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A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks

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arxiv 1401.5851 v1 pith:63E2WRZI submitted 2014-01-23 cs.GT cs.MA

classification cs.GTcs.MA
keywords managementtrafficnetworkroadtransportationurbaninfrastructureintersection
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Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed. In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservation-based intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.

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  1. An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

    cs.RO 2024-12 conditional novelty 5.0 of 10

    A collaborative MAPPO control method using compressed LiDAR feature sharing reduces simulated collision rates in occluded intersections compared to independent RL and early fusion baselines.

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