Negotiation and Coordination in Carpooling: Agent-Based Simulation Model

Authored by Luk Knapen, Davy Janssens, Geert Wets, Tom Bellemans, Iftikhar Hussain, Ansar-UI-Haque Yasar

Date Published: 2016

DOI: 10.3141/2542-11

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Carpooling enables commuters to share travel expenses, save costs, and improve their mobility options and reduces emission and traffic congestion. To commute by carpooling, individuals need to communicate, negotiate, and coordinate, and in most cases they need to adapt their schedule to enable cooperation. This paper presents the design of an agent-based model by defining phases and steps that may be taken to move from solo driving to carpooling. The paper analyzes the various effects of agent interaction and behavior adaptation for a set of candidate carpoolers. The start of the carpooling process depends on the individuals' objectives and intention to carpool. Through negotiation and coordination, individuals can reach complex agreements in an iterative way. The success of negotiation highly depends on the lifestyle factors that influence the departure time decision, on the profile of the individuals, and on the effect of constraining activities. The carpooling social network was established by use of the results predicted by FEATHERS, an operational activity based model for Flanders, Belgium. From the simulation's discussions, it is possible to portray the true picture of potential carpoolers throughout their carpooling period. The simulation results show that 9.33\% of the commuters started to carpool when the time window was +/- 30 min and the average occupancy per car was 2.4 persons. When the time window was larger, the chances for negotiation success were greater than those when a smaller time window was used. Hence, carpooling requires time flexibility. The Janus (multiagent) platform was used to simulate the interactions of autonomous agents.

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