speed limit does not affect the speed of vehicles in a desirable manner. These conclusions agree with Dilmore (2005).
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Date Issued: 2007
Identifier: CFE0001723, ucf:47309
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001723

Title: ASSESSMENT OF THE SAFETY BENEFITS OF VMS AND VSL USING THE UCF DRIVING SIMULATOR.
Creator: Dos Santos, Cristina, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Researchers at the University of Central Florida (UCF) have been working during the past few years on different strategies to improve freeway safety in real-time. An ongoing research at UCF has investigated crash patterns that occurred on a stretch of Interstate-4 located in Orlando, FL and created statistical models to predict in real-time the likelihood of a crash in terms of time and space. The models were then tested using PARAMICS micro-simulation and different strategies that would...
Show moreResearchers at the University of Central Florida (UCF) have been working during the past few years on different strategies to improve freeway safety in real-time. An ongoing research at UCF has investigated crash patterns that occurred on a stretch of Interstate-4 located in Orlando, FL and created statistical models to predict in real-time the likelihood of a crash in terms of time and space. The models were then tested using PARAMICS micro-simulation and different strategies that would reduce the risk of crashes were suggested. One of the main recommended strategies was the use of Variable Speed Limits (VSL) which intervenes by reducing the speed upstream the segment of high risk and increasing the speed downstream. The purpose of this study is to examine the recommendations reached by the micro-simulation using the UCF driving simulator. Drivers' speed behavior in response to changes in speed limits and different information messages are observed. Different scenarios that represent the recommendations from the earlier micro-simulation study and three different messages displayed using Variable Message Signs (VMS) as an added measure to advice drivers about changes in the speed limit were created. In addition, abrupt and gradual changes in speed were tested against the scenarios that maintained the speed limit constant or did include a VSL or VMS in the scenarios' design (base case). Dynamic congestion was also added to the scenarios' design to observe drivers' reactions and speed reductions once drivers approached congestion. A total of 85 subjects were recruited. Gender and age were the controlling variables for the subjects' recruitment. Each of the subjects drove 3 out of a total of 24 scenarios. In addition, a survey was conducted and involved hypothetical questions, including knowledge about VMS and VSL, and questions about their driving behavior. The survey data were useful in identifying the subjects' compliance with the speed limit and VSL/VMS acceptance. Two statistical analytical techniques were performed on the data that were collected from the simulator: ANOVA and PROC MIXED. The ANOVA test was used to investigate if the differences in speed and reaction distances between subjects were statistically significant for each sign compared to the base case. The PROC MIXED analysis was used to investigate the differences of all scenarios (24x24) based on the spot speed data collected for each driver. It was found from the analyses that drivers follow better the message displayed on VMS that informs them that the speed is changing, whether it is or not, strictly enforced as opposed to providing the reason for change or no information. Moreover, an abrupt change in speed produced immediate results; however both abrupt and gradual changes in speed produced the same reduction in speed at the target zone. It was also noticed that most drivers usually drive 5 mph above the speed limit, even though in the survey analysis the majority of them stated that they drive in compliance with the speed limit or with the flow of traffic. This means that if a modest speed reduction of 5 mph is requested they will ignore it, but if a 10 mph reduction is recommended they will reduce the speed by at least 5 mph. Consequently, it was noticed that drivers arrived at the congestion zone with a slower speed than the base speed limit due to the combination of VMS and VSL signage. By having drivers approaching congestion with a slower speed, potential rear-end crashes could be avoided. Comparing the two genders indicated that females are more likely to follow the VMS's recommendations to reduce the speed. Also females in general drive above the speed limit between 2 mph and 3 mph, while males drive above the speed limit between 5 mph and 8 mph. From the analysis of the age factor, it was concluded that drivers from the 16-19 age group drive faster and drivers from the 45 and above age group drive slower, than the drivers from the other groups. In general, all drivers reduced and/or increased their speed accordingly when a VMS and/or VSL was present in the scenario advising for this change in the speed limit. The investigations conducted for this thesis proved that the recommendations suggested previously based on the crash risk model and micro-simulation (Abdel-Aty et al., 2006) aid drivers in reducing their speed before they approach a segment of high risk and by doing so reduce the likelihood of a crash. Finally, the real-time safety benefits of VMS and VSL should be continuously evaluated in future studies.
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Date Issued: 2007
Identifier: CFE0001628, ucf:47167
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001628

Title: EXPLORING THE POTENTIAL OF COMBINING RAMP METERING AND VARIABLE SPEED LIMIT STRATEGIES FOR ALLEVIATING REAL-TIME CRASH RISK ON URBAN FREEWAYS.
Creator: Haleem, Kirolos, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Research recently conducted at the University of Central Florida involving crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical and neural networks models that are capable of determining the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models are able to calculate rear-end and lane-change crash risks along the freeway in real-time through the use of static information at various locations along the freeway as well...
Show moreResearch recently conducted at the University of Central Florida involving crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical and neural networks models that are capable of determining the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models are able to calculate rear-end and lane-change crash risks along the freeway in real-time through the use of static information at various locations along the freeway as well as real-time traffic data obtained by loop detectors. Since these models use real-time traffic data, they are capable of calculating rear-end and lane-change crash risk values as the traffic flow conditions are changing on the freeway. The objective of this study is to examine the potential benefits of combining two ITS strategies (Ramp Metering and Variable Speed Limits strategies) for reducing the crash risk (both rear-end and lane-change crash risks) along the I-4 freeway. Following this aspect, a 36.25-mile section of I-4 running though Orlando, FL was simulated using the PARAMICS micro-simulation program. Gayah (2006) used the same network to examine the potential benefits of two ITS strategies separately (Route Diversion and Ramp Metering) for reducing the crash risk along the freeway by changing traffic flow parameters. Cunningham (2007) also used the same network to examine the potential benefits of implementing Variable Speed Limits strategy for reducing the crash risk along the freeway. Since the same network is used, the calibration and validation procedures used in this study are the same as these previous two studies. This study simulates three volume loading scenarios on the I-4 freeway. These are 60, 80 and 90 percent loading scenarios. From the final experimental design for the 60 % loading, it was concluded that implementing VSL strategy only was more beneficial to the network than either implementing Ramp Metering everywhere (through the whole network) in conjunction with VSL everywhere or implementing Ramp Metering downtown (in downtown areas only) in conjunction with VSL everywhere. This was concluded from the comparison of the results of this study with the results from Cunningham (2007). However, either implementing Ramp Metering everywhere or downtown in conjunction with VSL everywhere showed safety benefits across the simulated network as well as a reduction in the total travel time. The best case for implementing Ramp Metering everywhere in conjunction with VSL everywhere was using a homogeneous speed zone threshold of 2.5 mph, a speed change distance of half speed zone and a speed change time of 5 minutes in conjunction with a 60 seconds cycle length for the Zone algorithm, a critical occupancy of 0.17 and a 30 seconds cycle length for the ALINEA algorithm. And the best case for implementing Ramp Metering downtown in conjunction with VSL everywhere was using a homogeneous speed zone threshold of 2.5 mph, a speed change distance of half speed zone and a speed change time of 10 minutes in conjunction with a 60 seconds cycle length for the Zone algorithm, a critical occupancy of 0.17 and a 30 seconds cycle length for the ALINEA algorithm. For the 80 % loading, it was concluded that either implementing Ramp Metering everywhere in conjunction with VSL everywhere or implementing Ramp Metering downtown in conjunction with VSL everywhere was more beneficial to the network than implementing VSL strategy only. This was also concluded from the comparison of the results of this study with the results from Cunningham (2007). Moreover, it was concluded that implementing Ramp Metering everywhere in conjunction with VSL everywhere showed higher safety benefits across the simulated network than implementing Ramp Metering downtown in conjunction with VSL everywhere. Also, both of them increased the total travel time a bit, but this was deemed acceptable. Additionally, both of them had successive fluctuations and variations in the average lane-change crash risk vs. time step. The best case for implementing Ramp Metering everywhere in conjunction with VSL everywhere was using a homogeneous speed zone threshold of 5 mph, a speed change distance of half speed zone and a speed change time of 30 minutes in conjunction with a 60 seconds cycle length for the Zone algorithm, a critical occupancy of 0.17 and a 30 seconds cycle length for the ALINEA algorithm. And the best case for implementing Ramp Metering downtown in conjunction with VSL everywhere was using a homogeneous speed zone threshold of 5 mph, a speed change distance of half speed zone and a speed change time of 30 minutes in conjunction with a 60 seconds cycle length for the Zone algorithm, a critical occupancy of 0.17 and a 30 seconds cycle length for the ALINEA algorithm. Searching for the best way to implement both Ramp Metering and VSL strategies in conjunction with each other, an indepth investigation was conducted in order to remove the fluctuations and variations in the crash risk with time step (through the entire simulation period). The entire simulation period is 3 hours, and each time step is 5 minutes, so there are 36 time steps representing the entire simulation period. This indepth investigation led to the idea of not implementing VSL at consecutive zones (using either a gap of one zone or more). Then this idea was applied for the best case of implementing Ramp Metering and VSL everywhere at the 80 % loading, and the successive fluctuations and variations in the crash risk with time step were removed. Moreover, much better safety benefits were found. So, this confirms that this idea was very beneficial to the network. For the 90 % loading, it was concluded that implementing Ramp Metering strategy only (Zone algorithm in downtown areas, and ALINEA algorithm in non downtown areas) was more beneficial to the network than implementing Ramp Metering everywhere in conjunction with VSL everywhere. This was concluded from the comparison of the results of this study with the results from Gayah (2006). However, implementing Ramp Metering everywhere in conjunction with VSL everywhere showed safety benefits across the simulated network as well as a reduction in the total travel time. The best case was using a homogeneous speed zone threshold of 2.5 mph, a speed change distance of the entire speed zone and a speed change time of 20 minutes in conjunction with a 60 seconds cycle length for the Zone algorithm, a critical occupancy of 0.17 and a 30 seconds cycle length for the ALINEA algorithm. In summary, Ramp Metering was more beneficial at congested situations, while Variable Speed Limits were more beneficial at free-flow conditions. At conditions approaching congestion, the combination of Ramp Metering and Variable Speed Limits produced the best benefits. These results illustrate the significant potential of ITS strategies to improve the safety and efficiency of urban freeways.
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Date Issued: 2007
Identifier: CFE0001840, ucf:47363
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001840

Title: NEURAL NETWORK TREES AND SIMULATION DATABASES: NEW APPROACHES FOR SIGNALIZED INTERSECTION CRASH CLASSIFICATION AND PREDICTION.
Creator: Nawathe, Piyush, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Intersection related crashes form a significant proportion of the crashes occurring on roadways. Many organizations such as the Federal Highway Administration (FHWA) and American Association of State Highway and Transportation Officials (AASHTO) are considering intersection safety improvement as one of their top priority areas. This study contributes to the area of safety of signalized intersections by identifying the traffic and geometric characteristics that affect the different types of...
Show moreIntersection related crashes form a significant proportion of the crashes occurring on roadways. Many organizations such as the Federal Highway Administration (FHWA) and American Association of State Highway and Transportation Officials (AASHTO) are considering intersection safety improvement as one of their top priority areas. This study contributes to the area of safety of signalized intersections by identifying the traffic and geometric characteristics that affect the different types of crashes. The first phase of this thesis was to classify the crashes occurring at signalized intersections into rear-end, angle, turn and sideswipe crash types based on the traffic and geometric properties of the intersections and the conditions at the time of the crashes. This was achieved by using an innovative approach developed in this thesis "Neural Network Trees". The first neural network model built in the Neural Network tree classified the crashes either into rear end and sideswipe or into angle and turn crashes. The next models further classified the crashes into their individual types. Two different neural network methods (MLP and PNN) were used in classification, and the neural network with a better performance was selected for each model. For these models, the significant variables were identified using the forward sequential selection method. Then a large simulation database was built that contained all possible combinations of intersections subjected to various crash conditions. The collision type of crashes was predicted for this simulation database and the output obtained was plotted along with the input variables to obtain a relationship between the input and output variables. For example, the analysis showed that the number of rear end and sideswipe crashes increase relative to the angle and turn crashes when there is an increase in the major and minor roadways' AADT and speed limits, surface conditions, total left turning lanes, channelized right turning lanes for the major roadway and the protected left turning lanes for the minor roadway, but decrease when the light conditions are dark. The next phase in this study was to predict the frequency of different types of crashes at signalized intersections by using the geometric and traffic characteristics of the intersections. A high accuracy in predicting the crash frequencies was obtained by using another innovative method where the intersections were first classified into two different types named the "safe" and "unsafe" intersections based on the total number of lanes at the intersections and then the frequency of crashes was predicted for each type of intersections separately. This method consisted of identifying the best neural network for each step of the analysis, selecting significant variables, using a different simulation database that contained all possible combinations of intersections and then plotting each input variable with the average output to obtain the pattern in which the frequency of crashes will vary based on the changes in the geometric and traffic characteristics of the intersections. The patterns indicated that an increase in the number of lanes of the major roadway, lanes of the minor roadway and the AADT on the major roadway leads to an increased crashes of all types, whereas an increase in protected left turning lanes on the major road increases the rear end and sideswipe crashes but decreases the angle, turning and overall crash frequencies. The analyses performed in this thesis were possible due to a diligent data collection effort. Traffic and geometric characteristics were obtained from multiple sources for 1562 signalized intersections in Brevard, Hillsborough, Miami-Dade, Seminole and Orange counties and the city of Orlando in Florida. The crash database for these intersections contained 27,044 crashes. This research sheds a light on the characteristics of different types of crashes. The method used in classifying crashes into their respective collision types provides a deeper insight on the characteristics of each type of crash and can be helpful in mitigating a particular type of crash at an intersection. The second analysis carried out has a three fold advantage. First, it identifies if an intersection can be considered safe for different crash types. Second, it accurately predicts the frequencies of total, rear end, angle, sideswipe and turn crashes. Lastly, it identifies the traffic and geometric characteristics of signalized intersections that affect each of these crash types. Thus the models developed in this thesis can be used to identify the specific problems at an intersection, and identify the factors that should be changed to improve its safety
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Date Issued: 2005
Identifier: CFE0000664, ucf:46524
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000664

Title: ASSESSING CRASH OCCURRENCE ON URBAN FREEWAYS USING STATIC AND DYNAMIC FACTORS BY APPLYING A SYSTEM OF INTERRELATED EQUATIONS.
Creator: Pemmanaboina, Rajashekar, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models...
Show moreTraffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.
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Date Issued: 2005
Identifier: CFE0000587, ucf:46468
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000587

Title: MODELING CRASH FREQUENCIES AT SIGNALIZED INTERSECTIONS IN CENTRAL FLORIDA.
Creator: Kowdla, Smitha, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: A high percentage of highway crashes in the United States occur at intersections. These crashes result in property damage, lost productivity, injury, and even death. Identifying intersections associated with high crash rate is very important to minimize future crashes. The purpose of this study is to develop efficient means to evaluate intersections, which may require safety improvements. The area covered by the analysis in this thesis includes Orange and Seminole Counties and the City of...
Show moreA high percentage of highway crashes in the United States occur at intersections. These crashes result in property damage, lost productivity, injury, and even death. Identifying intersections associated with high crash rate is very important to minimize future crashes. The purpose of this study is to develop efficient means to evaluate intersections, which may require safety improvements. The area covered by the analysis in this thesis includes Orange and Seminole Counties and the City of Orlando. The aforementioned counties and city thus represent Central Florida. Each County/City provided data that consisted of signalized intersection drawings that were either in the form of electronic or hard copies, the county's extensive crash database and a list of intersections that underwent modifications during the study period. A total of 786 intersections were used in the analysis and the crash database was made up of 4271 crashes. From the signalized intersection drawings obtained from the county's traffic engineering department, a geometry database was created to classify all intersections by the number of through lanes, number of left turning lanes, Average Annual Daily Traffic and Posted Speed limits on the Major road of the intersection. In this research, crashes and their type, e.g., rear-end, left-turn and angle as well as total crashes were investigated. Numerous models were developed first using the Poisson regression and then using the Negative Binomial approach as the data showed overdispersion. The modeling process aimed to relate geometric and traffic factors to the frequency of crashes at intersections. Expected value analysis tables were also developed to determine if an intersection had an abnormally high number of crashes. These tables can be used in assisting Traffic Engineers in identifying serious safety problems at intersections. The general models illustrated that rear-end crashes were associated with high natural logarithm of AADT on the major road and the number of lanes (major intersections, e.g. 6x4/6x6), whereas AADT on the major road did not affect left-turn crashes. Intersections with the configuration 4x2/6x2 (2 through lanes at the minor roadway) or T intersections as another category experienced an increase in left-turn crashes. Angle crashes were most frequent at one-way intersections especially in the case of 4x4 intersections. Individual models that included interaction terms with one variable at a time concluded that AADT on the major road positively influenced rear-end crashes more compared to angle and left-turn crashes. As the speed increases on the minor road, the left turn crashes are affected more when compared to angle and rear-end crashes, therefore it can be concluded that left-turn crashes are most influenced by the speed limit on the minor road compared to angle crashes and then followed by rear-end crashes. As the total number of left turn lanes increased at the intersection, thereby increasing the size of the intersection, the number of rear-end crashes increased. An overall model that contained natural logarithm of AADT on major road, total number of left turn lanes at the intersection, number of through lanes on the minor road and configuration of the intersection, as independent variables, along with interaction terms, further concluded and supported the individual models that the number of crashes (rear-end, left-turn and angle) increased as the AADT on the major road increased and the number of crashes decreased as the total number of left turn lanes at the intersection increased. Also, crashes increased as the number of through lanes on the minor road increased. The variables' interaction effects with dummies representing rear-end and left-turn crashes in the final model showed that as the AADT on the major road increased, the number of rear-end crashes increased compared to left-turn and angle crashes and also that as the total number of left turn lanes at the intersection increased, the number of left-turn crashes decreased when compared to rear-end and angle crashes. Also the number of rear-end crashes increased at major four leg intersections e.g. 6x4, 6x6 etc. This thesis demonstrated the superiority of Negative Binomial regression in modeling the frequency of crashes at signalized intersections.
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Date Issued: 2004
Identifier: CFE0000267, ucf:46224
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000267

Title: EVALUATING RAMP METERING AND VARIABLE SPEED LIMITS TO REDUCE CRASH POTENTIAL ON CONGESTED FREEWAYS USING MICRO-SIMULATION.
Creator: Dhindsa, Albinder, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Recent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes. Ramp Metering and Variable Speed...
Show moreRecent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes. Ramp Metering and Variable Speed Limits are two approaches which have the potential of becoming effective implementation strategies for improving the safety conditions on congested freeways. This research evaluates both these strategies in different configurations and attempts to quantify their effect on risk of crash on a 9-mile section of Interstate-4 in the Orlando metropolitan region. The section consists of 17 Loop Detector stations, 11 On-ramps and 10 off-ramps. PARAMICS micro-simulation is used as the tool for modeling the freeway section. The simulated network is calibrated and validated for 5 minute average flows and speeds using loop detector data. Feedback Ramp Metering algorithm, ALINEA, is used for controlling access from up to 7 on-ramps. Variable Speed Limits are implemented based on real-time speed conditions prevailing in the whole 9-mile section. Both these strategies are tested separately as well as collectively to determine the individual effects of all the parameters involved. The results have been used to formulate and recommend the best possible strategy for minimizing the risk of crashes on the corridor. The study concluded that Ramp Metering improves the conditions on the freeway in terms of safety by decreasing variance in speeds and decreasing average occupancy. A safety benefit index was developed for quantifying the reduction in crash risk and it indicated that an optimal implementation strategy might produce benefits of up to 55%. The condition on the freeway section improved with increase in the number of metered ramps. It was also observed that shorter signal cycles for metered ramps were more suitable for metering multiple ramps. Ramp Metering at multiple locations also decreased the segment wide travel-times by 5% and was even able to offset the delays incurred by drivers at the metered on-ramps. Variable Speed Limits (VSL) were individually not as effective as ramp metering but when implemented along with ramp metering, they were found to further improve the safety on the freeway section under consideration. By means of a detailed experimental design it was observed that the best strategy for introducing speed limit changes was to raise the speed limits downstream of the location of interest by 5 mph and not affecting the speed limits upstream. A coordinated strategy - involving simultaneous application of VSL and Ramp Metering - provided safety benefits of up to 56 % for the study section according to the safety benefit index. It also improved the average speeds on the network besides decreasing the overall network travel time by as much as 21%.
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Date Issued: 2005
Identifier: CFE0000913, ucf:46741
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000913

Title: ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS.
Creator: pande, anurag, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would...
Show moreRelevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.
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Date Issued: 2005
Identifier: CFE0000842, ucf:46659
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000842

Title: APPLYING LOG-LINEAR MODELS AND GIS TO STUDY THE SAFETY OF PEDESTRIANS AND BICYCLISTS : A CASE STUDY OF ORANGE COUNTY SCHOOL CHILDREN.
Creator: Chundi, Sai Srinivas, Mohamed, Abdel Aty, University of Central Florida
Abstract / Description: Abstract Pedestrian /bicycle safety of school children has been a growing menace that has been attracting attention from transportation professionals, school boards, media and the community all over the country. As such there has been a necessity to identify critical variables and assess their importance in pedestrian/bicycle crashes occurring in and around school zones. The current study is an endeavor in this direction. The literature review identified some studies that were conducted on...
Show moreAbstract Pedestrian /bicycle safety of school children has been a growing menace that has been attracting attention from transportation professionals, school boards, media and the community all over the country. As such there has been a necessity to identify critical variables and assess their importance in pedestrian/bicycle crashes occurring in and around school zones. The current study is an endeavor in this direction. The literature review identified some studies that were conducted on school zone safety related to pedestrian/bicyclist crashes. Most of the studies pertained to crashes with all age groups. There have been few studies with emphasis only on school aged children. In this study we focus on pedestrian age group (4 to 18 years), the time of the day when the school children are expected to be commuting (6:30 AM to 10:00 AM and 1:00 PM to 5:00.PM), the day of week (Monday through Friday) and the days when the school is opened (January 6th to May 31st and August 6th to December 21st). Geographical Information Systems was used to locate buffer zones around schools with higher crash incidence rates. The use of log-linear analysis has culminated in explaining the relationship between various variables and crash incidence or crash frequency Crash data for this study was obtained in the form of crash database and GIS maps from the Department of Highway Safety and Motor Vehicles and the Orange County School Board respectively. Crash reports were downloaded from the CAR database of the FDOT mainframe website. The crash data was related to the GIS maps to visually depict the proximity of crashes to the school zones and thus identified risky schools and school districts. It was concluded from the spatial analysis that the incidence of crashes was higher at middle schools. In the log-linear analysis different models were i tested to explain the effects of driver characteristics, geometric characteristics and pedestrian characteristics on the crash frequency. It was found that driver age, number of lanes, median type, pedestrian age and speed limit are the critical variables in explaining crash frequency. By examining the levels of the variables that were significantly involved in the crashes we would get an insight on ways to explain and control pedestrian/bicyclists crashes at school zones. It is hoped that this thesis would make an active contribution in improving the safety of bicyclists and pedestrians in and around school zones and make the schools much safer for the children.
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Date Issued: 2005
Identifier: CFE0000885, ucf:46643
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000885

Title: ANALYSIS OF AIRCRAFT ARRIVAL DELAY AND AIRPORT ON-TIME PERFORMANCE.
Creator: Bai, Yuqiong, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: In this research, statistical models of airport delay and single flight arrival delay were developed. The models use the Airline On-Time Performance Data from the Federal Aviation Administration (FAA) and the Surface Airways Weather Data from the National Climatic Data Center (NCDC). Multivariate regression, ANOVA, neural networks and logistic regression were used to detect the pattern of airport delay, aircraft arrival delay and schedule performance. These models are then integrated in the...
Show moreIn this research, statistical models of airport delay and single flight arrival delay were developed. The models use the Airline On-Time Performance Data from the Federal Aviation Administration (FAA) and the Surface Airways Weather Data from the National Climatic Data Center (NCDC). Multivariate regression, ANOVA, neural networks and logistic regression were used to detect the pattern of airport delay, aircraft arrival delay and schedule performance. These models are then integrated in the form of a system for aircraft delay analysis and airport delay assessment. The assessment of an airport¡¯s schedule performance is discussed. The results of the research show that the daily average arrival delay at Orlando International Airport (MCO) is highly related to the departure delay at other airports. The daily average arrival delay can also be used to evaluate the delay performance at MCO. The daily average arrival delay at MCO is found to show seasonal and weekly patterns, which is related to the schedule performance. The precipitation and wind speed are also found contributors to the arrival delay. The capacity of the airport is not found to be significant. This may indicate that the capacity constraint is not an important problem at MCO. This research also investigated the delays at the flight level, including the flights with delay ¡Ý0 minute and the flights with delay ¡Ý15min, which provide the delay pattern of single arrival flights. The characteristics of single flight and their effect on flight delay are considered. The precipitation, flight distance, season, weekday, arrival time and the time spacing between two successive arriving flights are found to contribute to the arrival delay. We measure the time interval of two consecutive flights spacing and analyze its effect on the flight delay and find that for a positively delayed flight, as the time space increases, the probability of the flights being delayed will decrease. While it was possible to calculate the immediate impact of originating delays, it is not possible to calculate their impact on the cumulative delay. If a late departing aircraft has no empty space in its down line schedule, it will continue to be late. If that aircraft enters a connecting airport, it can pass its lateness on to another aircraft. In the research we also consider purifying only the arrival delay at MCO, excluding the flights with originating delay >0. The model makes it possible to identify the pattern of the aircraft arrival delay. The weather conditions are found to be the most significant factors that influence the arrival delay due to the destination airport.
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Date Issued: 2006
Identifier: CFE0001049, ucf:46808
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001049

Title: DRIVING SIMULATOR VALIDATION AND REAR-END CRASH RISK ANALYSIS AT A SIGNALISED INTERSECTION.
Creator: Chilakapati, Praveen, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: In recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world...
Show moreIn recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world signalized intersection (Alafaya trail (SR-434) and Colonial Drive (SR-50)). A total of sixty one subjects of age ranging from sixteen to sixty years were recruited to drive the simulator for the experiment, which consists of eight scenarios. This research validates the driving simulator for speed, safety and visual aspects. Based on the overall comparisons of speed between the simulated results and the real world, it was concluded that the UCF driving simulator is a valid tool for traffic studies related to driving speed behavior. Based on statistical analysis conducted on the experiment results, it is concluded that SR-434 northbound right turn lane and SR-50 eastbound through lanes have a higher rear-end crash risk than that at SR-50 westbound right turn lane and SR-434 northbound through lanes, respectively. This conforms to the risk of rear-end crashes observed at the actual intersection. Therefore, the simulator is validated for using it as an effective tool for traffic safety studies to test high-risk intersection locations. The driving simulator is also validated for physical and visual aspects of the intersection as 87.10% of the subjects recognized the intersection and were of the opinion that the replicated intersection was good enough or realistic. A binary logistic regression model was estimated and was used to quantify the relative rear-end crash risk at through lanes. It was found that in terms of rear-end crash risk SR50 east- bound approach is 23.67% riskier than the SR434 north-bound approach.
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Date Issued: 2006
Identifier: CFE0001391, ucf:46964
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001391

Title: COMPREHENSIVE ANALYTICAL INVESTIGATION OF THE SAFETY OF UNSIGNALIZED INTERSECTIONS.
Creator: Haleem, Kirolos, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: According to documented statistics, intersections are among the most hazardous locations on roadway systems. Many studies have extensively analyzed safety of signalized intersections, but did not put their major focus on the most frequent type of intersections, unsignalized intersections. Unsignalized intersections are those intersections with stop control, yield control and no traffic control. Unsignalized intersections can be differentiated from their signalized counterparts in that their...
Show moreAccording to documented statistics, intersections are among the most hazardous locations on roadway systems. Many studies have extensively analyzed safety of signalized intersections, but did not put their major focus on the most frequent type of intersections, unsignalized intersections. Unsignalized intersections are those intersections with stop control, yield control and no traffic control. Unsignalized intersections can be differentiated from their signalized counterparts in that their operational functions take place without the presence of a traffic signal. In this dissertation, multiple approaches of analyzing safety at unsignalized intersections were conducted. This was investigated in this study by analyzing total crashes, the most frequent crash types at unsignalized intersections (rear-end as well as angle crashes) and crash injury severity. Additionally, an access management analysis was investigated with respect to the different median types identified in this study. Some of the developed methodological techniques in this study are considered recent, and have not been extensively applied. In this dissertation, the most extensive data collection effort for unsignalized intersections was conducted. There were 2500 unsignalized intersections collected from six counties in the state of Florida. These six counties were Orange, Seminole, Hillsborough, Brevard, Leon and Miami-Dade. These selected counties are major counties representing the central, western, eastern, northern and southern parts in Florida, respectively. Hence, a geographic representation of the state of Florida was achieved. Important intersections' geometric and roadway features, minor approach traffic control, major approach traffic flow and crashes were obtained. The traditional negative binomial (NB) regression model was used for modeling total crash frequency for two years at unsignalized intersections. This was considered since the NB technique is well accepted for modeling crash count data suffering from over-dispersion. The NB models showed several important variables affecting safety at unsignalized intersections. These include the traffic volume on the major road and the existence of stop signs, and among the geometric characteristics, the configuration of the intersection, number of right and/or left turn lanes, median type on the major road, and left and right shoulder widths. Afterwards, a new approach of applying the Bayesian updating concept for better crash prediction was introduced. Different non-informative and informative prior structures using the NB and log-gamma distributions were attempted. The log-gamma distribution showed the best prediction capability. Crash injury severity at unsignalized intersections was analyzed using the ordered probit, binary probit and nested logit frameworks. The binary probit method was considered the best approach based on its goodness-of-fit statistics. The common factors found in the fitted probit models were the logarithm of AADT on the major road, and the speed limit on the major road. It was found that higher severity (and fatality) probability is always associated with a reduction in AADT, as well as an increase in speed limit. A recently developed data mining technique, the multivariate adaptive regression splines (MARS) technique, which is capable of yielding high prediction accuracy, was used to analyze rear-end as well as angle crashes. MARS yielded the best prediction performance while dealing with continuous responses. Additionally, screening the covariates using random forest before fitting MARS model was very encouraging. Finally, an access management analysis was performed with respect to six main median types associated with unsignalized intersections/access points. These six median types were open, closed, directional (allowing access from both sides), two-way left turn lane, undivided and mixed medians (e.g., directional median, but allowing access from one side only). Also, crash conflict patterns at each of these six medians were identified and applied to a dataset including median-related crashes. In this case, separating median-related and intersection-related crashes was deemed significant in the analysis. From the preliminary analysis, open medians were considered the most hazardous median type, and closed and undivided medians were the safest. The binomial logit and bivariate probit models showed significant median-related variables affecting median-related crashes, such as median width, speed limit on the major road, logarithm of AADT, logarithm of the upstream and downstream distances to the nearest signalized intersection and crash pattern. The results from the different methodological approaches introduced in this study could be applicable to diagnose safety deficiencies identified. For example, to reduce crash severity, prohibiting left turn maneuvers from minor intersection approaches is recommended. To reduce right-angle crashes, avoiding installing two-way left turn lanes at 4-legged intersections is essential. To reduce conflict points, closing median openings across from intersections is recommended. Since left-turn and angle crash patterns were the most dominant at undivided medians, it is recommended to avoid left turn maneuvers at unsignalized intersections having undivided medians at their approach. This could be enforced by installing a left-turn prohibition sign on both major and minor approaches.
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Date Issued: 2009
Identifier: CFE0002900, ucf:48011
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002900

Title: MACROSCOPIC TRAFFIC SAFETY ANALYSIS BASED ON TRIP GENERATION CHARACTERISTICS.
Creator: Siddiqui, Chowdhury, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Recent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with...
Show moreRecent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with crashes. This study contributes to this ongoing macro level road safety research by investigating various trip productions and attractions along with roadway characteristics within traffic analysis zones (TAZs) of four counties in the state of Florida. Crashes occurring in one thousand three hundred and forty-nine TAZs in Hillsborough, Citrus, Pasco, and Hernando counties during the years 2005 and 2006 were examined in this study. Selected counties were representative from both urban and rural environments. To understand the prevalence of various trip attraction and production rates per TAZ the Euclidian distances between the centroid of a TAZ containing a particular crash and the centroid of the ZIP area containing the at fault driver's home address for that particular crash was calculated. It was found that almost all crashes in Hernando and Citrus County for the years 2005-2006 took place in about 27 miles radius centering at the at-fault drivers' home. Also about sixty-two percent of crashes occurred approximately at a distance of between 2 and 10 miles from the homes of drivers who were at fault in those crashes. These results gave an indication that home based trips may be more associated with crashes and later trip related model estimates which were found significant at 95% confidence level complied with this hypothesized idea. Previous aggregate level road safety studies widely addressed negative binomial distribution of crashes. Properties like non-negative integer counts, non-normal distribution, over-dispersion in the data have increased suitability of applying the negative binomial technique and has been selected to build crash prediction models in this research. Four response variables which were aggregated at TAZ-level were total number of crashes, severe (fatal and severe injury) crashes, total crashes during peak hours, and pedestrian and bicycle related crashes. For each response separate models were estimated using four different sets of predictors which are i) various trip variables, ii) total trip production and total trip attraction, iii) road characteristics, and iv) finally considering all predictors into the model. It was found that the total crash model and peak hour crash model were best estimated by the total trip productions and total trip attractions. On the basis of log-likelihoods, deviance value/degree of freedom, and Pearson Chi-square value/degree of freedom, the severe crash model was best fit by the trip related variables only and pedestrian and bicycle related crash model was best fit by the road related variables only. The significant trip related variables in the severe crash models were home-based work attractions, home-based shop attractions, light truck productions, heavy truck productions, and external-internal attractions. Only two variables- sum of roadway segment lengths with 35 mph speed limit and number of intersections per TAZ were found significant for pedestrian and bicycle related crash model developed using road characteristics only. The 1349 TAZs were grouped into three different clusters based on the quartile distribution of the trip generations and were termed as less-tripped, moderately-tripped, and highly-tripped TAZs. It was hypothesized that separate models developed for these clusters would provide a better fit as the clustering process increases the homogeneity within a cluster. The cluster models were re-run using the significant predictors attained from the joint models and were compared with the previous sets of models. However, the differences in the model fits (in terms of Alkaike's Information Criterion values) were not significant. This study points to different approaches when predicting crashes at the zonal level. This research is thought to add to the literature on macro level crash modeling research by considering various trip related data into account as previous studies in zone level safety have not explicitly considered trip data as explanatory covariates.
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Date Issued: 2009
Identifier: CFE0002871, ucf:48029
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002871

Title: ANALYSIS OF TYPE AND SEVERITY OF TRAFFIC CRASHES AT SIGNALIZED INTERSECTIONS USING TREE-BASED REGRESSION AND ORDERED PROBIT MODELS.
Creator: Keller, Joanne Marie, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes...
Show moreMany studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression.Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes.More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision.Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final order
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Date Issued: 2004
Identifier: CFE0000074, ucf:52857
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000074

Title: SAFETY IMPROVEMENTS ON MULTILANE ARTERIALS A BEFORE AND AFTER EVALUATION USING THE EMPIRICAL BAYES METHOD.
Creator: Devarasetty, Prem Chand, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: This study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories 1) corridor level improvements, and 2) intersection improvements. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the improvement projects. Safety effects are estimated not only in terms of all crashes but also rear-end (most common type) as well as...
Show moreThis study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories 1) corridor level improvements, and 2) intersection improvements. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the improvement projects. Safety effects are estimated not only in terms of all crashes but also rear-end (most common type) as well as severe crashes (crashes involving incapacitating and/or fatal injuries) and also angle crashes for intersection improvements. The Safety Performance Functions (SPFs) used in this study are negative binomial crash frequency estimation models that use the information on ADT, length of the segments, speed limit, and number of lanes for corridors. And for intersections the explanatory variables used are ADT, number of lanes, speed limit on major road, and number of lanes on the minor road. GENMOD procedure in SAS was used to develop the SPFs. Corridor SPFs are segregated by crash groups (all, rear-end, and severe), length of the segments being evaluated, and land use (urban, suburban and rural). The results of the analysis show that the resulting changes in safety following corridor level improvements vary widely. Although the safety effect of projects involving the same type of improvement varied, the overall effectiveness of each of the corridor level improvements were found to be positive in terms of reduction in crashes of each crash type considered (total, severe, and rear-end) except for resurfacing projects where the total number of crashes slightly increased after the roadway section is resurfaced. Evaluating additional improvements carried out with resurfacing activities showed that all (other than sidewalk improvements for total crashes) of them consistently led to improvements in safety of multilane arterial sections. It leads to the inference that it may be a good idea to take up additional improvements if it is cost effective to do them along with resurfacing. It was also found that the addition of turning lanes (left and/or right) and paving shoulders were two improvements associated with a project's relative performance in terms of reduction in rear-end crashes. No improvements were found to be associated with a resurfacing project's relative performance in terms of changes in (i.e., reducing) severe crashes. For intersection improvements also the individual results of each project varied widely. Except for adding turn lane(s) all other improvements showed a positive impact on safety in terms of reducing the number of crashes for all the crash types (total, severe, angle, and rear-end) considered. Indicating that the design guidelines for this work type have to be revisited and safety aspect has to be considered while implementing them. In all it can be concluded that FDOT is doing a good job in selecting the sites for treatment and it is very successful in improving the safety of the sections being treated although the main objective(s) of the treatments are not necessarily safety related.
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Date Issued: 2009
Identifier: CFE0002723, ucf:48148
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002723

Title: A GIS SAFETY STUDY AND A COUNTY-LEVEL SPATIAL ANALYSIS OF CRASHES IN THE STATE OF FLORIDA.
Creator: Darwiche, Ali, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: The research conducted in this thesis consists of a Geographic Information Systems (GIS) based safety study and a spatial analysis of vehicle crashes in the State of Florida. The GIS safety study is comprised of a County and Roadway Level GIS analysis of multilane corridors. The spatial analysis investigated the use of county-level vehicle crash models, taking spatial effects into account. The GIS safety study examines the locations of high trends of severe crashes (includes incapacitating...
Show moreThe research conducted in this thesis consists of a Geographic Information Systems (GIS) based safety study and a spatial analysis of vehicle crashes in the State of Florida. The GIS safety study is comprised of a County and Roadway Level GIS analysis of multilane corridors. The spatial analysis investigated the use of county-level vehicle crash models, taking spatial effects into account. The GIS safety study examines the locations of high trends of severe crashes (includes incapacitating and fatal crashes) on multilane corridors in the State of Florida at two levels, county level and roadway level. The GIS tool, which is used frequently in traffic safety research, was utilized to visually display those locations. At the county level, several maps of crash trends were generated. It was found that counties with high population and large metropolitan areas tend to have more crash occurrences. It was also found that the most severe crashes occurred in counties with more urban than rural roads. The neighboring counties of Pasco, Pinellas and Hillsborough had high severe crash rate per mile. At the roadway level, seven counties were chosen for the analysis based on their high severe crash trends, metropolitan size and geographical location. Several GIS maps displaying the safety level of multilane corridors in the seven counties were generated. The GIS maps were based on a ranking methodology that was developed in research that evaluated the safety condition of road segments and signalized intersections separately. The GIS maps were supported by Excel tables which provided details on the most hazardous locations on the roadways. The results of the roadway level analysis found that the worst corridors were located in Pasco, Pinellas and Hillsborough Counties. Also, a sliding window approach was developed and performed on the ten most hazardous corridors of the seven counties. The results were graphs locating the most dangerous 0.5 miles on a corridor. For the spatial analysis of crashes, the exploratory Moran's I statistic test revealed that crash related spatial clustering existed at the county level. For crash modeling, a full Bayesian (FB) hierarchical model is proposed to account for the possible spatial correlation among crash occurrence of adjacent counties. The spatial correlation is realized by specifying a Conditional Auto-regressive prior to the residual term of the link function in standard Poisson regression. Two FB models were developed, one for total crashes and one for severe crashes. The variables used include traffic related factors and socio-economic factors. Counties with higher road congestion levels, higher densities of arterials and intersections, higher percentage of population in the 15-24 age group and higher income levels have increased crash risk. Road congestion and higher education levels, however, were negatively correlated with the risk of severe crashes. The analysis revealed that crash related spatial correlation existed among the counties. The FB models were found to fit the data better than traditional methods such as Negative Binomial and that is primarily due to the existence of spatial correlation. Overall, this study provides the Transportation Agencies with specific information on where improvements must be implemented to have better safety conditions on the roads of Florida. The study also proves that neighboring counties are more likely to have similar crash trends than the more distant ones.
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Date Issued: 2009
Identifier: CFE0002623, ucf:48204
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002623

Traffic Safety (5)	+ -
VISSIM (5)	+ -
safety (5)	+ -
traffic safety (5)	+ -
Highway Safety Manual (4)	+ -

Ramp Metering (4)	+ -
Variable Speed Limits (4)	+ -
driving simulator (4)	+ -
Crash Prediction (3)	+ -
Empirical Bayes (3)	+ -
Freight (3)	+ -
GIS (3)	+ -
Safety Performance Functions (3)	+ -
VSL (3)	+ -
fog (3)	+ -
Crash Modification Factors (2)	+ -
Destination Choice (2)	+ -
Driving Simulator (2)	+ -
Driving behavior (2)	+ -
ITS (2)	+ -
Intelligent Transportation Systems (2)	+ -
Intersection (2)	+ -
Microsimulation (2)	+ -
Mode Choice (2)	+ -
Negative Binomial (2)	+ -
Negative Binomial Models (2)	+ -
Negative Binomial model (2)	+ -
Pedestrian Safety (2)	+ -
SSAM (2)	+ -
Safety (2)	+ -

University of Central Florida (86)	+ -
Abdel-Aty, Mohamed (82)	+ -
Eluru, Naveen (30)	+ -
Lee, JaeYoung (18)	+ -
Radwan, Essam (17)	+ -

Hasan, Samiul (11)	+ -
Abou-Senna, Hatem (10)	+ -
Uddin, Nizam (9)	+ -
Radwan, Ahmed (8)	+ -
Cai, Qing (5)	+ -
Ahmed, Mohamed (4)	+ -
Al-Deek, Haitham (4)	+ -
Wu, Yina (4)	+ -
Yan, Xin (4)	+ -
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