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Dilemma zone (DZ), also called decision zone in other literature, is an area where drivers face an indecisiveness of stopping or crossing at the yellow onset. The DZ issue is a major reason for the crashes at high-speed signalized intersections. As a result, how to prevent approaching vehicles from being caught in the DZ is a widely concerning issue. In this dissertation, the author addressed several DZ-associated issues, including the new stochastic safety measure, namely dilemma hazard, that indicates the vehicles' changing unsafe levels when they are approaching intersections, the optimal advance detector configurations for the multi-detector green extension systems, the new dilemma zone protection algorithm based on the Markov process, and the simulation-based optimization of traffic signal systems with the retrospective approximation concept. The findings include: the dilemma hazard reaches the maximum when a vehicle moves in the dilemma zone and it can be calculated according the caught vehicles' time to the intersection; the new (optimized) GES design can significantly improve the safety, but slightly improve the efficiency; the Markov process can be used in the dilemma zone protection, and the Markov-process-based dilemma zone protection system can outperform the prevailing dilemma zone protection system, the detection-control system (D-CS). When the data collection has higher fidelity, the new system will have an even better performance. The retrospective approximation technique can identify the sufficient, but not excessive, simulation efforts to model the true system and the new optimization algorithm can converge fast, as well as accommodate the requirements by the RA technique. / Ph. D.

The motivation behind this research is the need for a real-time implementable, yet accurate, procedure for estimating an origin-destination (O-D) trip-table based on entering and exiting traffic volume data for a given freeway section. These tables help in on-line control of traffic facilities, and consequently, are of significant use in alleviating traffic congestion. The dynamism of the approach captures the variations in the traffic counts with time which are in tum used to predict user travel patterns. Two models have been developed for this problem, one based on a least squares estimation and the other based on an <i>1</i>₁ norm approach. Two projected conjugate gradient schemes are investigated for solving the constrained least squares problem, and an interior point affine scaling algorithm that is applied to the dual problem is explored for solving the <i>1</i>₁ estimation linear programming problem. Computational results are presented on a set of test problems involving the determination of O-D trip tables for both intersection and freeway scenarios in order to demonstrate the viability of the proposed methods. These results exhibit that, unlike as reported in the literature based on previous efforts, properly designed parameter optimization methods can indeed provide accurate estimates in a realtime implementation. Hence, this research presents a competitive alternative to the iterative statistical techniques that have been heretofore used because of their real-time processing capabilities, despite their inherent inaccuracies. We hope that the proposed technology enhances existing methods for constructing O-D trip-tables from traffic counts. / Master of Science

The Alcohol Safety Action Program was introduced in the early 1970s as a comprehensive systems approach for reducing alcohol-related automobile crashes. This dissertation gathers evidence and insights helpful to planners, evaluators, policy-makers, and program implementors. Specifically, A.S.A.P. “Level II” effectiveness in reducing the recidivism rate among program participants was examined. A combination of quantitative and qualitative assessments of the program was performed to gain in-depth insight and to determine which program elements seem associated with its success or failure. Quantitative analysis emphasized A.S.A.P. and non-A.S.A.P. participant two-year recidivism rates. Participants from two Virginia localities in 1977-80 were examined. Independent variables commonly held by both types of participants are age, sex, court delay, and prior offense records. Variables unique to each program were also examined. Qualitative insights were gathered through interviews with current A.S.A.P. participants prior to and following program completion, past program participants, and course instructors. An approximate three-to-one difference in recidivism rate was found between A.S.A.P. and non-A.S.A.P. participants. Variables significantly related to recidivism were prior D.W.I. offenses, court delay, prior reckless driving offenses, and age. Differences based on location were also found. The interviews demonstrate basic satisfaction with the course. Participants cited the overall arrest experience as having the largest impact, with the course providing supportive information. The factor emerging to deter future behavior was the negative experience--the “hassle”--associated with the D.W.I. offense. Overall, it appears that the blend of the educational and punitive approaches makes the A.S.A.P. program more effective than the alternative approaches being used. Specific recommendations emerging from the research are of four general types: administrative mechanisms, laws and policies, the A.S.A.P. course, and evaluation. / Ph. D.

Dynamic Traffic Assignment (DTA) is gaining wider acceptance among agencies and practitioners because it serves as a more realistic representation of real-world traffic phenomena than static traffic assignment. Many metropolitan planning organizations and transportation departments are beginning to utilize DTA to predict traffic flows within their networks when conducting traffic analysis or evaluating management measures. To analyze DTA-based optimization applications, it is critical to obtain the dual (or gradient) information as dual information can typically be employed as a search direction in algorithmic design. However, very limited number of approaches can be used to estimate network-wide dual information while maintaining the potential to scale. This dissertation investigates the theoretical/practical aspects of DTA-based dual approximation techniques and explores DTA applications in the context of various transportation models, such as transportation network design, off-line DTA capacity calibration and dynamic congestion pricing. Each of the later entities is formulated as bi-level programs. Transportation Network Design Problem (NDP) aims to determine the optimal network expansion policy under a given budget constraint. NDP is bi-level by nature and can be considered a static case of a Stackelberg game, in which transportation planners (leaders) attempt to optimize the overall transportation system while road users (followers) attempt to achieve their own maximal benefit. The first part of this dissertation attempts to study NDP by combining a decomposition-based algorithmic structure with dual variable approximation techniques derived from linear programming theory. One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. It is therefore imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies. Satisfactory calibration of the DTA model is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. In this dissertation, the off-line DTA capacity calibration problem is studied in an attempt to devise a systematic approach for effective model calibration. Congestion pricing has increasingly been seen as a powerful tool for both managing congestion and generating revenue for infrastructure maintenance and sustainable development. By carefully levying tolls on roadways, a more efficient and optimal network flow pattern can be generated. Furthermore, congestion pricing acts as an effective travel demand management strategy that reduces peak period vehicle trips by encouraging people to shift to more efficient modes such as transit. Recently, with the increase in the number of highway Build-Operate-Transfer (B-O-T) projects, tolling has been interpreted as an effective way to generate revenue to offset the construction and maintenance costs of infrastructure. To maximize the benefits of congestion pricing, a careful analysis based on dynamic traffic conditions has to be conducted before determining tolls, since sub-optimal tolls can significantly worsen the system performance. Combining a network-wide time-varying toll analysis together with an efficient solution-building approach will be one of the main contributions of this dissertation. The problems mentioned above are typically framed as bi-level programs, which pose considerable challenges in theory and as well as in application. Due to the non-convex solution space and inherent NP-complete complexity, a majority of recent research efforts have focused on tackling bi-level programs using meta-heuristics. These approaches allow for the efficient exploration of complex solution spaces and the identification of potential global optima. Accordingly, this dissertation also attempts to present and compare several meta-heuristics through extensive numerical experiments to determine the most effective and efficient meta-heuristic, as a means of better investigating realistic network scenarios. / text

Accidents are a complex process involving many contributory factors. The understanding of the accident process has often been sought by the use of accident data. Although accident data provide a direct relationship to estimating accident risk, there are many drawbacks associated with the use of these data. The major drawback with the use of accident data is the very fact that traffic engineers have to wait for accidents to occur before any interventions can be made. This alone is significant as the time span required to collect a sample size is often a three-year period. The many deficiencies with accident data have led to alternative measures such as traffic conflict techniques (TCT's) to estimate accident risk.In this investigation. traffic conflict techniques were used to estimate accident risk. There are four basic traffic conflict concepts and the development of these techniques was based on the accident process. The aim of this investigation was to highlight the differences between these concepts and to assess the applicability of these concepts to vehicle-pedestrian conflicts. The investigation was based on applying the various conflict techniques to data obtained at three intersections in the Durban CBD. In order to record the data an innovative method of using digital imaging was employed. This led to the development of a computer program to analyse conflict events. Analysis of the intersections based on the conflict techniques indicates that the intersections of Pine-Field and Commercial-Grey have a high probability of road users being involved in a "serious event" once there is an interaction between them. However, the probability for Commercial-Albert intersection is low thus indicating a safe intersection for vehicle-pedestrian interactions. The number of "serious events" at these locations was found to be related to the interacting traffic volumes - the conflict rate increases with increasing traffic volume. The use of conflict-volume models and accident models together with the conflict concepts agree that the accident risk is related to the conflicting traffic volumes and speed of the road users. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

With the remarkable increase in the population and number of vehicles, traffic has become a severe problem in most metropolitan areas. Traffic congestion has imposed tight constraints on economic growth, national security, and mobility of riders and goods. The open-mode integrated transportation system (OMITS) has been designed to improve the traffic condition of roadways by increasing the ridership of vehicles and optimizing transportation modes through smart services integrating emerging information communication technologies, big data management, social networking, and transportation management. Even a modest reduction in the number of vehicles on roadways will lead to a considerable cost savings in terms of time and money. Additionally the reduction in traffic jams will lead to a significant decrease in both gasoline consumption and greenhouse gas emissions. As a result, novel transportation management is critical to reduce vehicle mileage in the peak time of the road network. The OMITS was proposed to enhance transportation services in respect to the following three aspects: optimization of the transportation modes by multimodal traveling assignment, dynamic routing and ridesharing service with advanced traveler information systems, and interactive user interface for social networking and traveling information. Therefore, the OMITS encompasses a broad range of advanced transportation research topics, say dynamic trip- match, transportation-mode optimization, traffic prediction, dynamic routing, and social network- based carpooling. This dissertation will focus on a kernel part of the OMITS, namely traffic simulation and prediction based on data containing the distribution of vehicles and the road network configuration. A microscopic traffic simulation framework has been developed to take into account various traffic phenomena, such as traffic jams resulting from bottlenecking, incidents, and traffic flow shock waves. Four fundamental contributions of the present study are summarized as follows: Firstly, an accurate and robust vehicle trajectory data collection method based on image data of unmanned aerial vehicle (UAV) has been presented, which can be used to rapidly and accurately acquire the real-time traffic conditions of the region of interest. Historically, a lack in the availability of trajectory data has posed a significant obstacle to the enhancement of microscopic simulation models. To overcome this obstacle, a UAV based vehicle trajectory data collection algorithm has been developed. This method extracts vehicle trajectory data from the UAV’s video at different altitudes with different view scopes. Compared with traditional methods, the present data collection algorithm incorporates many unique features to customize the vehicle and traffic flow, through which vehicle detection and tracking system accuracy can be considerably increased. Secondly, an open mechanics-based acceleration model has been presented to simulate the longitudinal motion of vehicles, in which five general factors—namely the subject vehicle’s speed and acceleration sensitivity, safety consideration, relative speed sensitivity and gap reducing desire—have been identified to describe drivers’ preferences and the interactions between vehicles. Inspired by the similarity between vehicle interactions and particle interactions, a mechanical system with force elements has been introduced to quantify the vehicle’s acceleration. Accordingly, each of the aforementioned five factors are assumed to function as an individual trigger to alter each vehicle’s speed. Based on Newton’s second law of motion, the subject vehicle’s longitudinal behavior can be simulated by the present open mechanics-based acceleration model. By introducing feeling gap, multilane acceleration behavior is included in the presented model. The simulation results fit realistic conditions for the traffic flow and the road capacity very well, where traffic shockwaves can be observed for a certain range of the traffic density. This model can be extended to more general scenarios if other factors can be recognized and introduced into the modeling framework. Thirdly, a driver decision-based lane change execution model has been developed to describe a vehicle’s lane change execution process, which includes two steps, i.e. driver’s lane selection and lane change execution. Currently, most lane change models focus on the driver’s lane selection, and overlook the driver’s behavior during a process of lane change execution which plays a significant role in the simulation of traffic flow characteristics. In this model, a lane change execution is analyzed as a driver’s decision-making process, which consists of desire point setting, priority decision-making, corresponding actions and achievement of consensus analysis. Compared with the traditional lane change execution models, the present model describes a realistic lane change process, and it provides more accurate and detailed simulation results in the microscopic traffic simulation. Based on the presented open mechanics-based acceleration model and the driver decision- based lane change execution model, a reverse lane change model has further been developed to simulate some complex traffic situations such as reverse lane change process at a two-way-two- lane road section where one lane is blocked by a traffic incident. Based on this reverse lane change model, information on the average waiting time and road capability can be obtained. The simulation results show that the present model is able to reflect real driver behavior and the corresponding traffic phenomenon during a reverse lane change process Through a homogenization process of the microscopic vehicle motion, we can obtain the macroscopic traffic flow of the roadway network within certain time and spatial ranges, which will be integrated into the OMITS system for traffic prediction. The validation of the models through future OMITS operations will also enable them to be high fidelity models in future driverless technologies and autonomous vehicles.

In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed. The current source of data is Twitter, however, the same mechanism can be extended to any kind of text available on the internet. As the data being considered is extremely large in size automated models are developed to stream, download, and mine the data in real-time. Furthermore, if any tweet has traffic related information then the models should be able to infer and extract this data. To pursue this task, Artificial Intelligence, Machine Learning, and Natural Language Processing techniques are used. These models are designed in such a way that they are able to detect the traffic congestion and traffic incidents from the Twitter stream at any location. Currently, the data is collected only for United States. The data is collected for 85 days (50 complete and 35 partial) randomly sampled over the span of five months (September, 2014 to February, 2015) and a total of 120,000 geo-tagged traffic related tweets are extracted, while six million geo-tagged non-traffic related tweets are retrieved. The classification models for detection of traffic congestion and incidents are trained on this dataset. Furthermore, this data is also used for various kinds of spatial and temporal analysis. A mechanism to calculate level of traffic congestion, safety, and traffic perception for cities in U.S. is proposed. Traffic congestion and safety rankings for the various urban areas are obtained and then they are statistically validated with existing widely adopted rankings. Traffic perception depicts the attitude and perception of people towards the traffic. It is also seen that traffic related data when visualized spatially and temporally provides the same pattern as the actual traffic flows for various urban areas. When visualized at the city level, it is clearly visible that the flow of tweets is similar to flow of vehicles and that the traffic related tweets are representative of traffic within the cities. With all the findings in current study, it is shown that significant amount of traffic related information can be extracted from Twitter and other sources on internet. Furthermore, Twitter and these data sources are freely available and are not bound by spatial and temporal limitations. That is, wherever there is a user there is a potential for data.

Thesis (M.S.)--Florida State University, 2006. / Advisor: James R. Simpson, Florida State University, College of Engineering, Dept. of Industrial Engineering. Title and description from dissertation home page (viewed Sept. 22, 2006). Document formatted into pages; contains ix, 95 pages. Includes bibliographical references.