Abstract
We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations.
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Notes
- 1.
Restricting origins/destinations of journeys to road junctions is without loss of generality since fictitious nodes that serve the sole purpose of acting as starting/ending point of a journey can always be created by edge splitting operations.
- 2.
Sometimes also referred to as dynamic flow in the literature. We prefer the term flow over time as the adjective dynamic has often been used in many algorithmic settings to refer to problems where the input data arrive online or change over time. We assume that all the agents are present at time \(t=0\) and the network is cleared after the last agent reaches their destination.
- 3.
We do not prevent agents from using edges other than the ones belonging to their assigned paths, as doing so would result in a waste of public resources (i.e., road capacity). To avoid congestion, though, we assume that agents not following their assigned route can be disincentivized from using an edge that, according to the scheduled traffic, is filled to capacity. This can be easily implemented in a smart traffic control system through the use of traffic cameras that check cars’ number plates.
- 4.
A graph is K-edge-connected if it remains connected when strictly fewer than K edges are removed.
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Serafino, P., Ventre, C., Tran-Thanh, L., Zhang, J., An, B., Jennings, N. (2019). Social Cost Guarantees in Smart Route Guidance. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_37
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