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Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement
Article

Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows

Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Thanikanti Sudhakar Babu
Sustainability 2022, 14(2), 932; https://doi.org/10.3390/su14020932
Received: 1 December 2021 / Revised: 5 January 2022 / Accepted: 11 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway. View Full-Text
Keywords: variable speed limit; electric vehicles; connected and autonomous vehicles; reinforcement learning; urban motorway; intelligent transportation systems; fuel consumption; exhaust gas emissions variable speed limit; electric vehicles; connected and autonomous vehicles; reinforcement learning; urban motorway; intelligent transportation systems; fuel consumption; exhaust gas emissions
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MDPI and ACS Style

Vrbanić, F.; Miletić, M.; Tišljarić, L.; Ivanjko, E. Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows. Sustainability 2022, 14, 932. https://doi.org/10.3390/su14020932

AMA Style

Vrbanić F, Miletić M, Tišljarić L, Ivanjko E. Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows. Sustainability. 2022; 14(2):932. https://doi.org/10.3390/su14020932

Chicago/Turabian Style

Vrbanić, Filip, Mladen Miletić, Leo Tišljarić, and Edouard Ivanjko. 2022. "Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows" Sustainability 14, no. 2: 932. https://doi.org/10.3390/su14020932

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