Vehicle Routing Simulation for Prediction of Commuter's Behaviour

We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in the long-term perspective from both, individual experience, and widely available social knowledge. The approach is illustrated as practical application to modeling commuter behavior in the city of Winnipeg, Manitoba, Canada. All simulations in the paper are fully reproducible as they have been carried out by utilizing a set of opensource libraries and tools that we have developed for the Julia programming language and that are openly available on GitHub.

Automated summary

This paper proposes a multiagent, large-scale vehicle routing modeling framework to simulate transportation systems. The goals are to investigate the interaction between individual and social knowledge and their impact on traffic flow, and to evaluate the effects of different discrete-event simulation designs. The approach combines efficient discrete-event modeling with intelligent drivers who can learn from individual experience and widely available social knowledge. It is applied to modeling commuter behavior in Winnipeg, Manitoba, Canada.