No description available.

In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behaviors between drivers (e.g., finding a route using a navigation system) while the last two interaction types are relevant with behaviors involving non-information exchange behaviors (e.g., finding a route based on a driver's own experiences). To calibrate the proposed learning behavior model and evaluate its performance in terms of inference accuracy and computational efficiency, drivers' decision data at intersections are collected via a human-in-the-loop experiment involving a driving simulator. Moreover, the proposed model is used to test and demonstrate the impact of five interactions on drivers' learning behavior under an en route planning scenario with real traffic data of Albany, New York, and Phoenix, Arizona. In this dissertation work, two major traffic simulation platforms, AnyLogic® and DynusT®, are used for the demonstration purposes. The experimental results reveal that the proposed model is effective in modeling realistic learning behaviors of drivers in conduction with interactions with other drivers.

The growth of serious injuries and fatalities resulting from traffic accidents at intersections is one of the main problems in urban areas. Signal control was proposed as an alternative intersection design on rural roads. There were many reasons behind this, the most outstanding of which was the traffic signals can be used as a cost effective tools for traffic management in urban areas. The LHOVRA technique was intended to improve safety and reduce lost time at signalized intersection along high speed roads. The LHOVRA technique is an isolated traffic control strategy in Sweden which is formed from different concepts. This thesis work is aimed to evaluate the LHOVRA technique with a focus on the O-function. Hence, two different scenarios, one with O-function and one without O-function were implemented in the micro traffic simulation software, VISSIM. VISSIM has been used to simulate the traffic situation of the Gamla Övägen – Albrektsvägen intersection by considering the LHOVRA scenario (with O-function) as well as traditional scenario (without O-function) of the intersection. Field measurements were used as input data for VISSIM simulation. The VISSIM simulation model was calibrated to find a close match between simulated and real data. Finally, a comparison of alternatives was carried out based on traffic performance and traffic safety measurements. The simulation experiment results gained by the comparisons were presented a higher time-to-collision value. The higher time-to-collision value the safer situation is. Both delays and travel time were reduced to primary road traffic.

No description available.

Traffic signals are very cost effective tools for urban traffic management in urban areas. The number of intersections in Sweden controlled by traffic signals has increased since the seventies, but efforts to study the traffic performance of the employed strategies are still lacking. The LHOVRA technique is the predominant isolated traffic signal control strategy in Sweden. Past-end green was originally incorporated as part of LHOVRA (the “O” function) and was intended to reduce the number of vehicles in the dilemma zone. Coordinated signal control in Sweden is often fixed-time with local vehicle actuated signal timing adjustments and bus priority. This research study was undertaken to increase the knowledge of the traffic performance impacts of these strategies. The aim was to evaluate the following control strategies using Stockholm as a case study: 1. The LHOVRA technique with a focus on the “O” function; 2. Fixed time coordination (FTC); 3. Fixed time coordination with local signal timing adjustment (FTC-LTA); 4. FTC-LTA as above + active bus priority (PRIBUSS); 5. Self-optimizing control (SPOT). Field measurements were used for study of driver behavior and traffic impacts as well as for collecting input data needs for simulation. The results from low speed approaches showed a higher proportion of stopped vehicles after receiving green extension. Moving the detectors closer to the stop line, and/or making the detectors speed dependent were suggested as measures to solve these problems. The VISSIM simulation model calibrated and validated with empirical data was used to study traffic performance and safety impacts of the LHOVRA technique as well as to test the suggested improvements. The simulation experiment results from these design changes were shown to reduce accident risk with little or no loss of traffic performance. TRANSYT was used to produce optimized fixed signal timings for coordinated intersections. HUTSIM simulations showed that local signal timing adjustment by means of past-end green was beneficial when applied to coordinated traffic signal control in the study area. Both delays and stops were reduced, although not for the main, critical intersection which operated close to capacity. To study the impacts of strategies for coordinated signal control with bus priority, extensive field data collection was undertaken during separate time periods with these strategies in the same area using mobile and stationary techniques. A method to calculate the approach delay was developed based on the observed number of queuing vehicles at the start and end of green. Compared to FTC-LTA, the study showed that PRIBUSS reduced bus travel time. SPOT reduced both bus and vehicle travel time. Future research efforts for the development of signal control strategies and their implementation in Sweden should be focused on strategies with self-optimization functionality. / QC 20100408

The Type-II dilemma zone refers to the segment of roadway approaching an intersection where drivers have difficulty deciding to stop or proceed through at the onset of the circular yellow (CY) indication. Signalized intersection safety can be improved when the dilemma zone is correctly identified and steps are taken to reduce the likelihood that vehicles are caught in it. This research employs driving simulation as a means to collect driver response data at the onset of the CY indication to better understand and describe the dilemma zone. The data obtained was compared against that from previous experiments documented in the literature and the evidence suggests that driving simulator data is valid for describing driver behavior under the given conditions. Fuzzy logic was proposed as a tool to model driver behavior in the dilemma zone, and three such models were developed to describe driver behavior as it relates to the speed and position of the vehicle. These models were shown to be consistent with previous research on this subject and were able to predict driver behavior with up to 90% accuracy. / Graduation date: 2013