Mitigating Congestion at Sags with Adaptive Cruise Control Systems
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
Sags are segments of the road where there is a significant change in gradient from downhill to uphill in a short distance. Empirically, it has been observed that drivers do not compensate adequately for the changing grade resistance force at sags, which limits vehicle acceleration. As a consequence, congestion forms at sags. This paper proposes and compares the implementation of Adaptive Cruise Control (ACC), Traffic State-Adaptive ACC (TSA-ACC), and Cooperative ACC (CACC) at sags. The controllers are implemented in a traffic simulation environment with a single-lane road stretch and a platoon of 200 vehicles of which 10% follow the movements set by the controller and 90% follow the normal driving rules. The simulation results show that all controllers reduce the travel time of the equipped vehicles and have significantly positive effects on the total travel time of the platoon. TSA-ACC produces the most travel time savings. The findings of this paper show that the proposed controllers can be used to improve the performance of traffic flow at sags.
Details
Originalsprache | Englisch |
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Titel | Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 2451-2457 |
Seitenumfang | 7 |
ISBN (elektronisch) | 9781467365956, 978-1-4673-6596-3 |
Publikationsstatus | Veröffentlicht - 30 Okt. 2015 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Publikationsreihe
Reihe | International Conference on Intelligent Transportation (ITSC) |
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ISSN | 2153-0009 |
Konferenz
Titel | 2015 18th IEEE International Conference on Intelligent Transportation Systems |
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Kurztitel | ITSC 2015 |
Veranstaltungsnummer | 18 |
Dauer | 15 - 18 September 2015 |
Stadt | Las Palmas de Gran Canaria |
Land | Spanien |
Externe IDs
ORCID | /0000-0001-6555-5558/work/171064732 |
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