Embedding Automated Planning within Urban Traffic Management Operations

Author:

McCluskey Thomas,Vallati Mauro

Abstract

This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network within some region of the city. We describe the resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an automated planning component to generate light signal control strategies in real time. This paper reports on the development surrounding the planning component, and in particular the engineering, configuration and validation issues that arose in the application. It discusses a range of lessons learned from the experience of deploying automated planning in the road transport area, under the direction of transport operators and technology developers.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Framework for Risk-Aware Routing of Connected Vehicles via Artificial Intelligence;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

2. In Defence of Good Old-Fashioned Artificial Intelligence Approaches in Intelligent Transportation Systems;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

3. An Efficient Heuristic for AI-based Urban Traffic Control;2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS);2023-06-14

4. Centralised Vehicle Routing for Optimising Urban Traffic: A Scalability Perspective;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04

5. Reformulation techniques for automated planning: a systematic review;The Knowledge Engineering Review;2023

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