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New parameter-free mobility model: Opportunity priority selection model

Author

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  • Liu, Erjian
  • Yan, Xiaoyong

Abstract

Predicting human mobility patterns has many practical applications in urban planning, traffic engineering, infectious disease epidemiology, emergency management and location-based services. Developing a universal model capable of accurately predicting the mobility fluxes between locations is a fundamental and challenging problem in regional economics and transportation science. Here, we propose a new parameter-free model named opportunity priority selection model as an alternative in human mobility prediction. The basic assumption of the model is that an individual will select destination locations that present higher opportunity benefits than the location opportunities of the origin and the intervening opportunities between the origin and destination. We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model. The results show that the new model offers universal predictions of intracity and intercity mobility patterns that are consistent with real observations, thus suggesting that the proposed model better captures the mechanism underlying human mobility than previous models.

Suggested Citation

  • Liu, Erjian & Yan, Xiaoyong, 2019. "New parameter-free mobility model: Opportunity priority selection model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
  • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119306338
    DOI: 10.1016/j.physa.2019.04.259
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    Citations

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    Cited by:

    1. Lixuan Chen & Tianyu Mu & Xiuting Li & Jichang Dong, 2022. "Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    2. Chen, Yong & Geng, Maosi & Zeng, Jiaqi & Yang, Di & Zhang, Lei & Chen, Xiqun (Michael), 2023. "A novel ensemble model with conditional intervening opportunities for ride-hailing travel mobility estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Zhang, Hong & Xu, Shan & Liu, Xuan & Liu, Chengliang, 2021. "Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities," Journal of Transport Geography, Elsevier, vol. 91(C).

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