An Innovative Heuristic for Planning-Based Urban Traffic Control. / Franco, Santiago; Lindsay, Alan; Vallati, Mauro; McCluskey, Thomas Lee.

Computational Science - ICCS 2018: 18th International Conference, Wuxi, China, June 11–13, 2018, Proceedings, Part I. ed. / Yong Shi; Haohuan Fu; Yingjie Tian; Valeria V. Krzhizhanovskaya; Michael Harold Lees; Jack J. Dongarra; Peter M. A. Sloot. Springer, 2018. p. 181-193 (Lecture Notes in Computer Science; Vol. 10860).

Research output: Chapter in Book/Report/Conference proceedingConference contribution



The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road network is a pivotal challenge, particularly in the case of unexpected situations. In order to tackle this challenge, approaches based on mixed discrete-continuous planning have been recently proposed and although their feasibility has been demonstrated, there is a lack of informative heuristics for this class of applications. Therefore, existing approaches tend to provide low-quality solutions, leading to a limited impact of generated plans on the actual urban infrastructure.

In this work, we introduce the Time-Based heuristic: a highly informative heuristic for PDDL+ planning-based urban traffic control. The heuristic, which has an admissible and an inadmissible variant, has been evaluated considering scenarios that use real-world data.
Original languageEnglish
Title of host publicationComputational Science - ICCS 2018
Subtitle of host publication18th International Conference, Wuxi, China, June 11–13, 2018, Proceedings, Part I
EditorsYong Shi, Haohuan Fu, Yingjie Tian, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Jack J. Dongarra, Peter M. A. Sloot
Number of pages13
ISBN (Electronic)978-3-319-93698-7
ISBN (Print)978-3-319-93697-0
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 38237588