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An Assessment of Age and Gender Characteristics of Mixed Traffic with Autonomous and Manual Vehicles: A Cellular Automata Approach

Author

Listed:
  • Muhammad Tanveer

    (Department of Civil Engineering, Tsinghua University, Beijing 100084, China)

  • Faizan Ahmad Kashmiri

    (Department of Civil Engineering, University of Management and Technology, Lahore 54770, Pakistan)

  • Hassan Naeem

    (Lahore Transport Company (LTC), Lahore 54000, Pakistan)

  • Huimin Yan

    (Department of Civil Engineering, Tsinghua University, Beijing 100084, China)

  • Xin Qi

    (Department of Civil Engineering, Tsinghua University, Beijing 100084, China)

  • Syed Muzammil Abbas Rizvi

    (School of Transportation, Southeast University, Nanjing 210096, China)

  • Tianshi Wang

    (Department of Civil Engineering, Tsinghua University, Beijing 100084, China)

  • Huapu Lu

    (Department of Civil Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Traffic congestion has become increasingly prevalent in many urban areas, and researchers are continuously looking into new ways to resolve this pertinent issue. Autonomous vehicles are one of the technologies expected to revolutionize transportation systems. To this very day, there are limited studies focused on the impact of autonomous vehicles in heterogeneous traffic flow in terms of different driving modes (manual and self-driving). Autonomous vehicles in the near future will be running parallel with manual vehicles, and drivers will have different characteristics and attributes. Previous studies that have focused on the impact of autonomous vehicles in these conditions are scarce. This paper proposes a new cellular automata model to address this issue, where different autonomous vehicles (cars and buses) and manual vehicles (cars and buses) are compared in terms of fundamental traffic parameters. Manual cars are further divided into subcategories on the basis of age groups and gender. Each category has its own distinct attributes, which make it different from the others. This is done in order to obtain a simulation as close as possible to a real-world scenario. Furthermore, different lane-changing behavior patterns have been modeled for autonomous and manual vehicles. Subsequently, different scenarios with different compositions are simulated to investigate the impact of autonomous vehicles on traffic flow in heterogeneous conditions. The results suggest that autonomous vehicles can raise the flow rate of any network considerably despite the running heterogeneous traffic flow.

Suggested Citation

  • Muhammad Tanveer & Faizan Ahmad Kashmiri & Hassan Naeem & Huimin Yan & Xin Qi & Syed Muzammil Abbas Rizvi & Tianshi Wang & Huapu Lu, 2020. "An Assessment of Age and Gender Characteristics of Mixed Traffic with Autonomous and Manual Vehicles: A Cellular Automata Approach," Sustainability, MDPI, vol. 12(7), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2922-:d:342135
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    Cited by:

    1. Vytautas Palevičius & Rasa Ušpalytė-Vitkūnienė & Jonas Damidavičius & Tomas Karpavičius, 2020. "Concepts of Development of Alternative Travel in Autonomous Cars," Sustainability, MDPI, vol. 12(21), pages 1-13, October.
    2. Susana García-Herrero & Juan Diego Febres & Wafa Boulagouas & José Manuel Gutiérrez & Miguel Ángel Mariscal Saldaña, 2021. "Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity," IJERPH, MDPI, vol. 18(13), pages 1-15, July.
    3. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    4. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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