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Title: Urban Expressway Safety and Efficiency Evaluation and Improvement using Big Data.
Creator: Shi, Qi, Abdel-Aty, Mohamed, Eluru, Naveen, Nam, Boo Hyun, Lee, Chris, University of Central Florida
Abstract / Description: In an age of data explosion, almost every aspect of social activities is impacted by the abundance of information. The information, characterized by alarming volume, velocity and variety, is often referred to as (")Big Data("). As one fundamental elements of human life, transportation also confronts the promises and challenges brought about by the Big Data era. Big Data in the transportation arena, enabled by the rapid popularization of Intelligent Transportation Systems (ITS) in the past few...
Show moreIn an age of data explosion, almost every aspect of social activities is impacted by the abundance of information. The information, characterized by alarming volume, velocity and variety, is often referred to as (")Big Data("). As one fundamental elements of human life, transportation also confronts the promises and challenges brought about by the Big Data era. Big Data in the transportation arena, enabled by the rapid popularization of Intelligent Transportation Systems (ITS) in the past few decades, are often collected continuously from different sources over vast geographical scale. Huge in size and rich in information, the seemingly disorganized data could considerably enhance experts' understanding of their system. In addition, proactive traffic management for better system performance is made possible due to the real-time nature of the Big Data in transportation.Operation efficiency and traffic safety have long been deemed as priorities among highway system performance measurement. While efficiency could be evaluated in terms of traffic congestion, safety is studied through crash analysis. Extensive works have been conducted to identify the contributing factors and remedies of traffic congestion and crashes. These studies lead to gathering consensus that operation and safety have played as two sides of a coin, ameliorating either would have a positive effect on the other. With the advancement of Big Data, monitoring and improvement of both operation and safety proactively in real-time have become an urgent call.In this study, the urban expressway network operated by Central Florida Expressway Authority's (CFX) traffic safety and efficiency was investigated. The expressway system is equipped with multiple Intelligent Transportation Systems (ITS). CFX utilizes Automatic Vehicle Identification (AVI) system for Electronic Toll Collection (ETC) as well as for the provision of real-time information. Recently, the authority introduced Microwave Vehicle Detection System (MVDS) on their expressways for more precise traffic monitoring. These traffic detection systems collect different types of traffic data continuously on the 109-mile expressway network, making them one of the sources of Big Data. In addition, multiple Dynamic Message Signs are currently in use to communicate between CFX and motorists. Due to their dynamic nature, they serve as an ideal tool for efficiency and safety improvement. Careful examination of the Big Data from the ITS traffic detection systems was carried out. Based on the characteristics of the data, three types of congestion measures based on the AVI and MVDS system were proposed for efficiency evaluation. MVDS-based congestion measures were found to be better at capturing the subtle changes in congestion in real-time compared with the AVI-based congestion measure. Moreover, considering the high deployment density of the MVDS system, the whole expressway network is well covered. Thus congestion could be evaluated at the microscopic level in both spatial and temporal dimensions. According to the proposed congestion measurement, both mainline congested segments and ramps experiencing congestion were identified. For congestion alleviation, the existing DMS that could be utilized for queue warning were located. In case of no existing DMS available upstream to the congestion area, the potential area where future DMS could be considered was suggested. Substantial efforts have also been dedicated to Big Data applications in safety evaluation and improvement. Both aggregate crash frequency modeling and disaggregate real-time crash prediction were constructed to explore the use of ITS detection data for urban expressway safety analyses. The safety analyses placed an emphasis on the congestion's effects on the Expressway traffic safety. In the aggregate analysis the three congestion measures developed in this research were tested in the context of safety modeling and their performances compared. Multi-level Bayesian ridge regression was utilized to deal with the multicollinearity issue in the modeling process. While all of the congestion measures indicated congestion was a contributing factor to crash occurrence in the peak hours, they suggested that off-peak hour crashes might be caused by factors other than congestion. Geometric elements such as the horizontal curves and existence of auxiliary lanes were also identified to significantly affect the crash frequencies on the studied expressways.In the disaggregate analysis, rear-end crashes were specifically studied since their occurrence was believed to be significantly related to the traffic flow conditions. The analysis was conducted in Bayesian logistic regression framework. The framework achieved relatively good classifier performance. Conclusions confirmed the significant effects of peak hour congestion on crash likelihood. Moreover, a further step was taken to incorporate reliability analysis into the safety evaluation. With the developed logistic model as a system function indicating the safety states under specific traffic conditions, this method has the advantage that could quantitatively determine the traffic states appropriate to trigger safety warning to motorists. Results from reliability analysis also demonstrate the peak hours as high risk time for rear-end crashes. Again, DMS would be an essential tool to carry the messages to drivers for potential safety benefits. In existing safety studies, the ITS traffic data were normally used in aggregated format or only the pre-crash traffic data were used for real-time prediction. However, to fully realize their applications, this research also explored their use from a post-crash perspective. The real-time traffic states immediately before and after crash occurrence were extracted to identify whether the crash caused traffic deterioration. Elements regarding spatial, temporal, weather and crash characteristics from individual crash reports were adopted to analyze under what conditions a crash could significantly worsen traffic conditions on urban expressways. Multinomial logit model and two separate binomial models were adopted to identify each element's effects. Expected contribution of this work is to shorten the reaction and clearance time to those crashes that might cause delay on expressways, thus reducing congestion and probability of secondary crashes simultaneously.Finally, potential relevant applications beyond the scope of this research but worth investigation in the future were proposed.
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Date Issued: 2014
Identifier: CFE0005886, ucf:50888
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005886

Title: Macroscopic Crash Analysis and Its Implications for Transportation Safety Planning.
Creator: Siddiqui, Chowdhury, Abdel-Aty, Mohamed, Abdel-Aty, Mohamed, Uddin, Nizam, Huang, Helai, University of Central Florida
Abstract / Description: Incorporating safety into the transportation planning stage, which is often termed as transportation safety planning (TSP), relies on the vital interplay between zone characteristics and zonal traffic crashes. Although a few safety studies had made some effort towards integrating safety and planning, several unresolved problems and a complete framework of TSP are still absent in the literature. This research aims at examining the suitability of the current traffic-related zoning planning...
Show moreIncorporating safety into the transportation planning stage, which is often termed as transportation safety planning (TSP), relies on the vital interplay between zone characteristics and zonal traffic crashes. Although a few safety studies had made some effort towards integrating safety and planning, several unresolved problems and a complete framework of TSP are still absent in the literature. This research aims at examining the suitability of the current traffic-related zoning planning process in a new suggested planning method which incorporates safety measures. In order to accomplish this broader research goal, the study defined its research objectives in the following directions towards establishing a framework of TSP- i) exploring the existing key determinants in traditional transportation planning (e.g., trip generation/distribution data, land use types, demographics, etc.) in order to develop an effective and efficient TSP framework, ii) investigation of the Modifiable Aerial Unit Problem (MAUP) in the context of macro-level crash modeling to investigate the effect of the zone's size and boundary, iii) understanding neighborhood influence of the crashes at or near zonal boundaries, and iv) development of crash-specific safety measure in the four-step transportation planning process.This research was conducted using spatial data from the counties of West Central Florida. Analysis of different crash data per spatial unit was performed using nonparametric approaches (e.g., data mining and random forest), classical statistical methods (e.g., negative binomial models), and Bayesian statistical techniques. In addition, a comprehensive Geographic Information System (GIS) based application tools were utilized for spatial data analysis and representation.Exploring the significant variables related to specific types of crashes is vital in the planning stages of a transportation network. This study identified and examined important variables associated with total crashes and severe crashes per traffic analysis zone (TAZ) by applying nonparametric statistical techniques using different trip related variables and road-traffic related factors. Since a macro-level analysis, by definition, will necessarily involve aggregating crashes per spatial unit, a spatial dependence or autocorrelation may arise if a particular variable of a geographic region is affected by the same variable of the neighboring regions. So far, few safety studies were performed to examine crashes at TAZs and none of them explicitly considered spatial effect of crashes occurring in them. In order to understand the clear picture of spatial autocorrelation of crashes, this study investigated the effect of spatial autocorrelation in modeling pedestrian and bicycle crashes in TAZs. Additionally, this study examined pedestrian crashes at Environmental Justice (EJ) TAZs which were identified in compliance with the various ongoing practices undertaken by Metropolitan Planning Organizations (MPOs) and previous research. Minority population and the low-income group are two important criteria based on which EJ areas are being identified. These unique areal characteristics have been of particular interest to the traffic safety analysts in order to investigate the contributing factors of pedestrian crashes in these deprived areas. Pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences are present between the predictor sets for pedestrian and bicycle crashes. In all cases the models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implied that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level. Also, the significance of spatial autocorrelation was later found in the total and severe crash analyses and accounted for in their respective modeling techniques.Since the study found affirmative evidence about the inclusion of spatial autocorrelation in the safety performance functions, this research considered identifying appropriate spatial entity based on which TSP framework would be developed. A wide array of spatial units has been explored in macro-level crash modeling in previous safety research. With the advancement of GIS, safety analysts are able to analyze crashes for various geographical units. However, a clear guideline on which geographic entity should a modeler choose is not present so far. This preference of spatial unit can vary with the dependent variable of the model. Or, for a specific dependent variable, models may be invariant to multiple spatial units by producing a similar goodness-of-fits. This problem is closely related to the Modifiable Areal Unit Problem which is a common issue in spatial data analysis. The study investigated three different crash (total, severe, and pedestrian) models developed for TAZs, block groups (BGs) and census tracts (CTs) using various roadway characteristics and census variables (e.g., land use, socio-economic, etc.); and compared them based on multiple goodness-of-fit measures.Based on MAD and MSPE it was evident that the total, severe and pedestrian crash models for TAZs and BGs had similar fits, and better than the ones developed for CTs. This indicated that the total, severe and pedestrian crash models are being affected by the size of the spatial units rather than their zoning configurations. So far, TAZs have been the base spatial units of analyses for developing travel demand models. Metropolitan planning organizations widely use TAZs in developing their long range transportation plans (LRTPs). Therefore, considering the practical application it was concluded that as a geographical unit, TAZs had a relative ascendancy over block group and census tract.Once TAZs were selected as the base spatial unit of the TSP framework, careful inspections on the TAZ delineations were performed. Traffic analysis zones are often delineated by the existing street network. This may result in considerable number of crashes on or near zonal boundaries. While the traditional macro-level crash modeling approach assigns zonal attributes to all crashes that occur within the zonal boundary, this research acknowledged the inaccuracy resulting from relating crashes on or near the boundary of the zone to merely the attributes of that zone. A novel approach was proposed to account for the spatial influence of the neighboring zones on crashes which specifically occur on or near the zonal boundaries. Predictive model for pedestrian crashes per zone were developed using a hierarchical Bayesian framework and utilized separate predictor sets for boundary and interior (non-boundary) crashes. It was found that these models (that account for boundary and interior crashes separately) had better goodness-of-fit measures compared to the models which had no specific consideration for crashes located at/near the zone boundaries. Additionally, the models were able to capture some unique predictors associated explicitly with interior and boundary-related crashes. For example, the variables- 'total roadway length with 35mph posted speed limit' and 'long term parking cost' were statistically not significantly different from zero in the interior crash model but they were significantly different from zero at the 95% level in the boundary crash model.Although an adjacent traffic analysis zones (a single layer) were defined for pedestrian crashes and boundary pedestrian crashes were modeled based on the characteristic factors of these adjacent zones, this was not considered reasonable for bicycle-related crashes as the average roaming area of bicyclists are usually greater than that of pedestrians. For smaller TAZs sometimes it is possible for a bicyclist to cross the entire TAZ. To account for this greater area of coverage, boundary bicycle crashes were modeled based on two layers of adjacent zones. As observed from the goodness-of-fit measures, performances of model considering single layer variables and model considering two layer variables were superior from the models that did not consider layering at all; but these models were comparable. Motor vehicle crashes (total and severe crashes) were classified as 'on-system' and 'off-system' crashes and two sub-models were fitted in order to calibrate the safety performance function for these crashes. On-system and off-system roads refer to two different roadway hierarchies. On-system or state maintained roads typically possess higher speed limit and carries traffic from distant TAZs. Off-system roads are, however, mostly local roads with relatively low speed limits. Due to these distinct characteristics, on-system crashes were modeled with only population and total employment variables of a zone in addition to the roadway and traffic variables; and all other zonal variables were disregarded. For off-system crashes, on contrary, all zonal variables was considered. It was evident by comparing this on- and off-system sub-model-framework to the other candidate models that it provided superior goodness-of-fit for both total and severe crashes.Based on the safety performance functions developed for pedestrian, bicycle, total and severe crashes, the study proposed a novel and complete framework for assessing safety (of these crash types) simultaneously in parallel with the four-step transportation planning process with no need of any additional data requirements from the practitioners' side.
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Date Issued: 2012
Identifier: CFE0004191, ucf:49009
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004191

Title: A Comprehensive Severity Analysis of Large Vehicle Crashes.
Creator: Laman, Haluk, Abdel-Aty, Mohamed, Tatari, Mehmet, Ahmed, Mohamed, University of Central Florida
Abstract / Description: The goal of this thesis is to determine the contributing factors affecting severe traffic crashes (severe: incapacitating and fatal - non-severe: no injury, possible injury, and non-incapacitating), and in particular those factors influencing crashes involving large vehicles (heavy trucks, truck tractors, RVs, and buses). Florida Department of Highway Safety and Motor Vehicles (DHSMV) crash reports of 2008 have been used. The data included 352 fatalities and 9,838 injuries due to large...
Show moreThe goal of this thesis is to determine the contributing factors affecting severe traffic crashes (severe: incapacitating and fatal - non-severe: no injury, possible injury, and non-incapacitating), and in particular those factors influencing crashes involving large vehicles (heavy trucks, truck tractors, RVs, and buses). Florida Department of Highway Safety and Motor Vehicles (DHSMV) crash reports of 2008 have been used. The data included 352 fatalities and 9,838 injuries due to large vehicle crashes.Using the crashes involving large vehicles, a model comparison between binary logit model and a Chi-squared Automatic Interaction Detection (CHAID) decision tree model is provided. There were 13 significant factors (i.e. crash type with respect to vehicle types, residency of driver, DUI, rural-urban, etc.) found significant in the logistic procedure while 7 factors found (i.e. posted speed limit, intersection, etc.) in the CHAID model. The model comparison results indicate that the logit analysis procedure is better in terms of prediction power.The following analysis is a modeling structure involving three binary logit models. The first model was conducted to estimate the crash severity of crashes that involved only personal vehicles (PV). Second model uses the crashes that involved large vehicles (LV) and passenger vehicles (PV). The final model estimated the severity level of crashes involving only large vehicles (LV). Significant differences with respect to various risk factors including driver, vehicle, environmental, road geometry and traffic characteristics were found to exist between those crash types and models. For example, driving under the influence of Alcohol (DUI) has positive effect on the severity of PV vs. PV and LV vs. PV while it has no effect on LV vs. LV. As a result, 4 of the variables found to be significant were similar in all three models (although often with quite different impact) and there were 11 variables that significantly influenced crash injury severity in PV vs. PV crashes, and 9 variables that significantly influenced crash injury severity in LV vs. PV crashes.Based on the significant variables, maximum posted speed, number of vehicles involved, and intersections are among the factors that have major impact on injury severity. These results could be used to identify potential countermeasures to reduce crash severity in general, and for LVs in particular. For example, restricting the speed limits and enforcing it for large vehicles could be a suggested countermeasure based on this study.
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Date Issued: 2012
Identifier: CFE0004566, ucf:49216
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004566

Title: Dynamic Hotspot Identification for Limited Access Facilities using Temporal Traffic Data.
Creator: Al Amili, Samer, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Lee, JaeYoung, Wang, Chung-Ching, University of Central Florida
Abstract / Description: Crash frequency analysis is the most critical tool to investigate traffic safety problems. Therefore, an accurate crash analysis must be conducted. Since traffic continually fluctuates over time and this effects potential of crash occurrence, shorter time periods and less aggregated traffic factors (shorter intervals than AADT) need to be used. In this dissertation, several methodologies have been conducted to elevate the accuracy of crash prediction. The performance of using less aggregated...
Show moreCrash frequency analysis is the most critical tool to investigate traffic safety problems. Therefore, an accurate crash analysis must be conducted. Since traffic continually fluctuates over time and this effects potential of crash occurrence, shorter time periods and less aggregated traffic factors (shorter intervals than AADT) need to be used. In this dissertation, several methodologies have been conducted to elevate the accuracy of crash prediction. The performance of using less aggregated traffic data in modeling crash frequency was explored for weekdays and weekends. Four-time periods for weekdays and two time periods for weekends, with four intervals (5, 15, 30, and 60 minutes). The comparison between AADT based models and short-term period models showed that short-term period models perform better. As a shorter traffic interval than AADT considered, two difficulties began. Firstly, the number of zero observations increased. Secondly, the repetition of the same roadway characteristics arose. To reduce the number of zero observations, only segments with one or more crashes were used in the modeling process. To eliminate the effect of the repetition in the data, random effect was applied. The results recommend adopting segments with only one or more crashes, as they give a more valid prediction and less error.Zero-inflated negative binomial (ZINB) and hurdle negative binomial (HNB) models were examined in addition to the negative binomial for both weekdays and weekends. Different implementations of random effects were applied. Using the random effect either on the count part, on the zero part, or a pair of uncorrelated (or correlated) random effects for both parts of the model. Additionally, the adaptive Gaussian Quadrature, with five quadrature points, was used to increase accuracy. The results reveal that the model which considered the random effect in both parts performed better than other models, and ZINB performed better than HNB.
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Date Issued: 2018
Identifier: CFE0006966, ucf:51682
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006966

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: TRAFFIC CONFLICT ANALYSIS UNDER FOG CONDITIONS USING COMPUTER SIMULATION.
Creator: Zhang, Binya, Radwan, Essam, Abdel-Aty, Mohamed, Abou-Senna, Hatem, University of Central Florida
Abstract / Description: The weather condition is a crucial influence factor on road safety issues. Fog is one of the most noticeable weather conditions, which has a significant impact on traffic safety. Such condition reduces the road's visibility and consequently can affect drivers' vision, perception, and judgments. The statistical data shows that many crashes are directly or indirectly caused by the low-visibility weather condition. Hence, it is necessary for road traffic engineers to study the relationship of...
Show moreThe weather condition is a crucial influence factor on road safety issues. Fog is one of the most noticeable weather conditions, which has a significant impact on traffic safety. Such condition reduces the road's visibility and consequently can affect drivers' vision, perception, and judgments. The statistical data shows that many crashes are directly or indirectly caused by the low-visibility weather condition. Hence, it is necessary for road traffic engineers to study the relationship of road traffic accidents and their influence factors. Among these factors, the traffic volume and the speed limits in poor visibility areas are the primary reasons that can affect the types and occurring locations of road accidents.In this thesis, microscopic traffic simulation, through the use of VISSIM software, was used to study the road safety issue and its influencing factors due to limited visibility. A basic simulation model was built based on previously collected field data to simulate Interstate 4 (I-4)'s environment, geometry characteristics, and the basic traffic volume composition conditions. On the foundation of the basic simulation model, an experimental model was built to study the conflicts' types and distribution places under several different scenarios. Taking into consideration the entire 4-mile study area on I-4, this area was divided into 3 segments: section 1 with clear visibility, fog area of low visibility, and section 2 with clear visibility. Lower speed limits in the fog area, which were less than the limits in no-fog areas, were set to investigate the different speed limits' influence on the two main types of traffic conflicts: lane-change conflicts and rear-end conflicts. The experimental model generated several groups of traffic trajectory data files. The vehicle conflicts data were stored in these trajectory data files which, contains the conflict locations' coordinates, conflict time, time-to-conflict, and post-encroachment-time among other measures. The Surrogate Safety Assessment Model (SSAM), developed by the Federal Highway Administration, was applied to analyze these conflict data.From the analysis results, it is found that the traffic volume is an important factor, which has a large effect on the number of conflicts. The number of lane-change and rear-end conflicts increases along with the traffic volume growth. Another finding is that the difference between the speed limits in the fog area and in the no-fog areas is another significant factor that impacts the conflicts' frequency. Larger difference between the speed limits in two nearing road sections always leads to more accidents due to the inadequate reaction time for vehicle drivers to brake in time. And comparing to the scenarios that with the reduced speed limits in the low visibility zone, the condition that without the reduced speed limit has higher conflict number, which indicates that the it is necessary to put a lower speed limit in the fog zone which has a lower visibility. The results of this research have a certain reference value for studying the relationship between the road traffic conflicts and the impacts of different speed limits under fog condition. Overall, the findings of this research suggest follow up studies to further investigate possible relationships between conflicts as observed by simulation models and reported crashes in fog areas.
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Date Issued: 2015
Identifier: CFE0005747, ucf:50104
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005747

Title: Real-time traffic safety evaluation models and their application for variable speed limits.
Creator: Yu, Rongjie, Abdel-Aty, Mohamed, Radwan, Ahmed, Madani Larijani, Kaveh, Ahmed, Mohamed, Wang, Xuesong, University of Central Florida
Abstract / Description: Traffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly...
Show moreTraffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly developing safety performance functions (SPFs), are being conducted for the purpose of unveiling crash contributing factors for the interest locations. Results of the aggregate traffic safety studies can be used to identify crash hot spots, calculate crash modification factors (CMF), and improve geometric characteristics. Aggregate analyses mainly focus on discovering the hazardous factors that are related to the frequency of total crashes, of specific crash type, or of each crash severity level. While disaggregate studies benefit from the reliable surveillance systems which provide detailed real-time traffic and weather data. This information could help in capturing microlevel influences of the hazardous factors which might lead to a crash. The disaggregate traffic safety models, also called real-time crash risk evaluation models, can be used in monitoring crash hazardousness with the real-time field data fed in. One potential use of real-time crash risk evaluation models is to develop Variable Speed Limits (VSL) as a part of a freeway management system. Models have been developed to predict crash occurrence to proactively improve traffic safety and prevent crash occurrence.In this study, first, aggregate safety performance functions were estimated to unveil the different risk factors affecting crash occurrence for a mountainous freeway section. Then disaggregate real-time crash risk evaluation models have been developed for the total crashes with both the machine learning and hierarchical Bayesian models. Considering the need for analyzing both aggregate and disaggregate aspects of traffic safety, systematic multi-level traffic safety studies have been conducted for single- and multi-vehicle crashes, and weekday and weekend crashes. Finally, the feasibility of utilizing a VSL system to improve traffic safety on freeways has been investigated. This research was conducted based on data obtained from a 15-mile mountainous freeway section on I-70 in Colorado. The data contain historical crash data, roadway geometric characteristics, real-time weather data, and real-time traffic data. Real-time weather data were recorded by 6 weather stations installed along the freeway section, while the real-time traffic data were obtained from the Remote Traffic Microwave Sensor (RTMS) radars and Automatic Vechicle Identification (AVI) systems. Different datasets have been formulated from various data sources, and prepared for the multi-level traffic safety studies. In the aggregate traffic safety investigation, safety performance functions were developed to identify crash occurrence hazardous factors. For the first time real-time weather and traffic data were used in SPFs. Ordinary Poisson model and random effects Poisson models with Bayesian inference approach were employed to reveal the effects of weather and traffic related variables on crash occurrence. Two scenarios were considered: one seasonal based case and one crash type based case. Deviance Information Criterion (DIC) was utilized as the comparison criterion; and the correlated random effects Poisson models outperform the others. Results indicate that weather condition variables, especially precipitation, play a key role in the safety performance functions. Moreover, in order to compare with the correlated random effects Poisson model, Multivariate Poisson model and Multivariate Poisson-lognormal model have been estimated. Conclusions indicate that, instead of assuming identical random effects for the homogenous segments, considering the correlation effects between two count variables would result in better model fit. Results from the aggregate analyses shed light on the policy implication to reduce crash frequencies. For the studied roadway segment, crash occurrence in the snow season have clear trends associated with adverse weather situations (bad visibility and large amount of precipitation); weather warning systems can be employed to improve road safety during the snow season. Furthermore, different traffic management strategies should be developed according to the distinct seasonal influence factors. In particular, sites with steep slopes need more attention from the traffic management center and operators especially during snow seasons to control the excess crash occurrence. Moreover, distinct strategy of freeway management should be designed to address the differences between single- and multi-vehicle crash characteristics.In addition to developing safety performance functions with various modeling techniques, this study also investigates four different approaches of developing informative priors for the independent variables. Bayesian inference framework provides a complete and coherent way to balance the empirical data and prior expectations; merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance Information Criterion, R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparisons across the models indicate that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies.In addition to the aggregate analyses, real-time crash risk evaluation models have been developed to identify crash contributing factors at the disaggregate level. Support Vector Machine (SVM), a recently proposed statistical learning model and Hierarchical Bayesian logistic regression models were introduced to evaluate real-time crash risk. Classification and regression tree (CART) model has been developed to select the most important explanatory variables. Based on the variable selection results, Bayesian logistic regression models and SVM models with different kernel functions have been developed. Model comparisons based on receiver operating curves (ROC) demonstrate that the SVM model with Radial basis kernel function outperforms the others. Results from the models demonstrated that crashes are likely to happen during congestion periods (especially when the queuing area has propagated from the downstream segment); high variation of occupancy and/or volume would increase the probability of crash occurrence.Moreover, effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types have been investigated. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristics data were utilized as explanatory variables. Conclusions from this study imply that different active traffic management (ATM) strategies should be designed for three- and two-lane roadway sections and also considering the seasonal effects. Based on the abovementioned results, real-time crash risk evaluation models have been developed separately for multi-vehicle and single-vehicle crashes, and weekday and weekend crashes. Hierarchical Bayesian logistic regression models (random effects and random parameter logistic regression models) have been introduced to address the seasonal variations, crash unit level's diversities, and unobserved heterogeneity caused by geometric characteristics. For the multi-vehicle crashes: congested conditions at downstream would contribute to an increase in the likelihood of multi-vehicle crashes; multi-vehicle crashes are more likely to occur during poor visibility conditions and if there is a turbulent area that exists downstream. Drivers who are unable to reduce their speeds timely are prone to causing rear-end crashes. While for the single-vehicle crashes: slow moving traffic platoons at the downstream detector of the crash occurrence locations would increase the probability of single-vehicle crashes; large variations of occupancy downstream would also increase the likelihood of single-vehicle crash occurrence.Substantial efforts have been dedicated to revealing the hazardous factors that affect crash occurrence from both the aggregate and disaggregate level in this study, however, findings and conclusions from these research work need to be transferred into applications for roadway design and freeway management. This study further investigates the feasibility of utilizing Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm has been proposed. First, an extension of the traffic flow model METANET was employed to predict traffic flow while considering VSL's impacts on the flow-density diagram; a real-time crash risk evaluation model was then estimated for the purpose of quantifying crash risk; finally, the optimal VSL control strategies were achieved by employing an optimization technique of minimizing the total predicted crash risks along the VSL implementation area. Constraints were set up to limit the increase of the average travel time and differences between posted speed limits temporarily and spatially. The proposed VSL control strategy was tested for a mountainous freeway bottleneck area in the microscopic simulation software VISSIM. Safety impacts of the VSL system were quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels were modeled in VISSIM to monitor the sensitivity of VSL's safety impacts on driver compliance levels. Conclusions demonstrate that the proposed VSL system could effectively improve traffic safety by decreasing crash risk, enhancing speed homogeneity, and reducing travel time under both high and moderate driver compliance levels; while the VSL system does not have significant effects on traffic safety enhancement under the low compliance scenario. Future implementations of VSL control strategies and related research topics were also discussed.
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Date Issued: 2013
Identifier: CFE0005283, ucf:50556
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005283

Title: Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions.
Creator: Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description: Connected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to...
Show moreConnected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Vehicle platooning with CV technologies is another key element of the future transportation systems which helps to simultaneously enhance traffic operations and safety. CV technologies can also further increase the efficiency and reliability of automated vehicles (AV) by collecting real-time traffic information through V2V and V2I. However, the market penetration rate (MPR) of CAVs and the higher level of automation might not be fully available in the foreseeable future. Hence, it is worthwhile to study the safety benefits of CAV technologies under different MPRs and lower level of automation. None of the studies focused on both traffic safety and operational benefits for these technologies including different roadway, traffic, and weather conditions. In this study, the effectiveness of CAV technologies (i.e., CV /AV/CAV/CV platooning) were evaluated in different roadway, traffic, and weather conditions. To be more specific, the impact of CVs in reduced visibility condition, longitudinal safety evaluation of CV platooning in the managed lane, lower level of AVs in arterial roadway, and the optimal MPRs of CAVs for both peak and off-peak period are analyzed using simulation techniques. Currently, CAV fleet data are not easily obtainable which is one of the primary reasons to deploy the simulation techniques in this study to evaluate the impacts of CAVs in the roadway. The car following, lane changing, and the platooning behavior of the CAV technologies were modeled in the C++ programming language by considering realistic car following and lane changing models in PTV VISSIM. Surrogate safety assessment techniques were considered to evaluate the safety effectiveness of these CAV technologies, while the average travel time, average speed, and average delay were evaluated as traffic operational measures. Several statistical tests (i.e., Two sample t-test, ANOVA) and the modelling techniques (Tobit, Negative binomial, and Logistic regression) were conducted to evaluate the CAV effectiveness with different MPRs over the baseline scenario. The statistical tests and modeling results suggested that the higher the MPR of CAVs implemented, the higher were the safety and mobility benefits achieved for different roadways (i.e., freeway, expressway, arterials, managed lane), weather (i.e., clear, foggy), and traffic conditions (i.e., peak and off-peak period). Interestingly, from the safety and operation perspective, at least 30% and 20% MPR were needed to achieve both the safety and operational benefits of peak and off-peak period, respectively. This dissertation has major implications for improving transportation infrastructure by recommending optimal MPR of CAVs to achieve balanced mobility and safety benefits considering varying roadway, traffic, and weather condition.
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Date Issued: 2019
Identifier: CFE0007709, ucf:52442
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007709

Title: Evaluation of Real World Toll Plazas Using Driving Simulation.
Creator: Carroll, Kali, Abdel-Aty, Mohamed, Lee, JaeYoung, Eluru, Naveen, University of Central Florida
Abstract / Description: Toll plazas are becoming an essential part of the highway system, especially within the state of Florida. Many crashes reported on highways occur at toll plazas. A primary reason for many vehicle collisions happening at these facilities is the fact that each toll plaza agency has different design, signage and marking criteria. This, in turn, causes driver confusion and possible last minute weaving maneuvers. Even though the varying design of toll plazas is a clear highway safety factor,...
Show moreToll plazas are becoming an essential part of the highway system, especially within the state of Florida. Many crashes reported on highways occur at toll plazas. A primary reason for many vehicle collisions happening at these facilities is the fact that each toll plaza agency has different design, signage and marking criteria. This, in turn, causes driver confusion and possible last minute weaving maneuvers. Even though the varying design of toll plazas is a clear highway safety factor, research in the field is very limited but expanding. This study focuses on one toll plaza, in particular the Dean Mainline Toll Plaza, located in Orlando, Florida. The toll plaza is located directly between two roads that are in close proximity of each other. Because of this, the toll plaza is very close to the on- and off- ramps, which can be even more confusing and stressful for a driver entering or leaving the highway. The purpose of this study is to evaluate the safety and efficiency of the Dean Mainline Toll Plaza in order to make recommendations to improve or maintain the current toll plaza design, as well as potentially contribute to a nationally set design standard for toll plazas. Using the NADS miniSimTM Simulator, 72 subjects were recruited, and each subject was asked to drive 3 scenarios that were randomly selected from a pool of 24 scenarios. The following factors were changed in order to study the driver's behavior: signage and their location, pavement markings, distances between the toll plaza and ramps, and traffic conditions. All of these factors were altered and observed on five of the eight possible routes than can be taken through the toll plaza. The subjects were asked to complete questionnaires before and after all of the scenarios, as well as in between each driving scenario. These questionnaires included demographic characteristics, such as age, education, income, E-PASS ownership, etc. The data that were collected by the driving simulator and questionnaires were analyzed by ANOVA and multinomial logistic regression models. A positive relationship was found between non-urgent lane changing and the current real-world sign conditions prior to the toll plaza. Relationships were also found between the subjects' speed in various locations and signage before the toll plaza and segment length after the toll plaza. Along with specified recommendations for future research in toll plaza safety, recommendations for the Dean Mainline Toll Plaza include maintaining the current signs and pavement markings, as they were found to be beneficial in drivers performing safe lane changing maneuvers.
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Date Issued: 2016
Identifier: CFE0006085, ucf:50960
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006085

Title: The effectiveness of Child Restraint and Bicycle Helmet Policies to Improve Road Safety.
Creator: Bustamante, Claudia, Abdel-Aty, Mohamed, Eluru, Naveen, Lee, JaeYoung, University of Central Florida
Abstract / Description: Analyzing the effect of legislation in children's safety when they travel as motor-vehicle passengers and bicycle riders can allow us to evaluate the effectiveness in transportation policies. The Child Restraint Laws (CRL) and Bicycle Helmet Laws (BHL) were studied by analyzing the nationwide Fatality Analysis Reporting System (FARS) to estimate the fatality reduction as well as drivers' decisions to use Child Restraint Systems (CRS) and bicycle helmets respectively. Differences in...
Show moreAnalyzing the effect of legislation in children's safety when they travel as motor-vehicle passengers and bicycle riders can allow us to evaluate the effectiveness in transportation policies. The Child Restraint Laws (CRL) and Bicycle Helmet Laws (BHL) were studied by analyzing the nationwide Fatality Analysis Reporting System (FARS) to estimate the fatality reduction as well as drivers' decisions to use Child Restraint Systems (CRS) and bicycle helmets respectively. Differences in legislation could have different effects on traffic fatalities. Therefore, this study presents multiple methodologies to study these effects. In the evaluation of traffic safety issues, several proven statistical models have shown to be effective at estimating risky factors that might influence crash prevention. These proven models and predictive data analysis guided the process to attempt different models, leading to the development of three specific models used in this study to best estimate the effectiveness of these laws. Then, it was found that legislation in Child Safety Policy has consequences in traffic fatalities. A negative binomial model was created to analyze the CRL influence at the state-level in fatal crashes involving children, and showed that legislating on CRS can reduce the number of fatalities by 29% for children aged 5 to 9. Additionally, at the drivers-level a logistic regression model with random effects was used to determine the significant variables that influence the driver's decision to restrain his/her child. Such variables include: driver's restraint use, road classification, weather condition, number of occupants in the vehicle, traffic violations and driver's and child's age. It was also shown that drivers from communities with deprived socio-economic status are less likely to use CRS. In the same way, a binary logistic regression model was developed to evaluate the effect of BHL in bicycle helmet-use. Findings from this model show that bicyclists from states with the BHL are 236 times more likely to wear a helmet compared to those from states without the BHL. Moreover, the bicyclist's age, gender, education, and income level also influences bicycle helmet use. Both studies suggest that enacting CRL and BHL at the state-level for the studied age groups can be combined with education, safety promotion, enforcement, and program evaluation as proven countermeasures to increase children's traffic safety. This study evidenced that there is a lack of research in this field, especially when policy making requires having enough evidence to support the laws in order to not become an arbitrary legislation procedure affecting child's protection in the transportation system.
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Date Issued: 2017
Identifier: CFE0006571, ucf:51315
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006571

Title: SAFETY ISSUES OF RED-LIGHT RUNNING AND UNPROTECTED LEFT-TURN AT SIGNALIZED INTERSECTIONS.
Creator: Yan, Xuedong, Radwan, Essam, University of Central Florida
Abstract / Description: Crashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity...
Show moreCrashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity of three aspects of risk factors related to traffic environments, driver characteristics, and vehicle types. A quasi-induced exposure concept and statistical techniques including classification tree model and multiple logistic regression were used to perform this analysis. Secondly, the UCF driving simulator was applied to test the effect of a proposed new pavement marking countermeasure which purpose is to reduce the red-light running rate at signalized intersections. The simulation experiment results showed that the total red-light running rate with marking is significantly lower than that without marking. Moreover, deceleration rate of stopping drivers with marking for the higher speed limit are significantly less than those without marking. These findings are encouraging and suggesting that the pavement marking may result in safety enhancement as far as right-angle and rear-end traffic crashes at signalized intersections. Thirdly, geometric models to compute sight distances of unprotected left-turns were developed for different signalized intersection configurations including a straight approach leading to a straight one, a straight approach leading to a curved one, and a curved approach leading to a curved one. The models and related analyses can be used to layout intersection design or evaluate the sight distance problem of an existing intersection configuration to ensure safe left-turn maneuvers by drivers.
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Date Issued: 2005
Identifier: CFE0000451, ucf:46389
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000451

Title: SAFETY ISSUES OF RED-LIGHT RUNNING AND UNPROTECTED LEFT-TURN AT SIGNALIZED INTERSECTIONS.
Creator: Yan, Xuedong, Radwan, Essam, University of Central Florida
Abstract / Description: Crashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity...
Show moreCrashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity of three aspects of risk factors related to traffic environments, driver characteristics, and vehicle types. A quasi-induced exposure concept and statistical techniques including classification tree model and multiple logistic regression were used to perform this analysis. Secondly, the UCF driving simulator was applied to test the effect of a proposed new pavement marking countermeasure which purpose is to reduce the red-light running rate at signalized intersections. The simulation experiment results showed that the total red-light running rate with marking is significantly lower than that without marking. Moreover, deceleration rate of stopping drivers with marking for the higher speed limit are significantly less than those without marking. These findings are encouraging and suggesting that the pavement marking may result in safety enhancement as far as right-angle and rear-end traffic crashes at signalized intersections. Thirdly, geometric models to compute sight distances of unprotected left-turns were developed for different signalized intersection configurations including a straight approach leading to a straight one, a straight approach leading to a curved one, and a curved approach leading to a curved one. The models and related analyses can be used to layout intersection design or evaluate the sight distance problem of an existing intersection configuration to ensure safe left-turn maneuvers by drivers.
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Date Issued: 2005
Identifier: CFE0000401, ucf:46347
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000401

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: SAFETY EFFECTS OF TRAFFIC SIGNAL INSTALLATIONS ON STATE ROAD INTERSECTIONS IN NORTHEAST FLORIDA.
Creator: LeDew, Christopher, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: The purpose of this thesis is to explore how the installations of traffic signals affect crash experience at intersections, to identify those factors which help predict crashes after a signal is installed, and to develop a crash prediction model. It is the intent of this thesis to supplement the Manual on Uniform Traffic Control Devices Signal Warrant procedure and aid the traffic engineer in the signal installation decision making process. Crash data, as well as operational and geometric...
Show moreThe purpose of this thesis is to explore how the installations of traffic signals affect crash experience at intersections, to identify those factors which help predict crashes after a signal is installed, and to develop a crash prediction model. It is the intent of this thesis to supplement the Manual on Uniform Traffic Control Devices Signal Warrant procedure and aid the traffic engineer in the signal installation decision making process. Crash data, as well as operational and geometric factors were examined for 32 state road intersections in the northeast Florida area before and after signal installation. Signal warrant studies were used as sources for traffic volumes, geometric information and crash history, before signal installation. The Florida Department of Transportation's Crash Analysis Reporting System (CARS) was used to gather crash data for the time period after signal installation. On average, the 32 intersections experienced a 12% increase in the total number of crashes and a 26% reduction in crash rate after signals were installed. The change in the number of crashes was not significant, but the rate change was significant with 90% confidence. Angle crash frequency dropped by 60% and the angle crash rate dropped by 66%, both are significant. Left-turn crashes dropped by 8% and their rate by 16%, although neither was significant. Rear-end crashes increased by 86% and the rear-end crash rate decreased by 5%. Neither of these changes was statistically significant. When crash severity was examined, it was found that the number of injury crashes increased by 64.8% and the rate by only 0.02%. Neither change was significant. Both the number of fatal crashes and the rate decreased by 100% and were significant. Property Damage Only (PDO) crashes increased by 96%, after signalization, but this change was not significant. The PDO rate, however, decreased by 46.5% and is significant. Operational factors such as AADT, turning movement counts, and speed limits; and geometric factors such as medians, turn lanes and numbers of lanes were considered to determine their effect on crashes at signalized intersections. Smaller roads, with low AADT, fewer lanes, and a rural character were found to benefit from signalization more than busier urbanized roads, in terms of crash rate reduction. The AADT, roadway cross section, number of lanes, medians, speed limit and left turn volume were all found to be important factors influencing crash rates. This thesis recommends: 1) the use of crash prediction models to supplement the MUTCD Crash Warrant, 2) the addition of a left-turn warrant to the MUTCD signal warranting procedure, and 3) development of an intersection database containing crash data as well as operational and geometric information to aid in future research.
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Date Issued: 2006
Identifier: CFE0001335, ucf:46972
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001335

Title: Investigation of factors contributing to fog-related single vehicle crashes.
Creator: Zhu, Jiazheng, Abdel-Aty, Mohamed, Hasan, Samiul, Wu, Yina, University of Central Florida
Abstract / Description: Fog-related crashes continue to be one of the most serious traffic safety problems in Florida. Based on the historical crash data, we found that single-vehicle crashes have the highest severity among all types of crashes under fog conditions. This study first analyzed the contributing factors of the fog-related single-vehicle crashes' (i.e., off road/rollover/other) severity in Florida from 2011 to 2014 using association rules mining. The results show that lane departure distracted driving,...
Show moreFog-related crashes continue to be one of the most serious traffic safety problems in Florida. Based on the historical crash data, we found that single-vehicle crashes have the highest severity among all types of crashes under fog conditions. This study first analyzed the contributing factors of the fog-related single-vehicle crashes' (i.e., off road/rollover/other) severity in Florida from 2011 to 2014 using association rules mining. The results show that lane departure distracted driving, wet road surface, and dark without road light are the main contributing factors to severe fog-related single vehicle crashes. Some suggested countermeasures were also provided to reduce the risk of fog-related single vehicle crashes. Since lane departure is one of the most important contributing factors to the single-vehicle crashes, an advanced warning system for lane departure under connected vehicle system was tested in driving simulation experiments. The system was designed based on the Vehicle-to-Infrastructure (V2I) with the concept of Augmented Reality (AR) using Head-Up Display (HUD). The results show that the warning with sound would reduce the lane departure and speed at curves, which would enhance the safety under fog conditions. In addition, the warning system was more effective for female drivers.
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Date Issued: 2018
Identifier: CFE0007118, ucf:51935
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007118

Title: Safety, Operational, and Design Analyses of Managed Toll and Connected Vehicles' Lanes.
Creator: Saad, Moatz, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Oloufa, Amr, Yan, Xin, University of Central Florida
Abstract / Description: Managed lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is...
Show moreManaged lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is achieved by determining the optimal managed lanes access design, including accessibility level and weaving distance for an at-grade access design. (2) the second objective is to study the effects of applying CVs and CV lanes on the MLs network. Several scenarios were tested using microscopic traffic simulation to determine the optimal access design while taking into consideration accessibility levels and weaving lengths. Both safety (e.g., standard deviation of speed, time-to-collision, and conflict rate) and operational (e.g., level of service, average speed, average delay) performance measures were included in the analyses. For the first objective, the results suggested that one accessibility level is the optimal option for the 9-mile network. A weaving length between 1,000 feet to 1,400 feet per lane change was suggested based on the safety analysis. From the operational perspective, a weaving length between 1,000 feet and 2,000 feet per lane change was recommended. The findings also suggested that MPR% between 10% and 30% was recommended when the CVs are only allowed in MLs. When increasing the number of MLs, the MPR% could be improved to reach 70%. Lastly, the findings proposed that MPR% of 100% could be achieved by allowing the CVs to use all the lanes in the network.
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Date Issued: 2019
Identifier: CFE0007719, ucf:52428
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007719

Title: Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation.
Creator: Jamialahmadi, Ahmad, Pourmohammadi Fallah, Yaser, Rahnavard, Nazanin, Chatterjee, Mainak, University of Central Florida
Abstract / Description: A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test...
Show moreA critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network.
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Date Issued: 2018
Identifier: CFE0007573, ucf:52578
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007573

Title: Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification.
Creator: Lee, JaeYoung, Abdel-Aty, Mohamed, Radwan, Ahmed, Nam, Boo Hyun, Kuo, Pei-Fen, Choi, Keechoo, University of Central Florida
Abstract / Description: Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic...
Show moreTraffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act (-) A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.
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Date Issued: 2014
Identifier: CFE0005195, ucf:50653
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005195

Title: Improving Safety under Reduced Visibility Based on Multiple Countermeasures and Approaches including Connected Vehicles.
Creator: Wu, Yina, Abdel-Aty, Mohamed, Lee, JaeYoung, Eluru, Naveen, University of Central Florida
Abstract / Description: The effect of low visibility on both crash occurrence and severity is a major concern in the traffic safety field. Different approaches were utilized in this research to analyze the effects of fog on traffic safety and evaluate the effectiveness of different fog countermeasures. First, a (")Crash Risk Increase Indicator (CRII)(") was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash...
Show moreThe effect of low visibility on both crash occurrence and severity is a major concern in the traffic safety field. Different approaches were utilized in this research to analyze the effects of fog on traffic safety and evaluate the effectiveness of different fog countermeasures. First, a (")Crash Risk Increase Indicator (CRII)(") was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risk with traffic flow characteristics. Second, a new algorithm was proposed to evaluate the rear-end crash risk under fog conditions. Logistic and negative binomial models were estimated in order to explore the relationship between the potential of rear-end crashes and the reduced visibility together with other traffic parameters. Moreover, the effectiveness of real-time fog warning systems was assessed by quantifying and characterizing drivers' speed adjustments through driving simulator experiments. A hierarchical assessment concept was suggested to explore the drivers' speed adjustment maneuvers. Two linear regression models and one hurdle beta regression model were estimated for the indexes. Also, another driving simulator experiment was conducted to explore the effectiveness of Connected-Vehicles (CV) crash warning systems on the drivers' awareness of the imminent situation ahead to take timely crash avoidance action(s). Finally, a micro-simulation experiment was also conducted to evaluate the safety benefits of a proposed Variable Speed limit (VSL) strategy and CV technologies. The proposed VSL strategy and CV technologies were implemented and tested for a freeway section through the micro-simulation software VISSIM. The results of the above mentioned studies showed the impact of reduced visibility on traffic safety, and the effectiveness of different fog countermeasures.
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Date Issued: 2017
Identifier: CFE0006928, ucf:51704
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006928

Title: Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models.
Creator: Cai, Qing, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Lee, JaeYoung, Yan, Xin, University of Central Florida
Abstract / Description: Crash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so...
Show moreCrash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so as to provide countermeasures from a planning perspective. On the other hand, the microscopic crashes on a segment or intersection are analyzed to identify the influence of geometric design, lighting and traffic flow characteristics with the objective of offering engineering solutions (such as installing sidewalk and bike lane, adding lighting). Although numerous traffic safety studies have been conducted, still there are critical limitations at both levels. In this dissertation, several methodologies have been proposed to alleviate several limitations in the macro- and micro-level safety research. Then, an innovative method has been suggested to analyze crashes at the two levels, simultaneously. At the macro-level, the viability of dual-state models (i.e., zero-inflated and hurdle models) were explored for traffic analysis zone based pedestrian and bicycle crash analysis. Additionally, spatial spillover effects were explored in the models by employing exogenous variables from neighboring zones. Both conventional single-state model (i.e., negative binomial) and dual-state models such as zero-inflated negative binomial and hurdle negative binomial models with and without spatial effects were developed. The model comparison results for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency. Then, the modifiable areal unit problem for macro-level crash analysis was discussed. Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), traffic analysis zones (TAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) were developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposed a method to compare the modeling performance of the three types of geographic units at different spatial configuration through a grid based framework. Specifically, the study region was partitioned to grids of various sizes and the model prediction accuracy of the various macro models was considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperformed the ones that do not consider it. Finally, based on the modeling results, it is recommended to adopt TADs for transportation safety planning.After determining the optimal traffic safety analysis zonal system, further analysis was conducted for non-motorist crashes (pedestrian and bicycle crashes). This study contributed to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we converted the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulated a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model was estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model was also estimated and compared with the joint model. The results indicated that the joint model provides better data fit and could identify more significant variables. Subsequently, a novel joint screening method was suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes were identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. At the microscopic level, crash modeling analysis was conducted for road facilities. This study, first, explored the potential macro-level effects which are always excluded or omitted in the previous studies. A Bayesian hierarchical model was proposed to analyze crashes on segments and intersection incorporating the macro-level data, which included both explanatory variables and total crashes of all segments and intersections. Besides, a joint modeling structure was adopted to consider the potentially spatial autocorrelation between segments and their connected intersections. The proposed model was compared with three other models: a model considering micro-level factors only, one hierarchical model considering macro-level effects with random terms only, and one hierarchical model considering macro-level effects with explanatory variables. The results indicated that models considering macro-level effects outperformed the model having micro-level factors only, which supports the idea to consider macro-level effects for micro-level crash analysis. Besides, the micro-level models were even further enhanced by the proposed model. Finally, significant spatial correlation could be found between segments and their adjacent intersections, supporting the employment of the joint modeling structure to analyze crashes at various types of road facilities. In addition to the separated analysis at either the macro- or micro-level, an integrated approach has been proposed to examine traffic safety problems at the two levels, simultaneously. If conducted in the same study area, the macro- and micro-level crash analyses should investigate the same crashes but aggregating the crashes at different levels. Hence, the crash counts at the two levels should be correlated and integrating macro- and micro-level crash frequency analyses in one modeling structure might have the ability to better explain crash occurrence by realizing the effects of both macro- and micro-level factors. This study proposed a Bayesian integrated spatial crash frequency model, which linked the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considered the spatial autocorrelation of different types of road facilities (i.e., segments and intersections) at the micro-level with a joint modeling structure. Two independent non-integrated models for macro- and micro-levels were also estimated separately and compared with the integrated model. The results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model provides more valuable insights about the crash occurrence at the two levels by revealing both macro- and micro-level factors. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels. It is expected that the proposed integrated model and hotspot identification method can help practitioners implement more reasonable transportation safety plans and more effective engineering treatments to proactively enhance safety.
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Date Issued: 2017
Identifier: CFE0006724, ucf:51891
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006724

Traffic Safety (7)	+ -
traffic safety (5)	+ -
Driving Simulator (3)	+ -
fog (3)	+ -
Intelligent Transportation Systems (2)	+ -

Red-light Running (2)	+ -
Sight Distance (2)	+ -
Signalized Intersection (2)	+ -
Traffic Simulation (2)	+ -
Traffic safety (2)	+ -
Unprotected Left-turn (2)	+ -
VISSIM (2)	+ -
driving simulator (2)	+ -
road safety (2)	+ -
Access Zones (1)	+ -
Accident analysis (1)	+ -
Active Traffic Management (1)	+ -
Advanced Traveler Information Systems (1)	+ -
Bayesian Inference (1)	+ -
Bayesian inference (1)	+ -
Bayesian modeling (1)	+ -
Big Data (1)	+ -
CMF (1)	+ -
CMS (1)	+ -
Car following model (1)	+ -
Connected Vehicle Lanes (1)	+ -
Connected Vehicles (1)	+ -
Connected and Automated Vehicles (1)	+ -
Connected and automated vehicle (1)	+ -
DMS (1)	+ -

University of Central Florida (22)	+ -
Abdel-Aty, Mohamed (19)	+ -
Eluru, Naveen (8)	+ -
Lee, JaeYoung (6)	+ -
Radwan, Essam (5)	+ -

Hasan, Samiul (4)	+ -
Ahmed, Mohamed (3)	+ -
Radwan, Ahmed (3)	+ -
Uddin, Nizam (3)	+ -
Yan, Xin (3)	+ -
Nam, Boo Hyun (2)	+ -
Wu, Yina (2)	+ -
Yan, Xuedong (2)	+ -
Abou-Senna, Hatem (1)	+ -
Abuzwidah, Muamer (1)	+ -
Al Amili, Samer (1)	+ -
Al-Deek, Haitham (1)	+ -
Bustamante, Claudia (1)	+ -
Cai, Qing (1)	+ -
Carroll, Kali (1)	+ -
Chatterjee, Mainak (1)	+ -
Choi, Keechoo (1)	+ -
Dos Santos, Cristina (1)	+ -
Huang, Helai (1)	+ -
Jamialahmadi, Ahmad (1)	+ -
Kowdla, Smitha (1)	+ -
Kuo, Pei-Fen (1)	+ -
Laman, Haluk (1)	+ -
LeDew, Christopher (1)	+ -
Lee, Chris (1)	+ -

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