Abstract

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.

Highlights

  • Urban motorways are key traffic roads in routes that provide traffic capacity for transit and local traffic

  • The results were compared with the RB-variable speed limit (VSL) algorithm, which was modeled according to the highway capacity manual (HCM)

  • The results for QL-VSL with r total time spent (TTS) performed better regarding the reduction in TTS for scenarios with 10%, 30%, and 50% connected and autonomous vehicles (CAVs)-penetration rates, while QL-VSL with r total energy consumption (TEC) performed better in the rest of the scenarios, excluding the scenario with 100% CAVs

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Summary

Introduction

Urban motorways are key traffic roads in routes that provide traffic capacity for transit and local traffic They mainly consist of many closely spaced on- and off-ramps. The problem of urban motorways is derived from the ever-increasing traffic demand that often leads to congestion and capacity drops Such situations occur mainly near on-ramps caused by the local traffic that merges into the motorway mainstream flow during peak periods with increased traffic demand. This on-ramp traffic flow is characterized by a lower mean speed than the mainstream flow mean speed. A bottleneck is created that decreases the safety and stability of the motorway traffic flow [1] This disruption of Sustainability 2022, 14, 932.

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