An agent-based simulation approach for dual toll pricing of hazardous material transportation

Abstract

A dual toll pricing is a conceptual policy in which policy maker imposes toll on both hazardous materials (hazmat) vehicles as well as regular vehicles for using populated road segments to mitigate a risk of hazmat transportation. It intends to separate the hazmat traffic flow from the regular traffic flow via controlling the dual toll. In order to design the dual toll pricing policy on a highly realistic road network environment and detailed human behaviors, an extended BDI framework is employed to mimic human decision behaviors in great detail. The proposed approach is implemented in AnyLogic ® agent based simulation software with using a traffic data of Albany, NY. Also, search algorithms in OptQuest ® are used to determine the optimum dual toll pricing policy which results in the minimum risk and travel cost based on the simulation results. The result reveals the effectiveness of the proposed approach in devising a reliable policy under the realistic road network conditions.

Conformity checking of LPG Transportation Trucks by Modelling and Simulation

“…Furthermore, it is the first and the only dynamic simulation tool that gathers and combines these threedifferent modelling-simulation approaches: System Dynamics, Discrete event, and Multi-Agent Systems (MAS). AnyLogic is the most suitable platform for modelling industrial problems especially the one of the supply chain [3][4][5][6][7].…”

Drivers' en-route divergence behavior modeling using Extended Belief-Desire-Intention (E-BDI) framework

“…Besides, instead of generating the optimum route (e.g., the shortest route), PDFS algorithm creates the most probable route in terms of preferences on attributes (e.g., travel time and travel time variation) based on driver's perceived information and knowledge about a road network. Thus, drivers' realistic en-route planning behaviors can be represented (Kim, Mungle, and Son 2013). Figure 5 shows the pseudo code of the PDFS algorithm.…”

“…Even though drivers have same knowledge about a road network, each individual driver may have different route plan from other drivers. This is because each individual driver has different preference on Kim, Son, Tian and Chiu route planning, and some drivers may consider multiple attributes to select a route (e.g., minimize travel time and risk of car incidents) (Kim, Mungle, and Son 2013).…”