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Browsing by Author "El-Shawarby, Ihab"

Now showing 1 - 6 of 6
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    Capacity Modeling of Freeway Weaving Sections
    Zhang, Yihua (Virginia Tech, 2005-05-09)
    The dissertation develops analytical models that estimate the capacity of freeway weaving sections. The analytical models are developed using simulated data that were compiled using the INTEGRATION software. Consequently, the first step of the research effort is to validate the INTEGRATION lane-changing modeling procedures and the capacity estimates that are derived from the model against field observations. The INTEGRATION software is validated against field data gathered by the University of California at Berkeley by comparing the lateral and longitudinal distribution of simulated and field observed traffic volumes categorized by O-D pair on nine weaving sections in the Los Angeles area. The results demonstrate a high degree of consistency between simulated and field observed traffic volumes within the various weaving sections. Subsequently, the second validation effort compares the capacity estimates of the INTEGRATION software to field observations from four weaving sections operating at capacity on the Queen Elizabeth Way (QEW) in Toronto, Canada. Again, the results demonstrate that the capacity estimates of the INTEGRATION software are consistent with the field observations both in terms of absolute values and temporal variability across different days. The error was found to be in the range of 10% between simulated and field observed capacities. Prior to developing the analytical models, the dissertation presents a systematic analysis of the factors that impact the capacity of freeway weaving sections, which were found to include the length of the weaving section, the weaving ratio (a new parameter that is developed as part of this research effort), the percentage of heavy vehicles, and the speed limit differential between freeway and on- and off-ramps. The study demonstrates that the weaving ratio, which is currently defined as the ratio of the lowest weaving volume to the total weaving volume in the 2000 Highway Capacity Manual, has a significant impact on the capacity of weaving sections. The study also demonstrates that the weaving ratio is an asymmetric function and thus should reflect the source of the weaving volume. Consequently, a new definition for the weaving ratio is introduced that explicitly identifies the source of the weaving volume. In addition, the study demonstrates that the length of the weaving section has a larger impact on the capacity of weaving sections for short lengths and high traffic demands. Furthermore, the study demonstrates that there does not exist enough evidence to conclude that the speed limit differential between mainline freeway and on- and off-ramps has a significant impact on weaving section capacities. Finally, the study demonstrates that the HCM procedures model the heavy duty vehicle impacts reasonably well. This dissertation presents the development of new capacity models for freeway weaving sections. In these models, a new definition of the weaving ratio that explicitly accounts for the source of weaving volume is introduced. The proposed analytical models estimate the capacity of weaving sections to within 12% of the simulated data, while the HCM procedures exhibit errors in the range of 114%. Among the newly developed models, the Artificial Neural Network (ANN) models performs slightly better that the statistical models in terms of model prediction errors. However, the sensitivity analysis results demonstrate unrealistic behavior of the ANN models under certain conditions. Consequently, the use of a statistical model is recommended because it provides a high level of accuracy while providing accurate model responses to changes in model input parameters (good response to the gradient of the input parameters).
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    Design of Wet Surface Traffic Signal Timing Change Intervals
    Li, Huan (Virginia Tech, 2011-02-02)
    Driver violations of traffic signals are a major cause of intersection vehicle crashes. The duration of yellow intervals is highly associated with driver yellow/red time stopping behavior. Rainy weather and wet pavement surface conditions may result in changes in both driver behavior and vehicle performance. The research presented in this thesis quantifies the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs) and deceleration levels, which are used in statistical models for the design of yellow intervals. A new dataset with a total of 648 stop-run records were collected as part of the research effort during rainy weather and wet pavement surface conditions at the Virginia Department of Transportation's Smart Road facility. This experiment was conducted at a 72.4 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Combined with an existing dataset that was collected by the same research group under clear weather conditions during the summer of 2008, statistical models for driver PRT and deceleration levels are developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, and time to the intersection at the onset of yellow. Using the state-of-the-practice procedures with the modeled PRT and deceleration levels, inclement weather yellow timings are then developed as a function of different factors (e.g., driver age/gender, roadway grade, speed limits, and precipitation levels). The results indicate that an increase in the duration of change interval is required for wet roadway surface and rainy weather conditions. Lookup tables are developed with different reliability levels to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. These recommended change durations can be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication.
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    Development of a Framework for Evaluating Yellow Timing at Signalized Intersections
    Rakha, Hesham A.; El-Shawarby, Ihab; Amer, Ahmed (Virginia Center for Transportation Innovation and Research, 2011-02-01)
    Studies show that the proper design of clearance intervals has significant implications for intersection safety. For example, in 2001, approximately 218,000 red-light-running crashes occurred at signalized intersections in the United States. These crashes resulted in nearly 181,000 injuries and 880 fatalities and an economic loss of $14 billion. Driver behavior while the driver is approaching high-speed signalized intersections at the onset of a yellow indication varies as a function of many parameters. Some of these parameters are related to the driver's attributes, e.g., age, gender, perception-reaction time, and acceptable deceleration levels. Other parameters that relate to the intersection geometry include the approach speed, distance, and time to the intersection at the onset of the yellow indication. This study developed a novice approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers are not caught in a dilemma zone). Lookup tables based on the limited data available from this study are provided to illustrate how the framework could be used in the design of yellow timings. The approach was developed using data gathered along Virginia's Smart Road test facility for dry and clear weather conditions for two approach speeds: 72.4 km/h (45 mph) and 88.5 km/h (55 mph). Each dataset includes a complete tracking of the vehicle every deci-second within 150 m (500 ft) before and after the intersection. A total of 3,454 stop-run records were gathered. These include 1,727 records (687 running records and 1,040 stopping records) for an approach speed of 45 mph and 1,727 records (625 running records and 1,102 stopping records) for an approach speed of 55 mph. Using these data, models that characterize driver perception-reaction times and deceleration levels were developed. The application of the proposed approach demonstrates that the current design procedures are consistent with a reliability level of 98%.
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    Field Testing of Eco-Speed Control Using V2I Communication
    Rakha, Hesham A.; Chen, Hao; Almannaa, Mohammed Hamad; Kamalanathsharma, Raj Kishore; El-Shawarby, Ihab; Loulizi, Amara (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-04-15)
    This research focused on the development of an Eco-Cooperative Adaptive Cruise Control (Eco-CACC) System and addressed the implementation issues associated with applying it in the field. The Eco-CACC system computes and recommends a fuel-efficient speed based on Signal Phasing and Timing (SPaT) data received from the traffic signal controller via vehicle-to-infrastructure (V2I) communication. The computed speed profile can either be broadcast as an audio alert to the driver to manually control the vehicle, or, implemented in an automated vehicle (AV) to automatically control the vehicle. The proposed system addresses all possible scenarios, algorithmically, that a driver may encounter when approaching a signalized intersection. Additionally, from an implementation standpoint, the research addresses the challenges associated with communication latency, data errors, real-time computation, and ride smoothness. The system was tested on the Virginia Smart Road Connected Vehicle Test Bed in Blacksburg, VA. Four scenarios were tested for each participant: a base driving scenario, where no speed profile data was communicated; a scenario in which the driver was provided with a “time to red light” countdown; a manual Eco-CACC scenario where the driver was instructed to follow a recommended speed profile given via audio alert; and finally, an automated Eco-CACC scenario where the AV system controlled the vehicle’s longitudinal motion. The field test included 32 participants, and each participant completed 64 trips to pass through a signalized intersection for different combinations of signal timing and road grades. The analyzed results demonstrate the benefits of the Eco-CACC system in assisting vehicles to drive smoothly in the vicinity of intersections, thereby reducing fuel consumption levels and travel times. Compared to an uninformed baseline drive, the longitudinally automated Eco-CACC system controlled vehicle drive resulted in savings in fuel consumption levels and travel times of approximately 37.8% and 9.3%, respectively.
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    Statistical and Behavioral Modeling of Driver Behavior on Signalized Intersection Approaches
    Amer, Ahmed (Virginia Tech, 2010-12-08)
    The onset of a yellow indication is typically associated with the risk of vehicle crashes resulting from dilemma-zone and red-light-running problems. Such risk of vehicle crashes is greater for high-speed signalized intersection approaches. The research presented in this dissertation develops statistical as well as behavioral frameworks for modeling driver behavior while approaching high-speed signalized intersection approaches at the onset of a yellow indication. The analysis in this dissertation utilizes two sources of data. The main source is a new dataset that was collected as part of this research effort during the summer of 2008. This experiment includes two instructed speeds; 72.4 km/h (45 mph) with 1727 approaching trials (687 running and 1040 stopping), and 88.5 km/h (55 mph) with 1727 approaching trials (625 running and 1102 stopping). The complementary source is an existing dataset that was collected earlier in the spring of 2005 on the Virginia Smart Road facility. This dataset includes a total of 1186 yellow approaching trials (441 running and 745 stopping). The adopted analysis approach comprises four major parts that fulfill the objectives of this dissertation. The first part is concerned with the characterization of different driver behavioral attributes, including driver yellow/red light running behavior, driver stop-run decisions, driver perception-reaction times (PRT), and driver deceleration levels. The characterization of these attributes involves analysis of variance (ANOVA) and frequency distribution analyses, as well as the calibration of statistical models. The second part of the dissertation introduces a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver PRT and deceleration levels. An extension of the proposed approach is presented that can be integrated with the IntelliDriveSM initiative. Furthermore, the third part of the dissertation develops an agent-based Bayesian statistics approach to capture the stochastic nature of the driver stop-run decision. The Bayesian model parameters are calibrated using the Markov Chain Monte Carlo (MCMC) slice procedure implemented within the MATLAB® software. In addition, two procedures for the Bayesian model application are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The proposed Bayesian approach is ideal for modeling multi-agent systems in which each agent has its own unique set of parameters. Finally, the fourth part of the dissertation introduces and validates a state-of-the-art behavioral modeling framework that can be used as a tool to simulate driver behavior after the onset of a yellow indication until he/she reaches the intersection stop line. The behavioral model is able to track dilemma zone drivers and update the information available to them every time step until they reach a final decision. It is anticipated that this behavioral model will be implemented in microscopic traffic simulation software to enhance the modeling of driver behavior as they approach signalized intersections.
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    A Study of the Capacity Drop Phenomenon at Time-Dependent and Time-Independent Bottlenecks
    El-Metwally, Maha (Virginia Tech, 2010-12-06)
    The fact that traffic congestion upstream of a bottleneck causes a reduction in the discharge flow rate through the bottleneck has been well documented in several empirical studies. However, what has been missing is an understanding of the causes of these empirically observed flow reductions. An identification of these causes is important in order to develop various mitigation schemes through the use of emerging technology. The concept of capacity drop can be introduced at time-independent bottlenecks (e.g. freeways) as well as time-dependent bottlenecks (e.g. signalized intersections). While to the author's knowledge no one has attempted to link these phenomena, the research presented in this thesis serves as a first step in doing so. The research uses the INTEGRATION simulation software, after demonstrating its validity against empirical data, to simulate time-independent and time-dependent bottlenecks in an attempt to characterize and understand the contributing factors to these flow reductions. Initially, the INTEGRATION simulation software is validated by comparing its results to empirically observed traffic stream behavior. This thesis demonstrates that the discharge flow rate is reduced at stationary bottlenecks at the onset of congestion. These reductions at stationary bottlenecks are not recovered as the traffic stream propagates downstream. Furthermore, these reductions are not impacted by the level of vehicle acceleration. Alternatively, the drop in the discharge flow rate caused by time-dependent bottleneck is recoverable and is dependent on the level of acceleration. The difference in behavior is attributed to the fact that in the case of a stationary bottleneck the delay in vehicle headways exceeds the losses caused by vehicle accelerations and thus is not recoverable. In the case of vehicles discharging from a backward recovery wave the dominant factor is the delay caused by vehicle acceleration and this can be recuperated as the traffic stream travels downstream.