A DDDAS-Based Multi-Scale Framework for Pedestrian Behavior Modeling and Interactions with Drivers

Persistent Link:
http://hdl.handle.net/10150/306361
Title:
A DDDAS-Based Multi-Scale Framework for Pedestrian Behavior Modeling and Interactions with Drivers
Author:
Xi, Hui
Issue Date:
2013
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
A multi-scale agent-based simulation framework is firstly proposed to analyze pedestrian delays at signalized crosswalks in large urban areas under different conditions. The aggregated-level model runs under normal conditions, where each crosswalk is represented as an agent. Pedestrian counts collected near crosswalks are utilized to derive the binary choice probability from a utility maximization model. The derived probability function is utilized based on the extended Adam's model to estimate an average pedestrian delay with corresponding traffic flow rate and traffic light control at each crosswalk. When abnormality is detected, the detailed-level model with each pedestrian as an agent is running in the affected subareas. Pedestrian decision-making under abnormal conditions, physical movement, and crowd congestion are considered in the detailed-level model. The detailed-level model contains two sub-level models: the tactical sub-level model for pedestrian route choice and the operational sub-level model for pedestrian physical interactions. The tactical sub-level model is based on Extended Decision Field Theory (EDFT) to represent the psychological preferences of pedestrians with respect to different route choice options during their deliberation process after evaluating current surroundings. At the operational sub-level model, physical interactions among pedestrians and consequent congestions are represented using a Cellular Automata model, in which pedestrians are allowed biased random-walking without back step towards their destination that has been given by the tactical sub-level model. In addition, Dynamic-Data-Driven Application Systems (DDDAS) architecture has been integrated with the proposed multi-scale simulation framework for an abnormality detection and appropriate fidelity selection (between the aggregate level and the detailed level models) during the simulation execution process. Various experiments have been conducted under varying conditions with the scenario of a Chicago Loop area to demonstrate the advantage of the proposed framework, balancing between computational efficiency and model accuracy. In addition to the signalized intersections, pedestrian crossing behavior under unsignalized conditions which has been recognized as a main reason for pedestrian-vehicle crashes has also been analyzed in this dissertation. To this end, an agent-based model is proposed to mimic pedestrian crossing behavior together with drivers' yielding behavior in the midblock crossing scenario. In particular, pedestrian-vehicle interaction is first modeled as a Two-player Pareto game which develops evaluation of strategies from two aspects, delay and risk, for each agent (i.e. pedestrian and driver). The evaluations are then used by Extended Decision Field Theory to mimic decision making of each agent based on his/her aggressiveness and physical capabilities. A base car-following algorithm from NGSIM is employed to represent vehicles' physical movement and execution of drivers' decisions. A midblock segment of a typical arterial in the Tucson area is adopted to illustrate the proposed model, and the model for the considered scenario has been implemented in AnyLogic® simulation software. Using the constructed simulation, experiments have been conducted to analyze different behaviors of pedestrians and drivers and the mutual impact upon each other, i.e. average pedestrian delay resulted from different crossing behaviors (aggressive vs. conservative), and average braking distance which is affected by driving aggressiveness and drivers' awareness of pedestrians. The results look interesting and are believed to be useful for improvement of pedestrians' safety during their midblock crossing. To the best of our knowledge, the proposed multi-scale modeling framework for pedestrians and drivers is one of the first efforts to estimate pedestrian delays in an urban area with adaptive resolution based on demand and accuracy requirement, as well as to address pedestrian-vehicle interactions under unsignalized conditions.
Type:
text; Electronic Dissertation
Keywords:
decision making; interaction; pedestrian behavior; Systems & Industrial Engineering; crowd modeling
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems & Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Son, Young-Jun

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA DDDAS-Based Multi-Scale Framework for Pedestrian Behavior Modeling and Interactions with Driversen_US
dc.creatorXi, Huien_US
dc.contributor.authorXi, Huien_US
dc.date.issued2013-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractA multi-scale agent-based simulation framework is firstly proposed to analyze pedestrian delays at signalized crosswalks in large urban areas under different conditions. The aggregated-level model runs under normal conditions, where each crosswalk is represented as an agent. Pedestrian counts collected near crosswalks are utilized to derive the binary choice probability from a utility maximization model. The derived probability function is utilized based on the extended Adam's model to estimate an average pedestrian delay with corresponding traffic flow rate and traffic light control at each crosswalk. When abnormality is detected, the detailed-level model with each pedestrian as an agent is running in the affected subareas. Pedestrian decision-making under abnormal conditions, physical movement, and crowd congestion are considered in the detailed-level model. The detailed-level model contains two sub-level models: the tactical sub-level model for pedestrian route choice and the operational sub-level model for pedestrian physical interactions. The tactical sub-level model is based on Extended Decision Field Theory (EDFT) to represent the psychological preferences of pedestrians with respect to different route choice options during their deliberation process after evaluating current surroundings. At the operational sub-level model, physical interactions among pedestrians and consequent congestions are represented using a Cellular Automata model, in which pedestrians are allowed biased random-walking without back step towards their destination that has been given by the tactical sub-level model. In addition, Dynamic-Data-Driven Application Systems (DDDAS) architecture has been integrated with the proposed multi-scale simulation framework for an abnormality detection and appropriate fidelity selection (between the aggregate level and the detailed level models) during the simulation execution process. Various experiments have been conducted under varying conditions with the scenario of a Chicago Loop area to demonstrate the advantage of the proposed framework, balancing between computational efficiency and model accuracy. In addition to the signalized intersections, pedestrian crossing behavior under unsignalized conditions which has been recognized as a main reason for pedestrian-vehicle crashes has also been analyzed in this dissertation. To this end, an agent-based model is proposed to mimic pedestrian crossing behavior together with drivers' yielding behavior in the midblock crossing scenario. In particular, pedestrian-vehicle interaction is first modeled as a Two-player Pareto game which develops evaluation of strategies from two aspects, delay and risk, for each agent (i.e. pedestrian and driver). The evaluations are then used by Extended Decision Field Theory to mimic decision making of each agent based on his/her aggressiveness and physical capabilities. A base car-following algorithm from NGSIM is employed to represent vehicles' physical movement and execution of drivers' decisions. A midblock segment of a typical arterial in the Tucson area is adopted to illustrate the proposed model, and the model for the considered scenario has been implemented in AnyLogic® simulation software. Using the constructed simulation, experiments have been conducted to analyze different behaviors of pedestrians and drivers and the mutual impact upon each other, i.e. average pedestrian delay resulted from different crossing behaviors (aggressive vs. conservative), and average braking distance which is affected by driving aggressiveness and drivers' awareness of pedestrians. The results look interesting and are believed to be useful for improvement of pedestrians' safety during their midblock crossing. To the best of our knowledge, the proposed multi-scale modeling framework for pedestrians and drivers is one of the first efforts to estimate pedestrian delays in an urban area with adaptive resolution based on demand and accuracy requirement, as well as to address pedestrian-vehicle interactions under unsignalized conditions.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectdecision makingen_US
dc.subjectinteractionen_US
dc.subjectpedestrian behavioren_US
dc.subjectSystems & Industrial Engineeringen_US
dc.subjectcrowd modelingen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems & Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSon, Young-Junen_US
dc.contributor.committeememberSon, Young-Junen_US
dc.contributor.committeememberLin, Wei Huaen_US
dc.contributor.committeememberLiu, Jianen_US
dc.contributor.committeememberWeisband, Suzanne P.en_US
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