Optimization of the cost of urban traffic through an online bidding platform for commuters

In this paper, we consider the problem of increasing efficiency of a transportation system through optimizing the behavior of commuters. The assumption is that the time spent in the traffic can be represented by a monetary value and hence introduction of monetary compensations can lead to a more efficient organization of the transportation system. In our model, heterogeneous travelers differently assess the value of their time spent in congestion, hence it is presumably viable to reduce traffic in the most congested streets by introducing a bidding mechanism that will allow the participants who have a lower monetary value of time (external link)  to receive a compensation financed by the group of commuters that have a higher value of time spend in congestion. We start by presenting a design of a bidding system for optimal allocation of traffic. We analyze the properties of the proposed algorithm and show that it leads to a more efficient allocation of vehicles than the theoretical allocation that could be achieved in the Nash Equilibrium of an uncontrolled transportation network. Subsequently, we verify the proposed auction design via an agent-based simulation model representing the Manhattan area of New York City. The results of our simulation confirm theoretical findings that the introduction of the proposed auction mechanism in a real city settings leads to a more efficient allocation of routes or means of transportation chosen by commuters.

Urban traffic optimization, Congestion, Smart vehicles, Dynamic route pricing, Bidding mechanism, Agent-based modeling

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This research was supported, in part, through generous contributions by NXM Labs Inc. (external link, opens in new window)