Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles

• Published in: Networks and spatial economics 2018-12, Vol.18 (4), p.875-908
• Main Authors: Zhuge, Chengxiang, Shao, Chunfu
• Format: Article
• Language: English
• Subjects: Agent-based models; Charging; Civil Engineering; Computer simulation; Demand; Economic activity; Economics; Economics and Finance; Electric vehicles; Land use; Links; Mathematical models; Model testing; Multiagent systems; Operations Research/Decision Theory; Parameter sensitivity; Parking; Public transportation; Regional/Spatial Science; Sensitivity analysis; Stations
• ISSN: 1566-113X; 1572-9427
• DOI: 10.1007/s11067-018-9412-3

This paper proposes an agent-based transport facility development model for both Conventional Vehicles (CVs) and Electric Vehicles (EVs), as a key component of an agent-based land use-transport model, SelfSim. The model attempts to simultaneously locate public parking lots, refuelling stations, charging stations and charging posts at parking lots with the consideration of competitions and interactions between the facilities. The facility development model is composed of a link-based model and node-based model that are used to simulate the development of link-based (e.g., replenishing stations) and node-based facilities (e.g., parking lots), respectively, based on the spatial and temporal disaggregate demand. The demand is extracted from the activity-based simulation with MATSim-EV that is an EV extension of MATSim (Multi-Agent Transport Simulation). In the model, facility agents are defined with several specific attributes and behavioural rules, and act the role of locating transport facilities to accommodate the demand. Finally, both global and local sensitivity analyses are applied to fully test the model in several experiments set up based on a Chinese medium-sized city, Baoding. The global SA that is based on Elementary Effect Method is firstly applied to quantify the extent to which the twelve model outputs of interest are sensitive to forty key model parameters, resulting in nine significantly important parameters; Then the Once-At-A-Time (OAT)-based local SA is used to provide further insight into how these important parameters influence the model outputs of interest over years. The SA results are expected to be useful for model calibration, and how the SA results can be used to calibrate the model is discussed.