The Impact of Subject Diversity on Taxi Transportation System

  • Wencan Gao
  • Hao YueEmail author
  • Bingjian Yang
  • Mengyu Zhang
  • Lucheng Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)


In order to analyze the impact of different driver types and passenger types on the taxi transportation system, a simulation method is applied to reproduce the process of driver and passenger matching in the taxi market. According to the driver’s decision-making way, drivers were divided into three types: searching for the closest passenger in sight, searching for the most profitable passenger per hour by taxi-hailing app, searching for the closest passenger by taxi-hailing app. According to passenger travel characteristics, passengers are classified into ordinary passengers, congested passengers, short-distance passengers and marginal passengers. Formulate passenger taxi generation, passenger disappearance, driver decision, taxi parade, taxi pick-up passengers on board, taxi to transport passenger rules. Select passenger’s waiting time, driver’s search time, driver’s income, taxi’s empty rate, taxi’s status, number of specific passengers disappeared, ratio of disappeared passengers’ number to total disappeared number as evaluation indexes. Research shows that different types of drivers and passengers will have different impacts on the taxi transportation system. Using taxi-hailing app can help improve the taxi transportation system. Searching for the closest passenger by taxi-hailing app mode can improve passenger’s actual load efficiency while ensuring the driver’s income. It will not screen and eliminate special passengers and improve the fairness of passenger travel. Therefore, it is recommended drivers search for the closest passenger by taxi-hailing app.


Taxi transportation system Traffic simulation Decision-making method Passenger characteristics Search radius 



The work is supported by the National Natural Science Foundation of China (Grant No. 71771013, 51338008, 71621001, 71501011), and the Center of Cooperative Innovation for Beijing Metropolitan Transportation.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Wencan Gao
    • 1
  • Hao Yue
    • 1
    Email author
  • Bingjian Yang
    • 1
  • Mengyu Zhang
    • 1
  • Lucheng Zhao
    • 2
  1. 1.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Transportation, Ministry of TransportBeijing Jiaotong UniversityBeijingChina
  2. 2.Nanjing Institute of City & Transport Planning Co., LtdNangjingChina

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