scholarly journals Trip Cost Estimation of Connected Autonomous Vehicle Mixed Traffic Flow in a Two-Route Traffic Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhizhen Liu ◽  
Hong Chen ◽  
Hengrui Chen ◽  
Xiaoke Sun ◽  
Qi Zhang
Keyword(s):  
Bounded Rationality ◽  
Traffic Flow ◽  
Cost Estimation ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Flow Simulation ◽  
Mixed Traffic ◽  
Traffic Condition ◽  
Mixed Traffic Flow ◽  
Traffic Flow Simulation

With the advancement of connected autonomous vehicle (CAV) technology, research on future traffic conditions after the popularization of CAVs needs to be resolved urgently. Bounded rationality of human drivers is essential for simulating traffic flow precisely, but few studies focus on the traffic flow simulation considered bounded rationality in CAV mixed traffic flow. In this study, we introduce random bounded rationality into the hybrid feedback strategy (HFS) under CAV mixed traffic flow to explore the impacts of CAV penetration rate on the trip cost of vehicles. First, we investigated the bounded rationality of drivers, and we found that it follows normal contribution. Then, we proposed HFS considering random bounded rationality and the CAV penetration rate to simulate the traffic condition. The numerical results show that the enhancement of the CAV penetration rate could reduce total trip cost. The research could help us to simulate the CAVs mixed traffic flow more precisely and realistically.

Development and Application of CHANSIGNAL Signal Timing Software System

2011 ◽  
Vol 135-136 ◽  
pp. 436-442
Author(s):  
Hong Chen ◽  
Ji Biao Zhou ◽  
Juan Sun
Keyword(s):  
Traffic Flow ◽  
Simulation Analysis ◽  
High Accuracy ◽  
Urban Traffic ◽  
Signal Timing ◽  
Mixed Traffic ◽  
Traffic Condition ◽  
Mixed Traffic Flow ◽  
Timing Model ◽  
Timing System

Considering the mixed traffic flow of current urban traffic condition in our country, the paper develops the CHANSIGNAL signal timing system based on the improved TRRL signal timing model. Through simulation analysis and case verification, the system presents the advantages of high accuracy, calculating speed in signal timing for intersection. The system significantly improves the traffic operating condition, which proves the system’s validity and practicality.

scholarly journals Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanqiu Cheng ◽  
Chenxi Chen ◽  
Xianbiao Hu ◽  
Kuanmin Chen ◽  
Qing Tang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Trajectory Planning ◽  
Autonomous Vehicle ◽  
Planning Problem ◽  
Mixed Traffic ◽  
Benchmark Model ◽  
Mixed Traffic Flow ◽  
Tree Expansion ◽  
Study Results

The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CPI) is defined to quantitively measure the safety performance of the mixed traffic flow. The CAV trajectory planning problem is firstly formulated as an optimization model; then, the solution procedure based on reinforcement learning is proposed. The tree-expansion determination module and rollout termination module are developed to identify and reduce the unnecessary tree expansion, so as to train the model more efficiently towards the desired direction. The case study results showed that the proposed algorithm was able to reduce the CPI by 76.56%, when compared with a benchmark model without any intelligence, and 12.08%, when compared with another benchmark model that the team developed earlier. These results demonstrated the satisfactory performance of the proposed algorithm in enhancing the safety of the mixed traffic flow.

scholarly journals Fuel Consumption and Traffic Emissions Evaluation of Mixed Traffic Flow with Connected Automated Vehicles at Multiple Traffic Scenarios

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Bin Zhao ◽  
Yalan Lin ◽  
Huijun Hao ◽  
Zhihong Yao
Keyword(s):  
Fuel Consumption ◽  
Traffic Flow ◽  
Penetration Rate ◽  
Traffic Emissions ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Reduce Fuel Consumption ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Connected Automated Vehicles

To analyze the impact of different proportions of connected automated vehicles (CAVs) on fuel consumption and traffic emissions, this paper studies fuel consumption and traffic emissions of mixed traffic flow with CAVs at different traffic scenarios. Firstly, the car-following modes and proportional relationship of vehicles in the mixed traffic flow are analyzed. On this basis, different car-following models are applied to capture the corresponding car-following modes. Then, Virginia Tech microscopic (VT-micro) model is adopted to calculate the instantaneous fuel consumption and traffic emissions. Finally, based on three typical traffic scenarios, a basic segment with bottleneck zone, ramp of the freeway, and signalized intersection, a simulation platform is built based on Python and SUMO to obtain vehicle trajectory data, and the fuel consumption and traffic emissions in different scenarios are obtained. The results show that (1) In different traffic scenarios, the application of CAVs can reduce fuel consumption and traffic emissions. The higher the penetration rate, the more significant the reduction in fuel consumption and traffic emissions. (2) In the three typical traffic scenarios, the advantages of CAVs are more evident in the signalized intersection. When the penetration rate of CAVs is 100%, the fuel consumption and traffic emissions reduction ratio is as high as 32%. It is noteworthy that the application of CAVs in urban transportation will significantly reduce fuel consumption and traffic emissions.

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