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A novel agent-based transportation model of a university campus with application to quantifying the environmental cost of parking search

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  • Guo, Liya
  • Huang, Shan
  • Sadek, Adel W.

Abstract

This paper develops a novel agent-based transportation model of a university campus, primarily focusing on vehicle-related travel and the associated parking search process. In developing and validating the model, the study uses a wide range of data sources including: (1) a brief “trip-diary” type survey; (2) 24-h traffic counts at the entry and exit points to the campus; (3) information about the university buildings’ class room capacities and class schedules; (4) parking occupancy surveys; and (5) select intersections’ turn movement counts. The agent-based model is designed to explicitly capture trip chaining behavior, and the often-overlooked phenomenon of drivers searching for an available parking spot. The parking search process is modeled using a sequential game-theoretic, neo-additive capacity model which accounts for drivers optimistic and pessimistic attitudes regarding parking availability in their most desirable lot. The agent-based demand model is then integrated with the Transportation Analysis and Simulation System (TRANSIMS), which serves as the traffic micro-simulation engine, and with the MOVES2010 emissions model. Following the validation of the integrated model, it is used to quantify the environmental cost of the parking search process on campus. The study may be regarded as one of the few studies to integrate an agent- or activity based model of travel demand, albeit admittedly simplified, with a fine-grained transportation network, a detailed traffic micro-simulation, and a project-level emissions model. Another contribution of the study is in terms of quantifying the environmental cost, in terms of wasted fuel and increased emissions, associated with the parking search process on campus.

Suggested Citation

  • Guo, Liya & Huang, Shan & Sadek, Adel W., 2013. "A novel agent-based transportation model of a university campus with application to quantifying the environmental cost of parking search," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 86-104.
    • Handle: RePEc:eee:transa:v:50:y:2013:i:c:p:86-104
    DOI: 10.1016/j.tra.2013.01.045
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    References listed on IDEAS

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    Cited by:

    1. Nourinejad, Mehdi & Roorda, Matthew J., 2017. "Impact of hourly parking pricing on travel demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 28-45.
    2. Hamad, Khaled & Obaid, Lubna, 2022. "Tour-based travel demand forecasting model for a university campus," Transport Policy, Elsevier, vol. 117(C), pages 118-137.
    3. Chidambaram, Bhuvanachithra & Janssen, Marco A. & Rommel, Jens & Zikos, Dimitrios, 2014. "Commuters’ mode choice as a coordination problem: A framed field experiment on traffic policy in Hyderabad, India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 9-22.
    4. Lewe, J.-H. & Hivin, L.F. & Mavris, D.N., 2014. "A multi-paradigm approach to system dynamics modeling of intercity transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 188-202.
    5. Chai, Huajun, 2019. "Dynamic Traffic Routing and Adaptive Signal Control in a Connected Vehicles Environment," Institute of Transportation Studies, Working Paper Series qt9ng3z8vn, Institute of Transportation Studies, UC Davis.
    6. Cao, Jin & Menendez, Monica, 2018. "Quantification of potential cruising time savings through intelligent parking services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 151-165.

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