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A Spatio-Temporal Decision Support Framework for Large Scale Logistics Distribution in the Metropolitan Area

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Advances in Spatial Data Handling and Analysis

Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

Rapid growing urbanization and explosive e-business expect effective logistics distribution service in the metropolitan area. Because of traffic control, commuting peak and unpredictable traffic accidents, traffic states in the metropolitan area fluctuate sharply, leading to the unacceptable logistics service delay in our daily life. To overcome this problem, a spatio-temporal decision support (STDS) framework is developed to facilitate large scale logistics distribution in the metropolitan area. It consists of a traffic information database, a spatio-temporal heuristic algorithm module, many intelligent mobile apps and a cloud geographical information science (GIS) based logistics server. The spatio-temporal heuristics algorithm is to optimize logistics vehicle routing with the historical traffic information. The mobile apps guide the deliverymen in the real-time logistics. The cloud GIS based logistics server integrates traffic information, client demands, vehicle information, the optimization of vehicle routing and the monitoring of logistics processes. The STDS framework has been implemented in a GIS environment. Its performance is evaluated with large scale logistics cases in Guangzhou, China. Results demonstrates the effectiveness and the efficiency of the developed STDS framework. The STDS framework could be widely used in the logistics distribution in metropolitan area, such as the express delivery, e-business, and so on.

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Acknowledgments

This research was jointly supported by the National Science Foundation of China. (No. 41401444, 41371377), the Shenzhen Scientific Research and Development Funding Program (No. ZDSY20121019111146499, No. JSGG20121026111056204, No. JCYJ20120817163755063), the Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (No. JCYJ20121019111128765), China Postdoctoral Science Foundation funded project (2014M560671) and the open research fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 13S02). The authors also would like to thank the reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, 518060, Shenzhen, China

    Wei Tu, Qingquan Li, Xiaomeng Chang, Yang Yue & Jiasong Zhu

  2. Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, 518060, Shenzhen, China

    Wei Tu, Qingquan Li, Xiaomeng Chang, Yang Yue & Jiasong Zhu

  3. College of Information Engineering, Shenzhen University, 518060, Shenzhen, China

    Wei Tu

  4. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, 430079, Wuhan, China

    Qingquan Li & Xiaomeng Chang

Authors
  1. Wei Tu
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  2. Qingquan Li
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  3. Xiaomeng Chang
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  4. Yang Yue
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  5. Jiasong Zhu
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Corresponding author

Correspondence to Wei Tu .

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Editors and Affiliations

  1. Department of Geography, Environment and Society, University of Minnesota, Minneapolis, Minnesota, USA

    Francis Harvey

  2. Dept. of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong SAR, China

    Yee Leung

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Tu, W., Li, Q., Chang, X., Yue, Y., Zhu, J. (2015). A Spatio-Temporal Decision Support Framework for Large Scale Logistics Distribution in the Metropolitan Area. In: Harvey, F., Leung, Y. (eds) Advances in Spatial Data Handling and Analysis. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-19950-4_12

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