Cognition-based hierarchical en route planning for multi-agent traffic simulation |
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| Affiliation: | 1. Department of Engineering and Technology, Texas A&M University - Commerce, 2200 Campbell St, Commerce, TX 75429-3011, USA;2. Department of Systems and Industrial Engineering, University of Arizona, 1127 E. James E. Rogers Way, Room 111, Tucson, AZ 85721-0020, USA;3. Metropia, Inc., 3701 Executive Center Dr. STE 209, Austin, TX 78750, USA;4. Department of Civil Engineering and Engineering Mechanics, The University of Arizona, 1209 E. Second St., Room 206A, Tucson, AZ, USA;5. AAA Foundation for Traffic Safety, 601 14th Street, NW, Suite 201, Washington, DC 2005-2000, USA;1. Departamento de Arquitectura y Tecnología de Sistemas Informáticos (DATSI), Universidad Politécnica de Madrid, Campus Montegancedo S/N, 28660 Boadilla del Monte, Spain;2. Informática El Corte Inglés, Engineering and Telecommunication Division, Travesía de Costa Brava, 28034 Madrid, Spain;3. Phedes Lab, Calle Los Cedros 4, 33423 Soto de Llanera, Asturias, Spain;1. Decision Science Institute, School of Economics and Management, Fuzhou University, 2 Xueyuan road, Fuzhou 350116, China;2. School of Mathematics and Computer Science, Fuzhou University, 2 Xueyuan road, Fuzhou 350116, China;1. Moscow Institute of Physics and Technology, Russia;2. Computing Centre of the Russian Academy of Sciences, Russia;1. IT & Systems Area, Indian Institute of Management Kozhikode, 673570, Calicut, Kerala, India;2. Indian Institute of Information Technology and Management-Kerala, 695581, Trivandrum, India |
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| Abstract: | The goal of this study is to model drivers’ cognition-based en route planning behaviors in a large-scale road network via the Extended Belief-Desire-Intention (E-BDI) framework. E-BDI is a probabilistic behavior modeling framework based on agents’ own preferences of multiple attributes (e.g., travel time and its variance) and daily driving experiences. However, it is challenging to use the E-BDI framework for the demonstration of drivers’ en route planning behavior in a large-scale road network due to its high computational demand. To handle the computation issue, a hierarchical en route planning approach is proposed in this study. The proposed E-BDI-based en route planning approach consists of three major procedures: (1) network partitioning, (2) network aggregation, and (3) E-BDI-based en route planning. The Java-based E-BDI module integrated with DynusT® traffic simulation software is developed to demonstrate the proposed en route planning approach in Phoenix, Arizona road network involving 11,546 nodes and 24,866 links. The demonstration results reveal that the proposed approach is computationally efficient and effective in representing various en route planning behaviors of drivers in a large-scale road network. |
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