Ozkan, Mustafa Can;
Cheng, Tao;
(2023)
Finding Feasible Routes with Reinforcement
Learning Using Macro-Level Traffic Measurements.
In: Beecham, Roger and Long, Jed A and Smith, Dianna and Zhao, Qunshan and Wise, Sarah, (eds.)
12th International Conference on Geographic Information Science (GIScience 2023).
(pp. 58:1-58:6).
Dagstuhl Publishing: Wadern, Germany.
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Abstract
The quest for identifying feasible routes holds immense significance in the realm of transportation, spanning a diverse range of applications, from logistics and emergency systems to taxis and public transport services. This research area offers multifaceted benefits, including optimising traffic management, maximising traffic flow, and reducing carbon emissions and fuel consumption. Extensive studies have been conducted to address this critical issue, with a primary focus on finding the shortest paths, while some of them incorporate various traffic conditions such as waiting times at traffic lights and traffic speeds on road segments. In this study, we direct our attention towards historical data sets that encapsulate individuals’ route preferences, assuming they encompass all traffic conditions, real-time decisions and topological features. We acknowledge that the prevailing preferences during the recorded period serve as a guide for feasible routes. The study’s noteworthy contribution lies in our departure from analysing individual preferences and trajectory information, instead focusing solely on macro-level measurements of each road segment, such as traffic flow or traffic speed. These types of macro-level measurements are easier to collect compared to individual data sets. We propose an algorithm based on Q-learning, employing traffic measurements within a road network as positive attractive rewards for an agent. In short, observations from macro-level decisions will help us to determine optimal routes between any two points. Preliminary results demonstrate the agent’s ability to accurately identify the most feasible routes within a short training period.
| Type: | Proceedings paper |
|---|---|
| Title: | Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements |
| Event: | International Conference on Geographic Information Science (GIScience 2023) |
| ISBN-13: | 978-3-95977-288-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.4230/LIPIcs.GIScience.2023.58 |
| Publisher version: | https://doi.org/10.4230/LIPIcs.GIScience.2023.58 |
| Language: | English |
| Additional information: | Copyright © Mustafa Can Ozkan and Tao Cheng; licensed under Creative Commons License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Routing, reinforcement learning, q-learning, data mining, macro-level patterns |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10179320 |
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