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Dynamic analysis of network flows under advanced information and control systems
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Recent doctoral dissertation
statistical data collection from installed cameras or videotape sequences, and automatic control unit or system for variable-message sign applications in tunnels and on motorways.
Dynamic analysis of network flows under advanced information and control systems. Hu, Ta-Yin, Ph.D. The Universityof Texas at Austin, 1995. 309 pp. Supervisor: Hani S. Mahmassani. Order Number DA9534809 This research addresses network flow analysis by considering individual tripmaker decision-making processes and their interactions in traffic networks under advanced information and control systems. A day-to-day dynamic conceptual framework is proposed to mode1 the traffic system in the context of ATWATMS from the perspective of the individual tripmaker. In this framework, two levels of tripmaker decision-making processes are identified: day-to-day dynamics and real-time dynamics. Day-to-day dynamics consider tripmakers’ choices of departure time and route according to indifference bands of tolerable “schedule delay” defined as the difference between the user’s actual and preferred arrival times; real-time dynamics consider tripmakers’ en-route switching decisions in response to prevailing traffic conditions. The simulation-assignment model. DYNASMART, is developed to evaluate collective behavior resulting from individual trip decisions and traffic control strategies in a traffic system under advanced information systems. One of the key features of DYNASMART is its ability to model vehicle paths in the network as the explicit outcome of individual route choice decisions at origins or at nodes of the network. Traffic flow is represented using a hybrid approach where vehicles are tracked individually or in macro particles, and moved consistently with macroscopic traffic flow relations. Junction delays are explicitly modeled. Vehicles are routed in the network according to different rules depending on information availability. A day-to-day dynamic simulation-assignment procedure, constructed based on the framework and DYNASMART, is developed to study the evolution of daily network flows. In this procedure, tripmakers’ choices of departure time and route initially determine the temporal and spatial distribution of network flows; however, such flows are affected by tripmakers’ learning processes and en-route decisions from day to day. Numerical experiments are conducted to investigate time-dependent flow patterns, real-time dynamics, and day-to-day dynamics under a wide variety of information supply and signal control strategies, and to assess the effectiveness of such strategies in a dynamic perspective.
Dynamic estimation of travel time on arterial roads by using [an] automatic vehicle location (AVL) bus as a vehicle probe. Bae, Sanghoon, Ph.D. Virginia Polytechnic Institute and State University, 1995. 314. Chairman: Antoine G. Hobeika. Order Number DA%29838
This research focuses on the use of an Automatic Vehicle Location (AVL) system-equipped bus as a probe vehicle for estimating bus arrival times and auto travel times. Since many transit organizations throughout North America are currently operating these AVL buses on their bus routes, in a sense, this technique is a cost-effective traffic probe which can be utilized for practical, proactive, data collection. The goals of this research are to enhance the current use of AVL systems by introducing a new module to estimate bus arrival time information for transit travelers, and to use an AVL systems-equipped bus as a probe vehicle to estimate the non-transit travel time for auto travelers. The initial objective for the first goal was to model and simulate dynamic bus behavior at single and multiple bus stops using time-varying passenger arrival and boarding rates. Then, a prototype arrival time estimation model was simulated by adopting the Parameter Adaptation Algorithm (real-time identification). Later in the research, a dynamic link travel time function was developed by dividing the conventional arterial link into two regions. An integrated travel time estimation model, which combines the prototype arrival time estimation model and dynamic link travel time function, was developed and validated. Three main tasks were conducted in the development of an integrated travel-time estimation model: three stops-ahead link travel time estimation, dynamic link flow estimation and on-line parameter estimation. To fulfill the second goal, regression analysis was used to identify the correlation between bus travel time and auto travel time. Another modeling technique, Artificial Neural Networks (ANN), was applied to interpret auto travel time directly from bus travel time. A multilayer perceptron, adopting a supervised backpropagation learning algorithm. was constructed to map the bus travel time to auto travel time. Dynamic and static data which affect the travel time of bus and auto were collected by field observations from Blacksburg and Norfolk, Virginia and used to validate the auto travel time prediction model. ANN results outperformed the regression analysis.
Evaluation of automatic vehicle specific identification (AVSI) in a traffic signal control system. Kamyab, Alireza, Ph.D. Iowa State University, 1995. 175 pp. Major Professors: Thomas H. Maze; Thomas A. Barta. Order Number DA9531753 Automatic Vehicle-Specific Identification (AVSI) is a generic name for advanced vehicle detection systems. By automating the identification of vehicles by sensing the presence of vehicles with roadside detection sites or readers, AVSI is assumed to provide vehicle-specific information in traffic signal control systems. In the application of AVSI to traffic signal control systems, as a vehicle passes a reader site, the reader records the arrival time and type of the detected vehicle. The reader then sends the information received to a local
Recent doctoral dissertation
statistical data collection from installed cameras or videotape sequences, and automatic control unit or system for variable-message sign applications in tunnels and on motorways.
Dynamic analysis of network flows under advanced information and control systems. Hu, Ta-Yin, Ph.D. The Universityof Texas at Austin, 1995. 309 pp. Supervisor: Hani S. Mahmassani. Order Number DA9534809 This research addresses network flow analysis by considering individual tripmaker decision-making processes and their interactions in traffic networks under advanced information and control systems. A day-to-day dynamic conceptual framework is proposed to mode1 the traffic system in the context of ATWATMS from the perspective of the individual tripmaker. In this framework, two levels of tripmaker decision-making processes are identified: day-to-day dynamics and real-time dynamics. Day-to-day dynamics consider tripmakers’ choices of departure time and route according to indifference bands of tolerable “schedule delay” defined as the difference between the user’s actual and preferred arrival times; real-time dynamics consider tripmakers’ en-route switching decisions in response to prevailing traffic conditions. The simulation-assignment model. DYNASMART, is developed to evaluate collective behavior resulting from individual trip decisions and traffic control strategies in a traffic system under advanced information systems. One of the key features of DYNASMART is its ability to model vehicle paths in the network as the explicit outcome of individual route choice decisions at origins or at nodes of the network. Traffic flow is represented using a hybrid approach where vehicles are tracked individually or in macro particles, and moved consistently with macroscopic traffic flow relations. Junction delays are explicitly modeled. Vehicles are routed in the network according to different rules depending on information availability. A day-to-day dynamic simulation-assignment procedure, constructed based on the framework and DYNASMART, is developed to study the evolution of daily network flows. In this procedure, tripmakers’ choices of departure time and route initially determine the temporal and spatial distribution of network flows; however, such flows are affected by tripmakers’ learning processes and en-route decisions from day to day. Numerical experiments are conducted to investigate time-dependent flow patterns, real-time dynamics, and day-to-day dynamics under a wide variety of information supply and signal control strategies, and to assess the effectiveness of such strategies in a dynamic perspective.
Dynamic estimation of travel time on arterial roads by using [an] automatic vehicle location (AVL) bus as a vehicle probe. Bae, Sanghoon, Ph.D. Virginia Polytechnic Institute and State University, 1995. 314. Chairman: Antoine G. Hobeika. Order Number DA%29838
This research focuses on the use of an Automatic Vehicle Location (AVL) system-equipped bus as a probe vehicle for estimating bus arrival times and auto travel times. Since many transit organizations throughout North America are currently operating these AVL buses on their bus routes, in a sense, this technique is a cost-effective traffic probe which can be utilized for practical, proactive, data collection. The goals of this research are to enhance the current use of AVL systems by introducing a new module to estimate bus arrival time information for transit travelers, and to use an AVL systems-equipped bus as a probe vehicle to estimate the non-transit travel time for auto travelers. The initial objective for the first goal was to model and simulate dynamic bus behavior at single and multiple bus stops using time-varying passenger arrival and boarding rates. Then, a prototype arrival time estimation model was simulated by adopting the Parameter Adaptation Algorithm (real-time identification). Later in the research, a dynamic link travel time function was developed by dividing the conventional arterial link into two regions. An integrated travel time estimation model, which combines the prototype arrival time estimation model and dynamic link travel time function, was developed and validated. Three main tasks were conducted in the development of an integrated travel-time estimation model: three stops-ahead link travel time estimation, dynamic link flow estimation and on-line parameter estimation. To fulfill the second goal, regression analysis was used to identify the correlation between bus travel time and auto travel time. Another modeling technique, Artificial Neural Networks (ANN), was applied to interpret auto travel time directly from bus travel time. A multilayer perceptron, adopting a supervised backpropagation learning algorithm. was constructed to map the bus travel time to auto travel time. Dynamic and static data which affect the travel time of bus and auto were collected by field observations from Blacksburg and Norfolk, Virginia and used to validate the auto travel time prediction model. ANN results outperformed the regression analysis.
Evaluation of automatic vehicle specific identification (AVSI) in a traffic signal control system. Kamyab, Alireza, Ph.D. Iowa State University, 1995. 175 pp. Major Professors: Thomas H. Maze; Thomas A. Barta. Order Number DA9531753 Automatic Vehicle-Specific Identification (AVSI) is a generic name for advanced vehicle detection systems. By automating the identification of vehicles by sensing the presence of vehicles with roadside detection sites or readers, AVSI is assumed to provide vehicle-specific information in traffic signal control systems. In the application of AVSI to traffic signal control systems, as a vehicle passes a reader site, the reader records the arrival time and type of the detected vehicle. The reader then sends the information received to a local