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A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

Author

Listed:
  • Fan Yang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Linchao Li

    (College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China)

  • Fan Ding

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Huachun Tan

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Bin Ran

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

Suggested Citation

  • Fan Yang & Linchao Li & Fan Ding & Huachun Tan & Bin Ran, 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7688-:d:414997
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    References listed on IDEAS

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    1. Llorca, Carlos & Ji, Joanna & Molloy, Joseph & Moeckel, Rolf, 2018. "The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor," Research in Transportation Economics, Elsevier, vol. 72(C), pages 27-36.
    2. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    3. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    4. Xianyuan Zhan & Satish Ukkusuri & Feng Zhu, 2014. "Inferring Urban Land Use Using Large-Scale Social Media Check-in Data," Networks and Spatial Economics, Springer, vol. 14(3), pages 647-667, December.
    5. Fan Yang & Zhenxing Yao & Fan Ding & Huachun Tan & Bin Ran, 2019. "Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
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    Cited by:

    1. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

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