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Title: Reliability and Robustness Enhancement of Cooperative Vehicular Systems: A Bayesian Machine Learning Perspective.
Creator: Nourkhiz Mahjoub, Hossein, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Yuksel, Murat, Atia, George, Eluru, Naveen, University of Central Florida
Abstract / Description: Autonomous vehicles are expected to greatly transform the transportation domain in the near future. Some even envision that the human drivers may be fully replaced by automated systems. It is plausible to assume that at least a significant part of the driving task will be done by automated systems in not a distant future. Although we are observing a rapid advance towards this goal, which gradually pushes the traditional human-based driving toward more advanced autonomy levels, the full...
Show moreAutonomous vehicles are expected to greatly transform the transportation domain in the near future. Some even envision that the human drivers may be fully replaced by automated systems. It is plausible to assume that at least a significant part of the driving task will be done by automated systems in not a distant future. Although we are observing a rapid advance towards this goal, which gradually pushes the traditional human-based driving toward more advanced autonomy levels, the full autonomy concept still has a long way before being completely fulfilled and realized due to numerous technical and societal challenges. During this long transition phase, blended driving scenarios, composed of agents with different levels of autonomy, seems to be inevitable. Therefore, it is critical to design appropriate driving systems with different levels of intelligence in order to benefit all participants. Vehicular safety systems and their more advanced successors, i.e., Cooperative Vehicular Systems (CVS), have originated from this perspective. These systems aim to enhance the overall quality and performance of the current driving situation by incorporating the most advanced available technologies, ranging from on-board sensors such as radars, LiDARs, and cameras to other promising solutions e.g. Vehicle-to-Everything (V2X) communications. However, it is still challenging to attain the ideal anticipated benefits out of the cooperative vehicular systems, due to the inherent issues and challenges of their different components, such as sensors' failures in severe weather conditions or the poor performance of V2X technologies under dense communication channel loads. In this research we aim to address some of these challenges from a Bayesian Machine- Learning perspective, by proposing several novel ideas and solutions which facilitate the realization of more robust, reliable, and agile cooperative vehicular systems. More precisely, we have a two-fold contribution here. In one aspect, we have investigated the notion of Model-Based Communications (MBC) and demonstrated its effectiveness for V2X communication performance enhancement. This improvement is achieved due to the more intelligent communication strategy of MBC in comparison with the current state-of-the-art V2X technologies. Essentially, MBC proposes a conceptual change in the nature of the disseminated and shared information over the communication channel compared to what is being disseminated in current technologies. In the MBC framework, instead of sharing the raw dynamic information among the network agents, each agent shares the parameters of a stochastic forecasting model which represents its current and future behavior and updates these parameters as needed. This model sharing strategy enables the receivers to precisely predict the future behaviors of the transmitter even when the update frequency is very low. On the other hand, we have also proposed receiver-side solutions in order to enhance the CVS performance and reliability and mitigate the issues caused by imperfect communication and detection processes. The core concept for these solutions is incorporating other informative elements in the system to compensate for the lack of information which is lost during the imperfect communication or detection phases. For proof of concept, we have designed an adaptive FCW framework which considers the driver's feedbacks to the CVS system. This adaptive framework mitigates the negative impact of imperfectly received or detected information on system performance, using the inherent information of these feedbacks and responses. The effectiveness and superiority of this adaptive framework over traditional design has been demonstrated in this research.
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Date Issued: 2019
Identifier: CFE0007845, ucf:52807
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007845

Title: Fusing Freight Analysis Framework and Transearch Data: An Econometric Data Fusion Approach.
Creator: Momtaz, Salah Uddin, Eluru, Naveen, Abdel-Aty, Mohamed, Anowar, Sabreena, Zheng, Qipeng, University of Central Florida
Abstract / Description: A major hurdle in freight demand modeling has always been the lack of adequate data on freight movements for different industry sectors for planning applications. Freight Analysis Framework (FAF), and Transearch (TS) databases contain annualized commodity flow data. The primary motivation for our study is the development of a fused database from FAF and TS to realize transportation network flows at a fine spatial resolution (county-level) while accommodating for production and consumption...
Show moreA major hurdle in freight demand modeling has always been the lack of adequate data on freight movements for different industry sectors for planning applications. Freight Analysis Framework (FAF), and Transearch (TS) databases contain annualized commodity flow data. The primary motivation for our study is the development of a fused database from FAF and TS to realize transportation network flows at a fine spatial resolution (county-level) while accommodating for production and consumption behavioral trends (provided by TS). Towards this end, we formulate and estimate a joint econometric model framework grounded in maximum likelihood approach to estimate county-level commodity flows. The algorithm is implemented for the commodity flow information from 2012 FAF and 2011 TS databases to generate transportation network flows for 67 counties in Florida. The data fusion process considers several exogenous variables including origin-destination indicator variables, socio-demographic and socio-economic indicators, and transportation infrastructure indicators. Subsequently, the algorithm is implemented to develop freight flows for the Florida region considering inflows and outflows across the US and neighboring countries. The base year models developed are employed to predict future year data for years 2015 through 2040 in 5-year increments at the same spatial level. Furthermore, we disaggregate the county level flows obtained from algorithm to a finer resolution - statewide transportation analysis zone (SWTAZ) defined by the FDOT. The disaggregation process allocates truck-based commodity flows from a 79-zone system to an 8835-zone system. A two-stage factor multiplication method is proposed to disaggregate the county flow to SWTAZ flow. The factors are estimated both at the origin and destination level using a random utility factional split model approach. Eventually, we conducted a sensitivity analysis of the parameterization by evaluating the model structure for different numbers of intermediate stops in a route and/or the number of available routes for the origin-destinations.
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Date Issued: 2018
Identifier: CFE0007763, ucf:52384
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007763

Title: Arterial-level real-time safety evaluation in the context of proactive traffic management.
Creator: Yuan, Jinghui, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Wang, Liqiang, University of Central Florida
Abstract / Description: In the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and...
Show moreIn the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and freeways in terms of traffic flow characteristics, data availability, and even crash mechanism. Therefore, this study aims to deeply evaluate the real-time crash risk on arterials from multiple aspects by integrating all kinds of available data sources. First, Bayesian conditional logistic models (BCL) were developed to examine the relationship between crash occurrence on arterial segments and real-time traffic and signal timing characteristics by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted from four urban arterials in Central Florida. Second, real-time intersection-approach-level crash risk was investigated by considering the effects of real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Orange County. Third, a deep learning algorithm for real-time crash risk prediction at signalized intersections was proposed based on Long Short-Term Memory (LSTM) and Synthetic Minority Over-Sampling Technique (SMOTE). Moreover, in-depth cycle-level real-time crash risk at signalized intersections was explored based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). All the possible real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather conditions. Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management.
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Date Issued: 2019
Identifier: CFE0007743, ucf:52398
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007743

Title: Understanding How, Where and How much Freight Flows Using 2012 Commodity Flow Survey Data.
Creator: Keya, Nowreen, Eluru, Naveen, Abdel-Aty, Mohamed, Anowar, Sabreena, Uddin, Nizam, University of Central Florida
Abstract / Description: In recent years, with increased economic globalization, growing e-commerce and internet based shopping, freight movement patterns are undergoing a transformative change. The shipment size distribution is moving towards a higher share of smaller size shipments affecting transportation mode and vehicle type requirements. In addition, freight transportation mode is closely affected by the destination location (and its attributes). In our dissertation, we contribute to freight research by...
Show moreIn recent years, with increased economic globalization, growing e-commerce and internet based shopping, freight movement patterns are undergoing a transformative change. The shipment size distribution is moving towards a higher share of smaller size shipments affecting transportation mode and vehicle type requirements. In addition, freight transportation mode is closely affected by the destination location (and its attributes). In our dissertation, we contribute to freight research by developing a comprehensive framework to examine the how, where and how much freight flows in US. Specifically, we study the following dimensions of freight flow: (1) transportation mode, (2) mode and shipment weight choice and (3) mode and destination choice. For analyzing mode choice, an advanced discrete freight mode choice model- a hybrid utility-regret based model system has been estimated while accommodating for shipper level unobserved heterogeneity. To demonstrate the applicability of the proposed model system, detailed policy analyses examining the implementation of vehicle fleet automation and rerouting of freight movements away from a region were considered. While shipment weight could be considered as an explanatory variable in modeling mode choice (or vice-versa), it is more likely that the decision of mode and shipment choice is a simultaneous process. This joint decision is investigated both simultaneously employing a closed form copula structure and sequentially employing latent segmentation based sequence model. For destination choice, we investigated the connection between shipping mode and destination choice of shipment in a latent segmentation based sequential form. The analysis for the dissertation is conducted using 2012 Commodity Flow Survey (CFS) data.
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Date Issued: 2018
Identifier: CFE0007574, ucf:52569
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007574

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: 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: Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
Creator: Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description: This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
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Date Issued: 2018
Identifier: CFE0007358, ucf:52103
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007358

Title: Econometric Modeling Analysis of Public Transit Ridership: Application for Orlando Region.
Creator: Rahman, Moshiur, Eluru, Naveen, Abdel-Aty, Mohamed, Yasmin, Shamsunnahar, Uddin, Nizam, University of Central Florida
Abstract / Description: Policy makers are considering several alternatives to counter the negative externalities of personal vehicle dependence. Towards this end, public transit investments are critical in growing urban regions such as Orlando, Florida. Transit system managers and planners mostly rely on statistical models to identify the factors that affect ridership as well as quantifying the magnitude of the impact on the society. These models provide vital feedback to agencies on the benefits of public transit...
Show morePolicy makers are considering several alternatives to counter the negative externalities of personal vehicle dependence. Towards this end, public transit investments are critical in growing urban regions such as Orlando, Florida. Transit system managers and planners mostly rely on statistical models to identify the factors that affect ridership as well as quantifying the magnitude of the impact on the society. These models provide vital feedback to agencies on the benefits of public transit investments which in turn act as lessons to improve the investment process. We contribute to public transit literature by addressing several methodological challenges for transit ridership modeling. Frist, we examine the impact of new transit investments (such as an addition of commuter rail to an urban region) on existing transit infrastructure (such as the traditional bus service already present in the urban region). The process of evaluating the impact of new investments on existing public transit requires a comprehensive analysis of the before and after measures of public transit usage in the region. Second, we accommodate for the presence of common unobserved factors associated with spatial factors by developing a spatial panel model using stop level public transit boarding and alighting data. Third, we contribute to literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system (SunRail). The model system developed will allow us to predict ridership for existing stations in the future as well as potential ridership for future expansion sites. Fourth, we accommodate for potential endogeneity between bus headway and ridership by proposing a simultaneous model system of headway and ridership. Finally, a cost benefit analysis exercise is conducted for examining the impact of Sunrail on the region.
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Date Issued: 2018
Identifier: CFE0007583, ucf:52577
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007583

Title: A New Methodology for Evaluating the Effectiveness of Bus Rapid Transit Strategies.
Creator: Alomari, Ahmad, Al-Deek, Haitham, Eluru, Naveen, Tatari, Omer, Maboudou, Edgard, University of Central Florida
Abstract / Description: Over the last few years, public transportation has become more desirable as capacity of existing roadways failed to keep up with rapidly increasing traffic demand. Buses are one of the most common modes of public transportation with low impact on network capacity, especially in small and congested urban areas. However, the use of regularly scheduled buses as the main public transport mode can become useless with the presence of traffic congestion and dense construction areas. In cases like...
Show moreOver the last few years, public transportation has become more desirable as capacity of existing roadways failed to keep up with rapidly increasing traffic demand. Buses are one of the most common modes of public transportation with low impact on network capacity, especially in small and congested urban areas. However, the use of regularly scheduled buses as the main public transport mode can become useless with the presence of traffic congestion and dense construction areas. In cases like these, innovative solutions, such as bus rapid transit (BRT), can provide an increased level of service without having to resort to other, more expensive modes, such as light rail transit (LRT) and metro systems (subways). Transit signal priority (TSP), which provides priority to approaching buses at signalized intersections by extending the green or truncating the red, can also increase the performance of the bus service.Understanding the combined impact of TSP and BRT on network traffic operations can be complex. Although TSP has been implemented worldwide, none of the previous studies have examined in depth the effects of using conditional and unconditional TSP strategies with a BRT system. The objective of this research is to evaluate the effectiveness of BRT without TSP, then with conditional or unconditional TSP strategies. The micro-simulation software VISSIM was used to compare different TSP and BRT scenarios. These simulation scenarios include the base scenario (before implementation of the TSP and BRT systems), Unconditional TSP (TSP activates for all buses), Conditional TSP 3 minutes behind (TSP only activates for buses that are 3 minutes or more behind schedule), Conditional TSP 5 minutes behind (only activates for buses 5 minutes or more behind schedule), BRT with no TSP, BRT with Unconditional TSP, BRT with Conditional TSP 3 minutes behind, and BRT with Conditional TSP 5 minutes behind.The VISSIM simulation model was developed, calibrated and validated using a variety of data that was collected in the field. These data included geometric data, (number of lanes, intersection geometries, etc.); traffic data (average daily traffic volumes at major intersections, turning movement percentages at intersections, heavy vehicle percentages, bus passenger data, etc.); and traffic control data (signal types, timings and phasings, split history, etc.). Using this field data ensured the simulation model was sufficient for modeling the test corridor. From this model, the main performance parameters (for all vehicles and for buses only) for through movements in both directions (eastbound and westbound) along the corridor were analyzed for the various BRT/TSP scenarios. These parameters included average travel times, average speed profiles, average delays, and average number of stops. As part of a holistic approach, the effects of BRT and TSP on crossing street delay were also evaluated. Simulation results showed that TSP and BRT scenarios were effective in reducing travel times (up to 26 %) and delays (up to 64%), as well as increasing the speed (up to 47%), compared to the base scenario. The most effective scenarios were achieved by combining BRT and TSP. Results also showed that BRT with Conditional TSP 3 minutes behind significantly improved travel times (17 (-) 26%), average speed (30 (-) 39%), and average total delay per vehicle (11 (-) 32%) for the main corridor through movements compared with the base scenario, with only minor effects on crossing street delays. BRT with Unconditional TSP resulted in significant crossing street delays, especially at major intersections with high traffic demand, which indicates that this scenario is impractical for implementation in the corridor. Additionally, BRT with Conditional TSP 3 minutes behind had better travel time savings than BRT with Conditional TSP 5 minutes behind for both travel directions, making this the most beneficial scenario.This research provided an innovative approach by using nested sets (hierarchical design) of TSP and BRT combination scenarios. Coupled with microscopic simulation, nested sets in the hierarchical design are used to evaluate the effectiveness of BRT without TSP, then with conditional or unconditional TSP strategies. The robust methodology developed in this research can be applied to any corridor to understand the combined TSP and BRT effects on traffic performance. Presenting the results in an organized fashion like this can be helpful in decision making. This research investigated the effects of BRT along I-Drive corridor (before and after conditions) at the intersection level. Intersection analysis demonstrated based on real life data for the before and after the construction of BRT using the Highway Capacity SoftwareTM (HCS2010) that was built based on the Highway Capacity Manual (HCM 2010) procedures for urban streets and signalized intersections. The performance measure used in this analysis is the level of service (LOS) criteria which depends on the control delay (seconds per vehicle) for each approach and for the entire intersection. The results show that implementing BRT did not change the LOS. However, the control delay has improved at most of the intersections' approaches. The majority of intersections operated with an overall LOS "C" or better except for Kirkman Road intersection (T2) with LOS "E" because it has the highest traffic volumes before and after BRT construction.This research also used regression analysis to observe the effect of the tested scenarios analyzed in VISSIM software compared to the No TSP (-) No BRT base model for all vehicles and for buses only. The developed regression model can predict the effect of each scenario on each studied Measures of Performance (MOE). Minitab statistical software was used to conduct this multiple regression analysis. The developed models with real life data input are able to predict how proposed enhancements change the studied MOEs. The BRT models presented in this research can be used for further sensitivity analysis on a larger regional network in the upcoming regional expansion of the transit system in Central Florida. Since this research demonstrated the operational functionality and effectiveness of BRT and TSP systems in this critical corridor in Central Florida, these systems' accomplishments can be expanded throughout the state of Florida to provide greater benefits to transit passengers. Furthermore, to demonstrate the methodology developed in this research, it is applied to a test corridor along International Drive (I-Drive) in Orlando, Florida. This corridor is key for regional economic prosperity of Central Florida and the novel approach developed in this dissertation can be expanded to other transit systems.
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Date Issued: 2015
Identifier: CFE0005918, ucf:50848
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005918

Title: Pedestrian Safety Analysis through Effective Exposure Measures and Examination of Injury Severity.
Creator: Shah, Md Imran, Abdel-Aty, Mohamed, Eluru, Naveen, Lee, JaeYoung, University of Central Florida
Abstract / Description: Pedestrians are considered the most vulnerable road users who are directly exposed to traffic crashes. In 2014, there were 4,884 pedestrians killed and 65,000 injured in the United States. Pedestrian safety is a growing concern in the development of sustainable transportation system. But often it is found that safety analysis suffers from lack of accurate pedestrian trip information. In such cases, determining effective exposure measures is the most appropriate safety analysis approach. Also...
Show morePedestrians are considered the most vulnerable road users who are directly exposed to traffic crashes. In 2014, there were 4,884 pedestrians killed and 65,000 injured in the United States. Pedestrian safety is a growing concern in the development of sustainable transportation system. But often it is found that safety analysis suffers from lack of accurate pedestrian trip information. In such cases, determining effective exposure measures is the most appropriate safety analysis approach. Also it is very important to clearly understand the relationship between pedestrian injury severity and the factors contributing to higher injury severity. Accurate safety analysis can play a vital role in the development of appropriate safety countermeasures and policies for pedestrians.Since pedestrian volume data is the most important information in safety analysis but rarely available, the first part of the study aims at identifying surrogate measures for pedestrian exposure at intersections. A two-step process is implemented: the first step is the development of Tobit and Generalized Linear Models for predicting pedestrian trips (i.e., exposure models). In the second step, Negative Binomial and Zero Inflated Negative Binomial crash models were developed using the predicted pedestrian trips. The results indicate that among various exposure models the Tobit model performs the best in describing pedestrian exposure. The identified exposure relevant factors are the presence of schools, car-ownership, pavement condition, sidewalk width, bus ridership, intersection control type and presence of sidewalk barrier. The t-test and Wilcoxon signed-rank test results show that there is no significant difference between the observed and the predicted pedestrian trips. The process implemented can help in estimating reliable safety performance functions even when pedestrian trip data is unavailable.The second part of the study focuses on analyzing pedestrian injury severity for the nine counties in Central Florida. The study region covers the Orlando area which has the second-worst pedestrian death rate in the country. Since the dependent variable 'Injury' is ordinal, an 'Ordered Logit' model was developed to identify the factors of pedestrian injury severity. The explanatory variables can be classified as pedestrian/driver characteristics (e.g., age, gender, etc.), roadway traffic and geometric conditions (e.g.: shoulder presence, roadway speed etc.) and crash environment (e.g., light, road surface, work zone, etc.) characteristics. The results show that drug/alcohol involvement, pedestrians in a hurry, roadway speed limit 40 mph or more, dark condition (lighted and unlighted) and presence of elder pedestrians are the primary contributing factors of severe pedestrian crashes in Central Florida. Crashes within the presence of intersections and local roads result in lower injury severity. The area under the ROC (Receiver Operating Characteristic) curve has a value of 0.75 that indicates the model performs reasonably well. Finally the study validated the model using k-fold cross validation method. The results could be useful for transportation officials for further pedestrian safety analysis and taking the appropriate safety interventions.Walking is cost-effective, environmentally friendly and possesses significant health benefits. In order to get these benefits from walking, the most important task is to ensure safer roads for pedestrians.
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Date Issued: 2017
Identifier: CFE0006656, ucf:51224
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006656

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: Field Evaluation of Insync Adaptive Traffic Signal Control System in Multiple Environments Using Multiple Approaches.
Creator: Shafik, Md Shafikul Islam, Radwan, Essam, Abou-Senna, Hatem, Eluru, Naveen, University of Central Florida
Abstract / Description: Since the beginning of signalization of intersections, the management of traffic congestion is one of most critical challenges specifically for the city and urbanized area. Almost all the municipal agencies struggle to manage the perplexities associated with traffic congestion or signal control. The Adaptive Traffic Control System (ATCS), an advanced and major technological component of the Intelligent Transportation Systems (ITS) is considered the most dynamic and real-time traffic...
Show moreSince the beginning of signalization of intersections, the management of traffic congestion is one of most critical challenges specifically for the city and urbanized area. Almost all the municipal agencies struggle to manage the perplexities associated with traffic congestion or signal control. The Adaptive Traffic Control System (ATCS), an advanced and major technological component of the Intelligent Transportation Systems (ITS) is considered the most dynamic and real-time traffic management technology and has potential to effectively manage rapidly varying traffic flow relative to the current state-of-the-art traffic management practices.InSync ATCS is deployed in multiple states throughout the US and expanding on a large scale. Although there had been several 'Measure of Effectiveness' studies performed previously, the performance of InSync is not unquestionable especially because the previous studies failed to subject for multiple environments, approaches, and variables. Most studies are accomplished through a single approach using simple/na(&)#239;ve before-after method without any control group/parameter. They also lacked ample statistical analysis, historical, maturation and regression artifacts. An attempt to evaluate the InSync ATCS in varying conditions through multiple approaches was undertaken for the SR-434 and Lake Underhill corridor in Orange County, Florida. A before-after study with an adjacent corridor as control group and volume as a control parameter has been performed where data of multiple variables were collected by three distinct procedures. The average/floating-car method was utilized as a rudimentary data collection process and 'BlueMac' and 'InSync' system database was considered as secondary data sources. Data collected for three times a day for weekdays and weekends before and after the InSync ATCS was deployed.Results show variation in both performance and scale. It proved ineffective in some of the cases, especially for the left turns, total intersection queue/delay and when the intersection volumes approach capacity. The results are verified through appropriate statistical analysis.
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Date Issued: 2017
Identifier: CFE0006915, ucf:51687
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006915

Title: Evaluating Wrong-Way Driving for Florida Interstates and Toll Road Facilities: A Risk-Based Investigation, and Countermeasure Development.
Creator: Rogers, John, Al-Deek, Haitham, Tatari, Omer, Eluru, Naveen, Uddin, Nizam, University of Central Florida
Abstract / Description: The focus of this dissertation was to examine wrong-way driving (WWD) events on Florida toll roads and Interstates. The universe of WWD data contains many sources of WWD events or incidents. Most of the previous research focused only on WWD crashes without considering other data such as WWD citations and 911 calls related to WWD incidents. While WWD citations and 911 calls data is abundant, this data has been largely overlooked in other studies. This dissertation provides a novel and holistic...
Show moreThe focus of this dissertation was to examine wrong-way driving (WWD) events on Florida toll roads and Interstates. The universe of WWD data contains many sources of WWD events or incidents. Most of the previous research focused only on WWD crashes without considering other data such as WWD citations and 911 calls related to WWD incidents. While WWD citations and 911 calls data is abundant, this data has been largely overlooked in other studies. This dissertation provides a novel and holistic approach for evaluating WWD risk, which considers other risk factors such as WWD citations and 911 calls in addition to WWD crashes.WWD crashes are rare because they are less than 3% of all crashes, which makes them difficult to predict and analyze. WWD is very dangerous especially on high-speed limited access facilities. A right way driver on the mainline has very little time to take an action and avoid a wrong-way vehicle since the combined approach speed rates of both vehicles is very high. There is an average of 300 to 400 fatalities every year in the United States due to WWD crashes. There were 386 fatalities in Florida due to WWD crashes from 2007(-)2011; this ranked Florida third in terms of total WWD fatalities.There are many causes for WWD. The majority of WWD crashes occur during late night hours, and these crashes can be attributed to intoxicated drivers, confused/elderly drivers, and suicidal drivers. However, these are not all of the causes of WWD. In order to understand WWD, it is important to look beyond crash events. This research focused on two major toll road networks in Florida, which were the Central Florida Expressway (CFX) and the Florida Turnpike Enterprise (FTE). Overall, WWD crashes on the FTE system accounted for around 0.45% of all crashes, but accounted for 1.5% of fatal crashes. WWD on FTE shows that 15.2% of these crashes are usually fatal compared to only 2% of all WWD rural freeway crashes are fatal and only 0.7% of urban freeway crashes are fatal. In the citation data, not all wrong way drivers were issued citations. 15% of the WWD citations in the FTE dataset resulted in a crash. While analyzing the citation events, it has been found that they commonly do not result in crashes. However, the mere fact that a driver gets a wrong way driving citation, because he or she failed to correct his driving action before a police officer arrives at the scene, is by itself a risky behavior. The WWD Traffic Management Center (TMC) SunGuide data was explored in depth for the FTE system. 55% of the SunGuide events were never found, 11% were pulled over by Law Enforcement Officers (LEO), and 8% of the events resulted in crashes. 19% of the events were false calls. In 3% of the events, drivers corrected their WW action without an incident or crash. Understanding the relationships between non-crash WWD events (WWD citations and 911 calls) and WWD crash events is essential. The interaction between crash events and non-crash events was explored using six different models developed in this dissertation. Weighted crash risk values, which use all three types of WWD events (crashes, citations, and 911 calls), were created using the developed models from this research and were applied to rank locations in priority for enhanced WWD countermeasures. Model 1, a generalized linear model referred to as GLM 1, was developed from Florida statewide WWD data on limited access routes. GLM 1 was built using a Poisson's function. Non-crash events (citations and 911 events) were modeled to predict WWD crash events while leveraging the statewide count data that was broken down by hour of the day. The results of GLM 1 showed that Broward and Miami-Dade Counties are some of the hottest counties in Florida for WWD, and SR 821 located in these two counties is one of the hottest routes for WWD in Florida. SR 821 ranked highest in terms of WWD crash risk using a statewide developed model in this dissertation. Model 2, which was another generalized linear model (referred to as GLM 2), used an additional time variable to square the hour difference from noon. The form of GLM 2 was similar to GLM 1, but the results of GLM 1 were a little stronger than GLM 2. Another model using Artificial Neural Network (ANN) was developed and compared to GLM 2. It was found that ANN provided a stronger fit of WWD crash predictions compared to GLM 1 and GLM 2. However, when the ANN was used with other non-crash events to produce a crash prediction values outside of its original data set, the ANN model was not very useful for this application because of ANN's nature to overfit its original data set.Model 3, noted as GLM 3, used yearly non-crash data in South Florida to predict an entire route WWD crashes annually. Model 4, also noted as GLM 4, was one of the most useful models created from this body of work and used the same South Florida network as GLM 3. Using non-crash events and route characteristics such as geometric design configurations and traffic volumes at interchanges within the segment, GLM 4 predicts WWD crashes within 7- interchange route segments over a 4 year time period. GLM 4 used a method to aggregate the 7-interchange route segments, which leveraged more data points by overlapping segments to provide a larger data set of WWD crashes. The predicted WWD crashes from GLM 4 were added to the actual WWD crashes to produce a 7-interchange crash risk value. Using this WWD risk assessment method allows for the inclusion of more than just WWD crashes when evaluating and prioritizing sites for implementation of WWD countermeasures. In addition, using segments/corridors to target countermeasures is a smart approach for combating the WWD problem because in many instances, it is difficult to know where the WWD event first started or got initiated, and some of the WW drivers can travel considerable distances before they are either apprehended by law enforcement or end up crashing with the oncoming traffic. Similar to GLM 4, GLM 5 was another route segment model developed using WWD data collected for the Central Florida region's limited access network. The developed GLM 5 used 5-interchange segments to predict crash risk. Both GLM 4 and GLM 5 models were microscopic in the sense that they prioritize candidate interchanges for implementation of WWD countermeasures.In order to go beyond the minimal standards for combating WW, Florida toll road agencies are testing enhanced/flashing (")Wrong Way(") signs at exit ramps. These flashing devices add more emphasis to the existing (")Wrong Way(") signs (and or other traffic control devices) at the exit ramps. The CFX's application of the Rectangular Rapid Flashing Beacon (RRFB) for (")Wrong Way(") signs is an entirely new concept that was applied in Central Florida for the first time. The FTE's application of the MUTCD approved Blinker Sign for (")Wrong Way(") has been used in other states such as Texas. These countermeasures were examined and briefly studied during their test pilot phases. Partial results are documented in this dissertation but continuous observations and data collection at the pilot test sites and potential expansions of these sites in South and Central Florida (and other parts of the state) are needed for complete and comprehensive evaluation of the effectiveness of these new technologies.The FTE SunGuide TMC WWD event durations were collected for the nearest known interchange from the SunGuide reports. This information was compiled for the entire FTE system of interchanges. These SunGuide WWD event durations show the time spent by the FTE operators while actively combating and responding to various WWD events (never found events, pulled over events, and crashes). A method using the actual time spent responding to WWD, and the estimated duration of response (prior to the introduction of SunGuide) to crashes, citations, and 911 calls was developed to rank the interchanges in order of highest durations to lowest. The method developed in this dissertation showed the top percentiles in terms of durations (in minutes), and was used to cross check with the risk ranking of the WWD risk segment models GLM 4 and GLM 5. However, the SunGuide durations method is unique and robust because it weighs in individual interchanges using one common metric of WWD; i.e., total durations of response to the event at each interchange in the FTE system.Engineered countermeasures are important but these countermeasures are only effective if wrong-way drivers understand what they indicate. The Florida driver WWD survey implemented for this research showed that more than half of the respondents did not understand the meanings of the DO NOT ENTER symbol (only 44% of respondents were correct), and only 49% of respondents understood what wrong-way pavement arrows correctly mean. Over 70% of the 900 random respondents surveyed indicate their preference to RRFBs over the BlinkLink Signs. This is important to consider when expanding the implementation of countermeasures to other sites on the FTE system. The implementation of enhanced Intelligent Transportation System (ITS) countermeasure devices shows that Florida toll road agencies are working effectively towards reducing and correcting WWD events on their toll roads' networks. Reducing the risk of WWD crashes and non-crash events in general contributes significantly to the important goal of saving lives and money.
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Date Issued: 2016
Identifier: CFE0006544, ucf:51322
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006544

Title: Examining Multiple Approaches for the Transferability of Safety Performance Functions.
Creator: Farid, Ahmed Tarek Ahmed, Abdel-Aty, Mohamed, Lee, JaeYoung, Eluru, Naveen, University of Central Florida
Abstract / Description: Safety performance functions (SPFs) are essential in road safety since they are used to predict crash frequencies. They are commonly applied for detecting hot spots in network screening and assessing whether road safety countermeasures are effective. In the Highway Safety Manual (HSM), SPFs are provided for several crash classifications for several types of roadway facilities. The SPFs of the HSM are developed using data from multiple states. In regions where jurisdiction specific SPFs are...
Show moreSafety performance functions (SPFs) are essential in road safety since they are used to predict crash frequencies. They are commonly applied for detecting hot spots in network screening and assessing whether road safety countermeasures are effective. In the Highway Safety Manual (HSM), SPFs are provided for several crash classifications for several types of roadway facilities. The SPFs of the HSM are developed using data from multiple states. In regions where jurisdiction specific SPFs are not available, it is custom to adopt nationwide SPFs for crash predictions then apply a calibration factor. Yet, the research is limited regarding the application of national SPFs for local jurisdictions. In this study, the topic of transferability is explored by examining rural multilane highway SPFs from Florida, Ohio, and California. That is for both divided segments and intersections. Traffic, road geometrics and crash data from the three states are collected to develop one-state, two-state and three-state SPFs. The SPFs are negative binomial models taking the form of those of the HSM. Evaluation of the transferability of models is undertaken by calculating a measure known as the transfer index. It is used to explain which SPFs may be transferred tolerably to other jurisdictions. According to the results, the transferability of rural divided segments' SPFs of Florida to California and vice versa is superior to that of Ohio's SPFs. For four-leg signalized intersections, neither state's models are transferable to any state. Also, the transfer index indicates improved transferability when using pooled data from multiple states. Furthermore, a modified version of the Empirical Bayes method that is responsible for segment specific adjustment factors is proposed as an alternative to the HSM calibration method. It is used to adjust crash frequencies predicted by the SPFs being transferred to the jurisdiction of interest. The modified method, proposed, outperforms the HSM calibration method as per the analysis results.
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Date Issued: 2015
Identifier: CFE0006298, ucf:51604
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006298

Title: Safety investigation of traffic crashes incorporating spatial correlation effects.
Creator: Alkahtani, Khalid, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Lee, JaeYoung, Zheng, Qipeng, University of Central Florida
Abstract / Description: One main interest in crash frequency modeling is to predict crash counts over a spatial domain of interest (e.g., traffic analysis zones (TAZs)). The macro-level crash prediction models can assist transportation planners with a comprehensive perspective to consider safety in the long-range transportation planning process. Most of the previous studies that have examined traffic crashes at the macro-level are related to high-income countries, whereas there is a lack of similar studies among...
Show moreOne main interest in crash frequency modeling is to predict crash counts over a spatial domain of interest (e.g., traffic analysis zones (TAZs)). The macro-level crash prediction models can assist transportation planners with a comprehensive perspective to consider safety in the long-range transportation planning process. Most of the previous studies that have examined traffic crashes at the macro-level are related to high-income countries, whereas there is a lack of similar studies among lower- and middle-income countries where most road traffic deaths (90%) occur. This includes Middle Eastern countries, necessitating a thorough investigation and diagnosis of the issues and factors instigating traffic crashes in the region in order to reduce these serious traffic crashes. Since pedestrians are more vulnerable to traffic crashes compared to other road users, especially in this region, a safety investigation of pedestrian crashes is crucial to improving traffic safety. Riyadh, Saudi Arabia, which is one of the largest Middle East metropolises, is used as an example to reflect the representation of these countries' characteristics, where Saudi Arabia has a rather distinct situation in that it is considered a high-income country, and yet it has the highest rate of traffic fatalities compared to their high-income counterparts. Therefore, in this research, several statistical methods are used to investigate the association between traffic crash frequency and contributing factors of crash data, which are characterized by 1) geographical referencing (i.e., observed at specific locations) or spatially varying over geographic units when modeled; 2) correlation between different response variables (e.g., crash counts by severity or type levels); and 3) temporally correlated. A Bayesian multivariate spatial model is developed for predicting crash counts by severity and type. Therefore, based on the findings of this study, policy makers would be able to suggest appropriate safety countermeasures for each type of crash in each zone.
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Date Issued: 2018
Identifier: CFE0007148, ucf:52324
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007148

Title: Development of Decision Support System for Active Traffic Management Systems Considering Travel Time Reliability.
Creator: Chung, Whoibin, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Huang, Hsin-Hsiung, University of Central Florida
Abstract / Description: As traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks....
Show moreAs traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks. It has been required that these systems should prove their effectiveness in terms of travel time reliability. Therefore, this study aims to develop a new concept of a decision support system integrating variable speed limits, queue warning, and ramp metering on the basis of travel time reliability of freeways and arterials.Regarding the data preparation, in addition to collecting multiple data sources such as traffic data, crash data and so on, the types of traffic data sources that can be applied for the analysis of travel time reliability were investigated. Although there are many kinds of real-time traffic data from third-party traffic data providers, it was confirmed that these data cannot represent true travel time reliability through the comparative analysis of measures of travel time reliability. Related to weather data, it was proven that nationwide land-based weather stations could be applicable.Since travel time reliability can be measured by using long-term periods for more than six months, it is necessary to develop models to estimate travel time reliability through real-time traffic data and event-related data. Among various matrix to measure travel time reliability, the standard deviation of travel time rate [minute/mile] representing travel time variability was chosen because it can represent travel time variability of both link and network level. Several models were developed to estimate the standard deviation of travel time rate through average travel time rate, the number of lanes, speed limits, and the amount of rainfall.Finally, a DSS using a model predictive control method to integrate multiple traffic control measures was developed and evaluated. As a representative model predictive control, METANET model was chosen, which can include variable speed limit, queue warning, and ramp metering, separately or combined. The developed DSS identified a proper response plan by comparing travel time reliability among multiple combinations of current and new response values of strategies. In the end, it was found that the DSS provided the reduction of travel time and improvement of its reliability for travelers through the recommended response plans.
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Date Issued: 2019
Identifier: CFE0007615, ucf:52542
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007615

Title: Exploration and development of crash modification factors and functions for single and multiple treatments.
Creator: Park, Juneyoung, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Wang, Chung-Ching, Lee, JaeYoung, University of Central Florida
Abstract / Description: Traffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway...
Show moreTraffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway Traffic Safety Administration (NHTSA, 2013), 33,561 people died in motor vehicle traffic crashes in the United States in 2012, compared to 32,479 in 2011, and it is the first increase in fatalities since 2005. Moreover, in 2012, an estimated 2.36 million people were injured in motor vehicle traffic crashes, compared to 2.22 million in 2011. Due to the demand of highway safety improvements through systematic analysis of specific roadway cross-section elements and treatments, the Highway Safety Manual (HSM) (AASHTO, 2010) was developed by the Transportation Research Board (TRB) to introduce a science-based technical approach for safety analysis. One of the main parts in the HSM, Part D, contains crash modification factors (CMFs) for various treatments on roadway segments and at intersections. A CMF is a factor that can estimate potential changes in crash frequency as a result of implementing a specific treatment (or countermeasure). CMFs in Part D have been developed using high-quality observational before-after studies that account for the regression to the mean threat. Observational before-after studies are the most common methods for evaluating safety effectiveness and calculating CMFs of specific roadway treatments. Moreover, cross-sectional method has commonly been used to derive CMFs since it is easier to collect the data compared to before-after methods.Although various CMFs have been calculated and introduced in the HSM, still there are critical limitations that are required to be investigated. First, the HSM provides various CMFs for single treatments, but not CMFs for multiple treatments to roadway segments. The HSM suggests that CMFs are multiplied to estimate the combined safety effects of single treatments. However, the HSM cautions that the multiplication of the CMFs may over- or under-estimate combined effects of multiple treatments. In this dissertation, several methodologies are proposed to estimate more reliable combined safety effects in both observational before-after studies and the cross-sectional method. Averaging two best combining methods is suggested to use to account for the effects of over- or under- estimation. Moreover, it is recommended to develop adjustment factor and function (i.e. weighting factor and function) to apply to estimate more accurate safety performance in assessing safety effects of multiple treatments. The multivariate adaptive regression splines (MARS) modeling is proposed to avoid the over-estimation problem through consideration of interaction impacts between variables in this dissertation. Second, the variation of CMFs with different roadway characteristics among treated sites over time is ignored because the CMF is a fixed value that represents the overall safety effect of the treatment for all treated sites for specific time periods. Recently, few studies developed crash modification functions (CMFunctions) to overcome this limitation. However, although previous studies assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on the variation in the safety effects of treated sites with different multiple roadway characteristics over time. In this study, adopting various multivariate linear and nonlinear modeling techniques is suggested to develop CMFunctions. Multiple linear regression modeling can be utilized to consider different multiple roadway characteristics. To reflect nonlinearity of predictors, a regression model with nonlinearizing link function needs to be developed. The Bayesian approach can also be adopted due to its strength to avoid the problem of over fitting that occurs when the number of observations is limited and the number of variables is large. Moreover, two data mining techniques (i.e. gradient boosting and MARS) are suggested to use 1) to achieve better performance of CMFunctions with consideration of variable importance, and 2) to reflect both nonlinear trend of predictors and interaction impacts between variables at the same time. Third, the nonlinearity of variables in the cross-sectional method is not discussed in the HSM. Generally, the cross-sectional method is also known as safety performance functions (SPFs) and generalized linear model (GLM) is applied to estimate SPFs. However, the estimated CMFs from GLM cannot account for the nonlinear effect of the treatment since the coefficients in the GLM are assumed to be fixed. In this dissertation, applications of using generalized nonlinear model (GNM) and MARS in the cross-sectional method are proposed. In GNMs, the nonlinear effects of independent variables to crash analysis can be captured by the development of nonlinearizing link function. Moreover, the MARS accommodate nonlinearity of independent variables and interaction effects for complex data structures. In this dissertation, the CMFs and CMFunctions are estimated for various single and combination of treatments for different roadway types (e.g. rural two-lane, rural multi-lane roadways, urban arterials, freeways, etc.) as below:1) Treatments for mainline of roadway: - adding a thru lane, conversion of 4-lane undivided roadways to 3-lane with two-way left turn lane (TWLTL)2) Treatments for roadway shoulder: - installing shoulder rumble strips, widening shoulder width, adding bike lanes, changing bike lane width, installing roadside barriers3) Treatments related to roadside features: - decrease density of driveways, decrease density of roadside poles, increase distance to roadside poles, increase distance to trees Expected contributions of this study are to 1) suggest approaches to estimate more reliable safety effects of multiple treatments, 2) propose methodologies to develop CMFunctions to assess the variation of CMFs with different characteristics among treated sites, and 3) recommend applications of using GNM and MARS to simultaneously consider the interaction impact of more than one variables and nonlinearity of predictors.Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are discussed in this dissertation.
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Date Issued: 2015
Identifier: CFE0005861, ucf:50914
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005861

Title: Evaluation of crash modification factors and functions including time trends at intersections.
Creator: Wang, Jung-Han, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Lee, JaeYoung, Wang, Chung-Ching, University of Central Florida
Abstract / Description: Traffic demand has increased as population increased. The US population reached 313,914,040 in 2012 (US Census Bureau, 2015). Increased travel demand may have potential impact on roadway safety and the operational characteristics of roadways. Total crashes and injury crashes at intersections accounted for 40% and 44% of traffic crashes, respectively, on US roadways in 2007 according to the Intersection Safety Issue Brief (FHWA, 2009). Traffic researchers and engineers have developed a...
Show moreTraffic demand has increased as population increased. The US population reached 313,914,040 in 2012 (US Census Bureau, 2015). Increased travel demand may have potential impact on roadway safety and the operational characteristics of roadways. Total crashes and injury crashes at intersections accounted for 40% and 44% of traffic crashes, respectively, on US roadways in 2007 according to the Intersection Safety Issue Brief (FHWA, 2009). Traffic researchers and engineers have developed a quantitative measure of the safety effectiveness of treatments in the form of crash modification factors (CMF). Based on CMFs from multiple studies, the Highway Safety Manual (HSM) Part D (AASHTO, 2010) provides CMFs which can be used to determine the expected number of crash reduction or increase after treatments were installed. Even though CMFs have been introduced in the HSM, there are still limitations that require to be investigated. One important potential limitation is that the HSM provides various CMFs as fixed values, rather than CMFs under different configurations. In this dissertation, the CMFs were estimated using the observational before-after study to show that the CMFs vary across different traffic volume levels when signalizing intersections. Besides screening the effect of traffic volume, previous studies showed that CMFs could vary over time after the treatment was implemented. Thus, in this dissertation, the trends of CMFs for the signalization and adding red light running cameras (RLCs) were evaluated. CMFs for these treatments were measured in each month and 90- day moving windows using the time series ARMA model. The results of the signalization show that the CMFs for rear-end crashes were lower at the early phase after the signalization but gradually increased from the 9th month. Besides, it was also found that the safety effectiveness is significantly worse 18 months after installing RLCs.Although efforts have been made to seek reliable CMFs, the best estimate of CMFs is still widely debated. Since CMFs are non-zero estimates, the population of all CMFs does not follow normal distributions and even if it did, the true mean of CMFs at some intersections may be different than that at others. Therefore, a bootstrap method was proposed to estimate CMFs that makes no distributional assumptions. Through examining the distribution of CMFs estimated by bootstrapped resamples, a CMF precision rating method is suggested to evaluate the reliability of the estimated CMFs. The result shows that the estimated CMF for angle+left-turn crashes after signalization has the highest precision, while estimates of the CMF for rear-end crashes are extremely unreliable. The CMFs for KABCO, KABC, and KAB crashes proved to be reliable for the majority of intersections, but the estimated effect of signalization may not be accurate at some sites.In addition, the bootstrap method provides a quantitative measure to identify the reliability of CMFs, however, the CMF transferability is questionable. Since the development of CMFs requires safety performance functions (SPFs), could CMFs be developed using the SPFs from other states in the United States? This research applies the empirical Bayes method to develop CMFs using several SPFs from different jurisdictions and adjusted by calibration factors. After examination, it is found that applying SPFs from other jurisdictions is not desired when developing CMFs.The process of estimating CMFs using before-after studies requires the understanding of multiple statistical principles. In order to simplify the process of CMF estimation and make the CMFs research reproducible. This dissertation includes an open source statistics package built in R (R, 2013) to make the estimation accessible and reproducible. With this package, authorities are able to estimate reliable CMFs following the procedure suggested by FHWA. In addition, this software package equips a graphical interface which integrates the algorithm of calculating CMFs so that users can perform CMF calculation with minimum programming prerequisite. Expected contributions of this study are to 1) propose methodologies for CMFs to assess the variation of CMFs with different characteristics among treated sites, 2) suggest new objective criteria to judge the reliability of safety estimation, 3) examine the transferability of SPFs when developing CMF using before-after studies, and 4) develop a statistics software to calculate CMFs. Finally, potential relevant applications beyond the scope of this research, but worth investigation in the future are discussed in this dissertation.
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Date Issued: 2016
Identifier: CFE0006413, ucf:51454
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006413

Title: Microscopic Safety Evaluation and Prediction for Special Expressway Facilities.
Creator: Wang, Ling, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Lee, JaeYoung, Uddin, Nizam, University of Central Florida
Abstract / Description: Expressways are of great importance and serve as the backbone of a roadway system. One of the reasons why expressways increase travel speeds and provide high level of services is that limited access is provided to permit vehicles to enter or exit expressways. Entering and exiting of vehicles are accomplished through interchanges, which consist of several ramps, thus the spacing between ramps is important. A weaving segment might form when an on-ramp is closely followed by an off-ramp. The...
Show moreExpressways are of great importance and serve as the backbone of a roadway system. One of the reasons why expressways increase travel speeds and provide high level of services is that limited access is provided to permit vehicles to enter or exit expressways. Entering and exiting of vehicles are accomplished through interchanges, which consist of several ramps, thus the spacing between ramps is important. A weaving segment might form when an on-ramp is closely followed by an off-ramp. The geometric design of ramps and the traffic behavior of weaving segments are different from other expressway segments. These differences result in distinct safety mechanisms of these two expressway special facilities. Hence, the safety of these two facilities needs to be addressed.The majority of previous traffic safety studies on expressway special facilities are based on highly aggregated traffic data, e.g., Annual Average Daily Traffic (AADT). This highly aggregated traffic data cannot represent traffic conditions at the time of crashes and also cannot be used in the study of weather and temporal impact on crash occurrence. One way to solve this problem is microscopic safety evaluation and prediction through hourly crash prediction and real-time safety analysis. An hourly crash study averages one or several hours' traffic data in a year and also aggregates crash frequencies in the corresponding hour(s). Then it applies predictive models to determine the statistical relationship between crashes and hourly traffic flow characteristics, such as traffic volume. Real-time safety analysis enables us to predict crash risk and distinguish crashes from non-crashes in the next few minutes using the current traffic, weather, and other conditions.There are four types of crash contributing factors: traffic, geometry, weather, and driver. Among these, traffic parameters have been utilized in all previous microscopic safety studies. On the other hand, the other three factors' impact on microscopic safety has not been widely analyzed. The geometric factors' influence on safety are generally excluded by previous researchers using the matched-case-control method, because the majority of previous microscopic safety studies are on mainlines, where the geometric design of a segment does not change much and geometry does not have a significant effect on safety. Not enough studies have adopted weather factors in microscopic safety analysis because of the limited availability of weather data. The impact of drivers on safety has also not been widely considered since driver information is hard to be obtained. This study explores the relationship between crashes and the four contributing factors. Weather data are obtained from airport weather stations and crash reports which record weather and roadway surface conditions for crashes. Meanwhile, land-use and trip generation parameters serve as surrogates for drivers' behavior.Several methods are used to explore and quantify the impact of these factors. Random forests are used in discovering important and significant explanatory variables, which play significant roles in determining traffic safety, by ranking their importance. Meanwhile, in order to prevent high correlation between independent variables, Pearson correlation tests are carried out before model estimations. Only the variables which are not highly correlated are selected. Then, the selected variables are put in logistic regression models and Poisson-lognormal models to respectively estimate crash risk and crash frequency for special expressway facilities. Meanwhile, in case of correlation among observations in the same segment, a multilevel modeling structure has been implemented. Furthermore, a data mining technique(-)Support Vector Machine (SVM)(-)is used to distinguish crash from non-crash observations. Once the crash mechanisms for special expressway facilities are found, we are able to provide valuable information on how to manage roadway facilities to improve the traffic safety of special facilities. This study adopts Active Traffic Management (ATM) strategies, including Ramp Metering (RM) and Variable Speed Limit (VSL), in order to enhance the safety of a congested weaving segment. RM regulates the entering vehicle volume by adjusts metering rate, and VSL is able to provide smoother mainline traffic by changing the mainline speed limits. The ATM strategies are carried out in microscopic simulation VISSIM through the Component Object Model (COM) interface. The results shows that the crash risk and conflict count of the studies weaving segment have been significantly reduced because of ATM.Furthermore, the mechanisms of traffic conflicts, a surrogate safety measurement, are explored for weaving segments using microscopic simulation. The weaving segment conflict prediction model is compared with its crash prediction model. The results show that there are similarity and differences between conflict and crash mechanisms. Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are also discussed in this dissertation.
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Date Issued: 2016
Identifier: CFE0006414, ucf:51480
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006414

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

Traffic Safety (4)	+ -
Freight (3)	+ -
Highway Safety Manual (3)	+ -
Crash Modification Factors (2)	+ -
Destination Choice (2)	+ -

Intersection (2)	+ -
Mode Choice (2)	+ -
Safety Effects (2)	+ -
Simulation (2)	+ -
Transferability (2)	+ -
Transportation Engineering (2)	+ -
Travel Time (2)	+ -
VISSIM (2)	+ -
Wrong-Way Driving (2)	+ -
logistic regression (2)	+ -
ARMA (1)	+ -
ATIS (1)	+ -
Access Zones (1)	+ -
Adaptive Traffic Control System (ATCS) (1)	+ -
Adaptive Vehicular Systems (1)	+ -
Adaptive signal control data (1)	+ -
Arterial safety (1)	+ -
Average-car Method (1)	+ -
Bayesian Machine Learning (1)	+ -
Bayesian modeling (1)	+ -
Bayesian models (1)	+ -
Before-After Analysis (1)	+ -
Before-After Study (1)	+ -
Before-after Study (1)	+ -
Benefit-Cost Analysis (1)	+ -

Eluru, Naveen (41)	+ -
University of Central Florida (41)	+ -
Abdel-Aty, Mohamed (30)	+ -
Lee, JaeYoung (15)	+ -
Hasan, Samiul (10)	+ -

Radwan, Essam (9)	+ -
Al-Deek, Haitham (5)	+ -
Uddin, Nizam (5)	+ -
Cai, Qing (4)	+ -
Tatari, Omer (4)	+ -
Yan, Xin (4)	+ -
Zheng, Qipeng (4)	+ -
Abou-Senna, Hatem (3)	+ -
Anowar, Sabreena (3)	+ -
Wang, Chung-Ching (3)	+ -
Wu, Yina (3)	+ -
Alfawzan, Mohammed (2)	+ -
Farid, Ahmed Tarek Ahmed (2)	+ -
Keya, Nowreen (2)	+ -
Oloufa, Amr (2)	+ -
Rahman, Md Sharikur (2)	+ -
Saad, Moatz (2)	+ -
Yasmin, Shamsunnahar (2)	+ -
Al Amili, Samer (1)	+ -
Alkahtani, Khalid (1)	+ -
Alomari, Ahmad (1)	+ -
Atia, George (1)	+ -
Bhowmik, Tanmoy (1)	+ -
Bustamante, Claudia (1)	+ -
Carroll, Kali (1)	+ -

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