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Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects

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  • Ni, Linglin
  • Wang, Xiaokun (Cara)
  • Zhang, Dapeng

Abstract

Understanding the relationship between socioeconomic factors and the Less-than-Truckload (LTL) freight flows is important for transportation planners and policy makers. This paper explores the impacts of information technology, urbanization on LTL freight flows by using a spatial autocorrelation model with freight flow data from a leading LTL company in China. The results show that all IT variables and urbanization variables have positive effects on freight flows. Distance, as expected, is negatively correlated with the freight flow volume. The application of the spatial autocorrelation model further shows that origin dependence, destination dependence and OD dependence are all significant, justifying the consideration of spatial interdependence. Finally, policy implications are discussed based on the estimated results. These findings shed light on the impacts of internet and urbanization on freight transportation, and contribute to the design of freight policies and the development of the LTL industry.

Suggested Citation

  • Ni, Linglin & Wang, Xiaokun (Cara) & Zhang, Dapeng, 2016. "Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 12-25.
    • Handle: RePEc:eee:transa:v:92:y:2016:i:c:p:12-25
    DOI: 10.1016/j.tra.2016.06.030
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    2. Chengzhuo Wu & Li Zhuo & Zhuo Chen & Haiyan Tao, 2021. "Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    3. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    4. Ling Zhang & Jingjing Hao & Xiaofeng Ji & Lan Liu, 2019. "Research on the Complex Characteristics of Freight Transportation from a Multiscale Perspective Using Freight Vehicle Trip Data," Sustainability, MDPI, vol. 11(7), pages 1-20, March.
    5. Dapeng Zhang & Xiaokun Wang & José Holguín-Veras & Wei Zou, 2019. "Investigation of carriers’ ability to transfer toll increases: an empirical analysis of freight agents’ relative market power," Transportation, Springer, vol. 46(6), pages 2291-2308, December.
    6. Wang, Xiaokun (Cara) & Zhang, Dapeng, 2017. "Truck freight demand elasticity with respect to tolls in New York State," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 51-60.

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