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Pages
- Title
- Macroscopic Crash Analysis and Its Implications for Transportation Safety Planning.
- Creator
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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.
Show less - Date Issued
- 2012
- Identifier
- CFE0004191, ucf:49009
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004191
- Title
- A Framework for Assessing Sustainability Impacts of Truck Routing Strategies.
- Creator
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Laman, Haluk, Abdel-Aty, Mohamed, Tatari, Omer, Ahmed, Mohamed, University of Central Florida
- Abstract / Description
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The impact of freight on our transportation system is further accentuated by the fact that trucks consume greater roadway capacity and therefore cause more significant problems including traffic congestion, delay, crashes, air pollution, fuel consumption, and pavement damage. Assessing the actual effects of truck traffic is a growing need to support the ability to safely and efficiently move goods and people in areas where roadway expansion is not the best option. On one hand, trucks need to...
Show moreThe impact of freight on our transportation system is further accentuated by the fact that trucks consume greater roadway capacity and therefore cause more significant problems including traffic congestion, delay, crashes, air pollution, fuel consumption, and pavement damage. Assessing the actual effects of truck traffic is a growing need to support the ability to safely and efficiently move goods and people in areas where roadway expansion is not the best option. On one hand, trucks need to efficiently serve commerce and industry, while at the same time their activities need not contribute to a decline in the quality or public safety. In the current practice, to the best of the authors' knowledge, there is no framework methodology for real-time management of traffic, specifically on truck routes, to reduce travel duration and avoid truck travel delays due to non-recurring congestion (i.e. traffic incidents) and to estimate impacts on traffic flows, economy, and environment. The objective of this study is to develop a truck routing strategy and to quantify its' impacts on travel time, emissions and consequently assess the effects on the economy and environment. In order to estimate non-recurrent congestion based travel delay and fuel consumption by real-time truck routing simulation models, significant corridors with high truck percentages were selected. Furthermore, tailpipe emissions (on-site) due to traveled distance and idling are estimated via MOVES emissions simulator software. Economic Input Output-Life Cycle Assessment Model is utilized to gather fuel consumption related upstream (off-site) emissions. Simulation results of various scenarios indicated that potential annual value of time savings can reach up to $1.67 million per selected corridor. Consistently, fuel costs and emission values are lower, even though extra miles are traveled on the alternative route. In conclusion, our study confirms that truck routing strategies in incident conditions have high economic and environmental impacts.
Show less - Date Issued
- 2018
- Identifier
- CFE0007577, ucf:52570
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007577
- Title
- Applications of Deep Learning Models for Traffic Prediction Problems.
- Creator
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Rahman, Rezaur, Hasan, Samiul, Abdel-Aty, Mohamed, Zaki Hussein, Mohamed, University of Central Florida
- Abstract / Description
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Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first...
Show moreDeep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS.
Show less - Date Issued
- 2019
- Identifier
- CFE0007516, ucf:52654
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007516
- 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.
Show less - Date Issued
- 2012
- Identifier
- CFE0004566, ucf:49216
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004566
- Title
- SEVERITY ANALYSIS OF DRIVER CRASH INVOLVEMENTS ON MULTILANE HIGH SPEED ARTERIAL CORRIDORS.
- Creator
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Nevarez-Pagan, Alexis, Abdel-Aty, Mohamed, University of Central Florida
- Abstract / Description
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Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver...
Show moreArterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.
Show less - Date Issued
- 2008
- Identifier
- CFE0002080, ucf:47591
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002080
- Title
- DEVELOPING EMERGENCY PREPAREDNESS PLANS FOR ORLANDO INTERNATIONAL AIRPORT (MCO) USING MICROSCOPIC SIMULATOR WATSIM.
- Creator
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Dawson, Daniel, Abdel-Aty, Dr. Mohamed, University of Central Florida
- Abstract / Description
-
Emergency preparedness typically involves the preparation of detailed plans that can be implemented in response to a variety of possible emergencies or disruptions to the transportation system. One shortcoming of past response plans was that they were based on only rudimentary traffic analysis or in many cases none at all. With the advances in traffic simulation during the last decade, it is now possible to model many traffic problems, such as emergency management, signal control and testing...
Show moreEmergency preparedness typically involves the preparation of detailed plans that can be implemented in response to a variety of possible emergencies or disruptions to the transportation system. One shortcoming of past response plans was that they were based on only rudimentary traffic analysis or in many cases none at all. With the advances in traffic simulation during the last decade, it is now possible to model many traffic problems, such as emergency management, signal control and testing of Intelligent Transportation System technologies. These problems are difficult to solve using the traditional tools, which are based on analytical methods. Therefore, emergency preparedness planning can greatly benefit from the use of micro-simulation models to evaluate the impacts of natural and man-made incidents and assess the effectiveness of various responses. This simulation based study assessed hypothetical emergency preparedness plans and what geometric and/or operational improvements need to be done in response to emergency incidents. A detailed framework outlining the model building, calibration and validation of the model using microscopic traffic simulation model WATSim (academic version) is provided. The Roadway network data consists of geometric layout of the network, number of lanes, intersection description which include the turning bays, signal timings, phasing sequence, turning movement information etc. The network in and around the OIA region is coded into WATSim with 3 main signalized intersections, 180 nodes and 235 links. The travel demand data includes the vehicle counts in each link of the network and was modeled as percentage turning count movements. After the OIA network was coded into WATSim, the road network was calibrated and validated for the peak hour mostly obtained from ADT with 8% K factor by comparing the simulated and actual link counts at 15 different key locations in the network and visual verification done. Ranges of scenarios were tested that includes security checkpoint, route diversion incase of incident in or near the airport and increasing demand on the network. Travel time, maximum queue length and delay were used as measures of effectiveness and the results tabulated. This research demonstrates the potential benefits of using microscopic simulation models when developing emergency preparedness strategies. In all 4 main Events were modeled and analyzed. In Event 1, occurrence of 15 minutes traffic incident on a section of South Access road was simulated and its impact on the network operations was studied. The averaged travel time under the incident duration to Side A was more than doubled (29 minutes, more than a 100% increase) compared to the base case and similarly that of Side B two and a half times more (23 minutes, also more than a 100% increase). The overall network performance in terms of delay was found to be 231.09 sec/veh. and baseline 198.9 sec/veh. In Event 2, two cases with and without traffic diversions were assumed and evaluated under 15 minutes traffic incident modeled at the same link and spot as in Event 1. It was assumed that information about the traffic incident was disseminated upstream of the incident 2 minutes after the incident had occurred. This scenario study demonstrated that on the average, 17% (4 minutes) to 41% (12 minutes) per vehicle of travel time savings are achieved when real-time traffic information was provided to 26% percent of the drivers diverted. The overall network performance in delay for this event was also found to improve significantly (166.92 sec/veh). These findings led to the conclusion that investment in ITS technologies that support dissemination of traffic information (such as Changeable Message Signs, Highway Advisory Radio, etc) would provide a great advantage in traffic management under emergency situations and road diversion strategies. Event 3 simulated a Security Check point. It was observed that on the average, travel times to Sides A and B was 3 and 5 minutes more respectively compared to its baseline. Averaged queue length of 650 feet and 890 feet worst case was observed. Event 4 determined when and where the network breaks down when loaded. Among 10 sets of demand created, the network appeared to be breaking down at 30% increase based on the network-wide delay and at 15% based on Level of Service (LOS). The 90% increase appeared to have the most effect on the network with a total network-wide delay close to 620 seconds per vehicle which is 3 and a half times compared to the baseline. Conclusions and future scope were provided to ensure continued safe and efficient traffic operations inside and outside the Orlando International Airport region and to support efficient and informed decision making in the face of emergency situations.
Show less - Date Issued
- 2006
- Identifier
- CFE0000984, ucf:46705
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000984
- Title
- SAFETY EFFECTS OF TRAFFIC SIGNAL INSTALLATIONS ON STATE ROAD INTERSECTIONS IN NORTHEAST FLORIDA.
- Creator
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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.
Show less - Date Issued
- 2006
- Identifier
- CFE0001335, ucf:46972
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001335
- Title
- ANALYSES OF CRASH OCCURENCE AND INURY SEVERITIES ON MULTI LANE HIGHWAYS USING MACHINE LEARNING ALGORITHMS.
- Creator
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Das, Abhishek, Abdel-Aty, Mohamed A., University of Central Florida
- Abstract / Description
-
Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior;...
Show moreReduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; adverse weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributing factors for crashes and injury severity. Advances in computational methods render the use of modern statistical and machine learning algorithms. Even though most of the contributing factors are known a-priori, advanced methods unearth changing trends. Heuristic evolutionary processes such as genetic programming; sophisticated data mining methods like conditional inference tree; and mathematical treatments in the form of sensitivity analyses outline the major contributions in this research. Application of traditional statistical methods like simultaneous ordered probit models, identification and resolution of crash data problems are also key aspects of this study. In order to eliminate the use of unrealistic uniform intersection influence radius of 250 ft, heuristic rules were developed for assigning crashes to roadway segments, signalized intersection and access points using parameters, such as 'site location', 'traffic control' and node information. Use of Conditional Inference Forest instead of Classification and Regression Tree to identify variables of significance for injury severity analysis removed the bias towards the selection of continuous variable or variables with large number of categories. For the injury severity analysis of crashes on highways, the corridors were clustered into four optimum groups. The optimum number of clusters was found using Partitioning around Medoids algorithm. Concepts of evolutionary biology like crossover and mutation were implemented to develop models for classification and regression analyses based on the highest hit rate and minimum error rate, respectively. Low crossover rate and higher mutation reduces the chances of genetic drift and brings in novelty to the model development process. Annual daily traffic; friction coefficient of pavements; on-street parking; curbed medians; surface and shoulder widths; alcohol / drug usage are some of the significant factors that played a role in both crash occurrence and injury severities. Relative sensitivity analyses were used to identify the effect of continuous variables on the variation of crash counts. This study improved the understanding of the significant factors that could play an important role in designing better safety countermeasures on multi lane highways, and hence enhance their safety by reducing the frequency of crashes and severity of injuries. Educating young people about the abuses of alcohol and drugs specifically at high schools and colleges could potentially lead to lower driver aggression. Removal of on-street parking from high speed arterials unilaterally could result in likely drop in the number of crashes. Widening of shoulders could give greater maneuvering space for the drivers. Improving pavement conditions for better friction coefficient will lead to improved crash recovery. Addition of lanes to alleviate problems arising out of increased ADT and restriction of trucks to the slower right lanes on the highways would not only reduce the crash occurrences but also resulted in lower injury severity levels.
Show less - Date Issued
- 2009
- Identifier
- CFE0002928, ucf:48007
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002928
- Title
- REDUCED VISIBILITY RELATED CRASHES IN FLORIDA: CRASH CHARACTERISTICS, SPATIAL ANALYSIS AND INJURY SEVERITY.
- Creator
-
EKRAM, AL-AHAD, Abdel-Aty, Mohamed, University of Central Florida
- Abstract / Description
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Roadway crashes related to vision obstruction due to fog/smoke (FS) conditions constitute a challenge for traffic engineers. Previous research efforts mostly concentrated on the snow and rain related crashes. Statistics show that Florida is among the top three states in terms of crashes due to vision obstruction by FS. This research culminated in a comprehensive study of fog and smoke related crashes in the state of Florida. The analysis took into account the crashes that occurred between...
Show moreRoadway crashes related to vision obstruction due to fog/smoke (FS) conditions constitute a challenge for traffic engineers. Previous research efforts mostly concentrated on the snow and rain related crashes. Statistics show that Florida is among the top three states in terms of crashes due to vision obstruction by FS. This research culminated in a comprehensive study of fog and smoke related crashes in the state of Florida. The analysis took into account the crashes that occurred between 2003 and 2007 on Florida state roads. Spatial analysis and injury severity analysis have been conducted and significant results have been identified. The spatial analysis by GIS examines the locations of high trends of FS related crashes on state roads in the State of Florida. Statistical features of the GIS tool, which is used efficiently in traffic safety research, has been used to find the crash clusters for the particular types of crashes that occur due to vision obstruction by FS. Several segmentation processes have been used, and the best segmentation for this study was found to be dividing the state roads into 1 mile segments, keeping the roadway characteristics uniform. Taking into account the entire state road network, ten distinct clusters were found that can be clearly associated with these types of crashes. However, no clear pattern in terms of area was observed, as it was seen that the percentage of FS related crashes in rural and urban areas are close. The general characteristics of FS related crashes have been investigated in detail. For the comparison to clear visibility conditions, simple odds ratios (in terms of crash frequencies) have been introduced. The morning hours in the months of December to February are found to be the prevalent time for fog related crashes, while for the smoke related crashes the dangerous time was found to be morning to midday in the month of May. Compared to crashes under clear-visibility conditions, the fog crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash frequency and severe crashes. For the injury severity analysis, a random effect ordered logistic model was used. The model in brief illustrates that the head-on and rear-end crash types are the two most prevalent crash types in FS conditions. Moreover, these severe crashes mainly occurred at higher speeds. Also they mostly took place on undivided roads, roadways without any sidewalk and two-lane rural roads. Increase of average daily traffic decrease the severity of FS related crashes. Overall, this study provides the Florida Department of Transportation (FDOT) with specific information on where improvements could be made to have better safety conditions in terms of vision obstruction due to FS in the state roads of Florida. Also it suggests the times and seasons that the safety precautions must be taken or the FS warning systems to be installed, and the controlling roadway geometries that can be improved or modified to reduce injury severity of a crash due to FS related vision obstruction.
Show less - Date Issued
- 2009
- Identifier
- CFE0002903, ucf:48008
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002903
- Title
- A Joint Econometric Approach for Modeling Crash Counts by Collision Type.
- Creator
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Bhowmik, Tanmoy, Eluru, Naveen, Abdel-Aty, Mohamed, Yasmin, Shamsunnahar, University of Central Florida
- Abstract / Description
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In recent years, there is growing recognition that common unobserved factors that influence crash frequency by one attribute level are also likely to influence crash frequency by other attribute levels. The most common approach employed to address the potential unobserved heterogeneity in safety literature is the development of multivariate crash frequency models. The current study proposes an alternative joint econometric framework to accommodate for the presence of unobserved heterogeneity ...
Show moreIn recent years, there is growing recognition that common unobserved factors that influence crash frequency by one attribute level are also likely to influence crash frequency by other attribute levels. The most common approach employed to address the potential unobserved heterogeneity in safety literature is the development of multivariate crash frequency models. The current study proposes an alternative joint econometric framework to accommodate for the presence of unobserved heterogeneity (-) referred to as joint negative binomial-multinomial logit fractional split (NB-MNLFS) model. Furthermore, the study undertakes a first of its kind comparison exercise between the most commonly used multivariate model (multivariate random parameter negative binomial model) and the proposed joint approach by generating an equivalent log-likelihood measure. The empirical analysis is based on the zonal level crash count data for different collision types from the state of Florida for the year 2015. The model results highlight the presence of common unobserved effects affecting the two components of the joint model as well as the presence of parameter heterogeneity. The equivalent log-likelihood and goodness of fit measures clearly highlight the superiority of the proposed joint model over the commonly used multivariate approach.
Show less - Date Issued
- 2018
- Identifier
- CFE0007392, ucf:52740
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007392
- Title
- Accommodating Exogenous Variable and Decision Rule Heterogeneity in Discrete Choice Models: Application to Bicyclist Route Choice.
- Creator
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Dey, Bibhas, Eluru, Naveen, Abdel-Aty, Mohamed, Anowar, Sabreena, University of Central Florida
- Abstract / Description
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The thesis contributes to our understanding of incorporating heterogeneity in discrete choice models with respect to exogenous variables and decision rules. Specifically, we evaluate latent segmentation based mixed models that allow for segmenting population based on decision rules while also incorporating unobserved heterogeneity within the segment level decision rule models. In our analysis, we choose to consider the random utility framework along with random regret minimization approach....
Show moreThe thesis contributes to our understanding of incorporating heterogeneity in discrete choice models with respect to exogenous variables and decision rules. Specifically, we evaluate latent segmentation based mixed models that allow for segmenting population based on decision rules while also incorporating unobserved heterogeneity within the segment level decision rule models. In our analysis, we choose to consider the random utility framework along with random regret minimization approach. Further, instead of assuming the number of segments (as 2), we conduct an exhaustive exploration with multiple segments across the two decision rules. Within each segment we also allow for unobserved heterogeneity. The model estimation is conducted using a stated preference data from 695 commuter cyclists compiled through a web-based survey. The probabilistic allocation of respondents to different segments indicates that female commuter cyclists are more utility oriented, however the majority of the commuter cyclist's choice pattern is consistent with regret minimization mechanism. Overall, cyclists' route choice decisions are influenced by roadway attributes, cycling infrastructure availability, pollution exposure, and travel time. The analysis approach also allows us to investigate time based trade-offs across cyclists of different classes. Interestingly, we observed that the trade-off values in regret and utility based segments for roadway attributes are similar in magnitude; but the values differ greatly for cycling infrastructure and exposure attributes, particularly for maximum exposure levels.
Show less - Date Issued
- 2018
- Identifier
- CFE0007398, ucf:52059
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007398
- Title
- Investigation of factors contributing to fog-related single vehicle crashes.
- Creator
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Zhu, Jiazheng, Abdel-Aty, Mohamed, Hasan, Samiul, Wu, Yina, University of Central Florida
- Abstract / Description
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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.
Show less - Date Issued
- 2018
- Identifier
- CFE0007118, ucf:51935
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007118
- Title
- Analyzing Destination Choices of Tourists and Residents from Location Based Social Media Data.
- Creator
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Hasnat, Md Mehedi, Hasan, Samiul, Abdel-Aty, Mohamed, Eluru, Naveen, University of Central Florida
- Abstract / Description
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Ubiquitous uses of social media platforms in smartphones have created an opportunity to gather digital traces of individual activities at a large scale. Traditional travel surveys fall short in collecting longitudinal travel behavior data for a large number of people in a cost effective way, especially for the transient population such as tourists. This study presents an innovating methodological framework, using machine learning and econometric approaches, to gather and analyze location...
Show moreUbiquitous uses of social media platforms in smartphones have created an opportunity to gather digital traces of individual activities at a large scale. Traditional travel surveys fall short in collecting longitudinal travel behavior data for a large number of people in a cost effective way, especially for the transient population such as tourists. This study presents an innovating methodological framework, using machine learning and econometric approaches, to gather and analyze location-based social media (LBSM) data to understand individual destination choices. First, using Twitter's search interface, we have collected Twitter posts of nearly 156,000 users for the state of Florida. We have adopted several filtering techniques to create a reliable sample from noisy Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The performance of the proposed classifier has been validated using manually labeled data and compared against the state-of-the-art classification methods. Second, using different clustering methods, we have analyzed the spatial distributions of destination choices of tourists and residents. The clusters from tourist destinations revealed most popular tourist spots including emerging tourist attractions in Florida. Third, to predict a tourist's next destination type, we have estimated a Conditional Random Field (CRF) model with reasonable accuracy. Fourth, to analyze resident destination choice behavior, this study proposes an extensive data merging operation among the collected Twitter data and different geographic database from state level data libraries. We have estimated a Panel Latent Segmentation Multinomial Logit (PLSMNL) model to find the characteristics affecting individual destination choices. The proposed PLSMNL model is found to better explain the effects of variables on destination choices compared to trip-specific Multinomial Logit Models. The findings of this study show the potential of LBSM data in future transportation and planning studies where collecting individual activity data is expensive.
Show less - Date Issued
- 2018
- Identifier
- CFE0007012, ucf:52028
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007012
- Title
- Explore Contributing Geometric Factors and Built-Environment on Bicycle Activity and Safety at Intersections.
- Creator
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Castro, Scott, Abdel-Aty, Mohamed, Cai, Qing, Eluru, Naveen, University of Central Florida
- Abstract / Description
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This study attempts to explore all factors associated with bicycle motor-vehicle crashes at intersections in order to improve bicycle safety and bicycle activity. Factors such as exposure (bicycle and vehicle volumes), existing facilities (bike lanes, sidewalks, shared-use paths), geometric design (# of lanes, speed limit, medians, legs, roadway conditions), and land-use were collected and evaluated using Poisson, Zero-Inflated Poisson, and Negative Binomial models in SAS 9.4 software....
Show moreThis study attempts to explore all factors associated with bicycle motor-vehicle crashes at intersections in order to improve bicycle safety and bicycle activity. Factors such as exposure (bicycle and vehicle volumes), existing facilities (bike lanes, sidewalks, shared-use paths), geometric design (# of lanes, speed limit, medians, legs, roadway conditions), and land-use were collected and evaluated using Poisson, Zero-Inflated Poisson, and Negative Binomial models in SAS 9.4 software. Increasing the bicycle travel mode can have positive lasting effects on personal health, the environment, and improve traffic conditions. Deterrents that keep users from riding bicycles more are the lack of facilities and most importantly, safety concerns. Florida has consistently been a national leader in bicyclist deaths, which made this area a great candidate to study. Vehicle and bicycle volumes for 159 intersections in Orlando, Florida were collected and compared with crash data that was obtained. All existing facilities, geometric design properties, and land-uses for each intersection were collected for analysis. The results confirmed that an increase of motor-vehicles and bicyclists would increase the risk of a crash at an intersection. The presence of a keyhole lane (bike lane in-between a through and exclusive right turn lane), was shown to be statistically significant, and although it still had a positive correlation with injury risk, it had a much lower risk of crashes than a typical bike lane at intersections. The presence of a far shared path (more than 4 feet from the edge of curb) was shown to be statistically significant in decreasing the risk of crashes between bicycles and motor-vehicles at intersections. Institutional, agricultural, residential, government, and school land uses had positive correlations and were statistically significant with increasing activity of bicyclists at intersections. This study is unique because it uses actual bicycle volume as an exposure to determine the effects of bicycle safety and activity at intersections and not many others have done this. It is important for transportation planners and designers to use this information to design better complete streets in the future.
Show less - Date Issued
- 2018
- Identifier
- CFE0007318, ucf:52134
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007318
- Title
- Developing Warrants for Designing Continuous Flow Intersection and Diverging Diamond Interchange.
- Creator
-
Almoshaogeh, Meshal, Radwan, Essam, Abdel-Aty, Mohamed, Abou-Senna, Hatem, University of Central Florida
- Abstract / Description
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The main goal of this dissertation is to have better understanding of design and operation of the Continuous Flow Intersection (CFI) and Diverging Diamond Interchange (DDI) - as well as numerous factors that affect signalized intersection and interchange performance due to increased left-turn demand. The dissertation attempts to assess the need and justification to redesign intersections and interchanges to improve their efficiency. And to that end, an extensive literature review of existing...
Show moreThe main goal of this dissertation is to have better understanding of design and operation of the Continuous Flow Intersection (CFI) and Diverging Diamond Interchange (DDI) - as well as numerous factors that affect signalized intersection and interchange performance due to increased left-turn demand. The dissertation attempts to assess the need and justification to redesign intersections and interchanges to improve their efficiency. And to that end, an extensive literature review of existing studies was done with the prime aim of perceiving the principles of these innovative designs and determining the methodology to-be-followed, in order to reach the study's core. Accordingly, several DDI and CFI locations were selected as candidate locations, where the designs have already been implemented and the required data - to model calibration and validation - was collected. The micro-simulation software (VISSIM 8.0) was used for simulation, calibration and validation of the existing conditions - through several steps - including signal optimization and driving behavior parameter sensitivity analysis. Subsequently, an experiment was conceived for each design, aiming at examining several factors that affect each design's efficiency. The experiment comprised 180 and 90 different CFI (&) DDI scenarios and their conventional designs, respectively. Two measures of effectiveness were identified for result analysis: the average delay and capacity. Result analyses were performed to detect switching thresholds (from conventional to innovative designs. In addition, performance comparison studies of the CFI and DDI with their conventional designs were performed. The results and findings will serve as guidelines for decision-makers as to when they should consider switching from conventional to innovative design. Finally, decision support systems were developed to speed up the search for the superior design, in comparison with others.
Show less - Date Issued
- 2017
- Identifier
- CFE0007276, ucf:52187
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007276
- Title
- Assessing Pedestrian Safety Conditions on Campus.
- Creator
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Morris, Morgan, Abdel-Aty, Mohamed, Hasan, Samiul, Wu, Yina, University of Central Florida
- Abstract / Description
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Pedestrian-related crashes are a significant safety issue in the United States and cause considerable amounts of deaths and economic cost. Pedestrian safety is an issue that must be uniquely evaluated in a college campus, where pedestrian volumes are dense. The objective of this research is to identify issues at specific locations around UCF and suggest solutions for improvement. To address this problem, a survey that identifies pedestrian safety issues and locations is distributed to UCF...
Show morePedestrian-related crashes are a significant safety issue in the United States and cause considerable amounts of deaths and economic cost. Pedestrian safety is an issue that must be uniquely evaluated in a college campus, where pedestrian volumes are dense. The objective of this research is to identify issues at specific locations around UCF and suggest solutions for improvement. To address this problem, a survey that identifies pedestrian safety issues and locations is distributed to UCF students and staff, and an evaluation of drivers reactions to pedestrian to vehicle (P2V) warning systems is studied through the use of a NADS MiniSim driving simulator. The survey asks participants to identify problem intersections around campus and other issues as pedestrians or bicyclists in the UCF area. Univariate probit models were created from the survey data to identify which factors contribute to pedestrian safety issues, based off the pedestrian's POV and the driver's POV. The models indicated that the more one is exposed to traffic via walking, biking, and driving to campus contributes to less safe experiences. The models also show that higher concerns with drivers not yielding, unsafety of crossing the intersections, and the number of locations to cross, indicate less safe pedestrian experiences from the point of view of pedestrians and drivers. A promising solution for pedestrian safety is Pedestrian to Vehicle (P2V) communication. This study simulates P2V connectivity using a NADS MiniSim Driving Simulator to study the effectiveness of the warning system on drivers. According to the results, the P2V warning system significantly reduced the number of crashes in the tested pre-crash scenarios by 88%. Particularly, the P2V warning system can help decrease the driver's reaction time as well as impact velocity if the crash were to occur.
Show less - Date Issued
- 2019
- Identifier
- CFE0007839, ucf:52818
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007839
- Title
- Evaluation and Augmentation of Traffic Data from Private Sector and Bluetooth Detection System on Arterials.
- Creator
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Gong, Yaobang, Abdel-Aty, Mohamed, Hasan, Samiul, Cai, Qing, University of Central Florida
- Abstract / Description
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Traffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE...
Show moreTraffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE, one of the private sector data vendors, and BDS of arterial corridors in Orlando, Florida. The results showed that the accuracy and reliability of BDS data are better than private sector data, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In addition, another preliminary study aiming at improving the quality of private sector data was also demonstrated. Information about bimodal traffic flow extracted by a finite mixture model from historical BDS is employed to augment real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the studied corridor except for segments highly influenced by traffic from or to the expressway ramps.
Show less - Date Issued
- 2018
- Identifier
- CFE0007330, ucf:52120
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007330
- Title
- Sustainable Transportation at the University of Central Florida: Evaluation of UCF Rideshare Program, Zimride.
- Creator
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Defrancisco, Joseph, Radwan, Ahmed, Abdel-Aty, Mohamed, Harb, Rami, University of Central Florida
- Abstract / Description
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As the second-largest university in the United States, UCF has experienced the largest enrollment in its history. A more densely populated campus has in turn caused increased traffic congestion. Despite increased parking permit fees and newly constructed parking garages, traveling and parking on campus is unpredictable. In effort to reduce congestion on campus, a rideshare program was implemented in Summer 2010. Several universities across the nation have successfully used carpooling as a...
Show moreAs the second-largest university in the United States, UCF has experienced the largest enrollment in its history. A more densely populated campus has in turn caused increased traffic congestion. Despite increased parking permit fees and newly constructed parking garages, traveling and parking on campus is unpredictable. In effort to reduce congestion on campus, a rideshare program was implemented in Summer 2010. Several universities across the nation have successfully used carpooling as a viable alternative mode to manage traffic and parking demand. This thesis evaluates the UCF rideshare program, Zimride, using stated- and revealed-preference surveys. Preliminary results indicate most students prefer to commute to campus using their own car and without incentives there is no reason to change mode choice, regardless of associated costs(-)e.g. decal cost, parking time and frustration. Despite 70% of respondents considering themselves environmentally friendly and over 80% are aware of savings in money and productive by using alternative modes, 70% still use their car to commute to campus. Using Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM), the observed variables were organized into three (3) latent variables based on the correlation among them. The SEM results of the revealed-preference survey indicate current travel behavior significantly influences attitudes towards carpooling and demographics have a significant effect on current travel behavior. It was also found that demographics influences attitudes towards carpooling at a non statistically significant level.
Show less - Date Issued
- 2012
- Identifier
- CFE0004226, ucf:48996
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004226
- Title
- Evaluation and Modeling of the Safety of Open Road Tolling System.
- Creator
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Abuzwidah, Muamer, Abdel-Aty, Mohamed, Radwan, Ahmed, Uddin, Nizam, University of Central Florida
- Abstract / Description
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The goal of this thesis is to examine the traffic safety impact of upgrading Toll Plazas (TP) to Open Road Tolling (ORT). The ORT could enhance safety but could also pose some traffic safety concerns at Toll plazas. Crashes from eight years were investigated by evaluating the crash data before and after the implementation of the ORT.The study was conducted by using two approaches: 1) a simple before and after study and with a comparison group; 2) a modeling effort to help understand the...
Show moreThe goal of this thesis is to examine the traffic safety impact of upgrading Toll Plazas (TP) to Open Road Tolling (ORT). The ORT could enhance safety but could also pose some traffic safety concerns at Toll plazas. Crashes from eight years were investigated by evaluating the crash data before and after the implementation of the ORT.The study was conducted by using two approaches: 1) a simple before and after study and with a comparison group; 2) a modeling effort to help understand the relationship between the crash frequency and several important factors and circumstances such as injury severity, collision types, average daily traffic (ADT) and Toll plaza characteristics. The study investigated 11 Toll plazas on State Roads 408, 417, 528 and 429 that have been changed to the ORT design. Several maps showing the Toll plazas and identifying the relevant crash locations were generated. Negative Binomial (NB), Log Linear model and two-way contingency table were examined. Two log-linear models with three variables in each model with all possible two-way interactions were developed. Categorical data analysis of the 2009 and 2010 crash dataset was performed. In order to compare the differences in response between the crash frequency and a particular crash-related variable, odds ratios were computed. The effects of crash frequency and crash-related factors were examined, and interactions among them were considered. The results indicated significant relationships between the crash frequency and ADT, crash type and driver age.It is worth mentioning that the expressway network understudy was continuously experiencing constructions throughout the study period. There is indication that ORT reduced the total crash number; also there is indication of changing the crash types and locations; and the majority of crashes occurred at the diverging and merging areas and resulted in more severe crashes. More data may be needed to confirm these results especially after all constructions and upgrades are made.The Implementation of open road tolling, the locations of Toll plazas, Automatic Vehicle Identification (AVI) subscription rate, traffic demand, and plaza geometry all may have a high influence on traffic safety concerns at Toll plazas, as concluded from the negative Binomial Model's results. The changing of sign locations, reducing the speed limit, installing variable message signs, configuring plazas properly, and other considerations may be the solution to overcome the potential safety problems in the vicinity of Toll plazas.The change of design to ORT was proven to be an excellent solution to several traffic operation problems, including reducing congestion and improving traffic flow and capacity at Toll plazas. However, addressing safety concerns at Toll plazas should take priority.
Show less - Date Issued
- 2011
- Identifier
- CFE0004466, ucf:49330
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004466
- Title
- LEVEL-OF-SERVICE AND TRAFFIC SAFETY RELATIONSHIP: AN EXPLORATORY ANALYSIS OF SIGNALIZED INTERSECTIONS AND MULTILANE HIGH-SPEED ARTERIAL CORRIDORS.
- Creator
-
Almonte-Valdivia, Ana, Abdel-Aty, Mohamed, University of Central Florida
- Abstract / Description
-
Since its inception in 1965, the Level-of-Service (LOS) has proved to be an important and practical "quality of service" indicator for transportation facilities around the world, widely used in the transportation and planning fields. The LOS rates these facilities' traffic operating conditions through the following delay-based indicators (ordered from best to worst conditions): A, B, C, D, E and F. This LOS rating has its foundation on quantifiable measures of effectiveness (MOEs) and on...
Show moreSince its inception in 1965, the Level-of-Service (LOS) has proved to be an important and practical "quality of service" indicator for transportation facilities around the world, widely used in the transportation and planning fields. The LOS rates these facilities' traffic operating conditions through the following delay-based indicators (ordered from best to worst conditions): A, B, C, D, E and F. This LOS rating has its foundation on quantifiable measures of effectiveness (MOEs) and on road users' perceptions; altogether, these measures define a LOS based on acceptable traffic operating conditions for the road user, implying that traffic safety is inherent to this definition. However, since 1994 safety has been excluded from the LOS definition since it cannot be quantified nor explicitly defined. The latter has been the motivation for research based on the LOS-Safety relationship, conducted at the University of Central Florida (UCF). Using data from two of the most studied transportation facility types within the field of traffic safety, signalized intersections and multilane high-speed arterial corridors, the research conducted has the following main objectives: to incorporate the LOS as a parameter in several traffic safety models, to extend the methodology adopted in previous studies to the subject matter, and to provide a platform for future transportation-related research on the LOS-Safety relationship. A meticulous data collection and preparation process was performed for the two LOS-Safety studies comprising this research. Apart from signalized intersections' and multilane-high speed arterial corridors' data, the other required types of information corresponded to crashes and road features, both obtained from FDOT's respective databases. In addition, the Highway Capacity Software (HCS) and the ArcGIS software package were extensively used for the data preparation. The result was a representative and robust dataset for each LOS-Safety study, to be later tested and analyzed with appropriate statistical methods. Regarding the LOS-Safety study for signalized intersections, two statistical techniques were used. The Generalized Estimating Equations (GEEs), the first technique, was used for the analyses considering all periods of a regular weekday (i.e. Monday through Friday): Early Morning, A.M. Peak, Midday, P.M. Peak and Late Evening; the second technique considered was the Negative Binomial, which was used for performing an individual analysis per period of the day. On the other hand, the LOS-Safety study for multilane high-speed arterial corridors made exclusive use of the Negative Binomial technique. An appropriate variable selection process was required for the respective model building and calibration procedures; the resulting models were built upon the six following response variables: total crashes, severe crashes, as well as rear-end, sideswipe, head-on and angle plus left-turn crashes. The final results proved to be meaningful for the understanding of traffic congestion effects on road safety, and on how they could be useful within the transportation planning scope. Overall, it was found that the risk for crash occurrence at signalized intersections and multilane high-speed arterial corridors is quite high between stable and unacceptable operating conditions; it was also found that this risk increases as it becomes later in the day. Among the significant factors within the signalized intersection-related models were LOS for the intersection as a whole, cycle length, lighting conditions, land use, traffic volume (major and minor roads), left-turn traffic volume (major road only), posted speed limit (major and minor roads), total number of through lanes (major and minor roads), overall total and total number of left-turn lanes (major road only), as well as county and period of the day (dummy variables). For multilane-high speed arterial corridors, the final models included LOS for the road section, average daily traffic (ADT), total number of through lanes in a single direction, total length of the road section, pavement surface type, as well as median and inside shoulder widths. A summary of the overall results per study, model implications and each LOS indicator is presented. Some of the final recommendations are to develop models for other crash types, to perform a LOS-Safety analysis at the approach-level for signalized intersections, as well as one that incorporates intersections within the arterial corridors' framework.
Show less - Date Issued
- 2009
- Identifier
- CFE0002615, ucf:48285
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002615
