Handbook of Mobility Data Mining, Volume 3

Handbook of Mobility Data Mining, Volume 3

Mobility Data-Driven Applications

1st Edition - January 1, 2023

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  • Editor: Haoran Zhang
  • Paperback ISBN: 9780323958929

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Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality. 

Key Features

  • Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality
  • Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data
  • Helps develop policy innovations beneficial to citizens, businesses, and society
  • Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage


Researchers, engineers, operators, company administrators, and policymakers on transportation, environment, urban planning, data mining, and sustainability; Transport-mobility planners, the road and vehicle industry, urban management authorities, transportation institutes, traffic police, public and goods transport operators; masters and Ph.D. students pursuing research in the area of mobility and transportation

Table of Contents

  • Part I: Intelligent Transportation Management

    1. Mobile Big Data in Dynamic Road Pricing System
    1.1 Data-driven Dynamic Road Pricing Models
    1.2 Real Cases of Dynamic Road Pricing System

    2. Mobile Big Data in P2P Bidding System for Transportation Services
    2.1 Bidding in Blockchain for Transportation Services
    2.2 Real Cases of P2P Bidding System

    3. Mobile Big Data in Bicycle-sharing System
    3.1 Market-oriented Area Division
    3.2 Bicycle-sharing Docks Optimization
    3.3 Electronic Fences Optimization

    4. Mobile Big Data in Ride-sharing System
    4.1 Ride-sharing Simulation
    4.2 Data-driven dispatching optimization

    5. Mobile Big Data in Customized Bus System
    5.1 Dynamic line design and emission reduction potentials analysis
    5.2 Hierarchical location selection

    Part II: Smart Emergency Management

    6. Mobile Big Data in Disaster Migration detection
    6.1 Data-driven Disaster Migration detection
    6.2 Real Case of Fukushima Earthquake

    7. Mobile Big Data in Disaster Relief Detection
    7.1 Crowd-sousing Disaster Relief Detection with Mobile Phone Big Data
    7.2 Real Case of Hurricane Katrina

    8. Mobile Big Data in Social Close Contact Detection
    8.1 Heterogeneous Mobile Sensor Data-driven Close Contact Detection
    8.2 Real Case of COVID19

    9. Mobile Big Data in Pandemic Simulation
    9.1 Mobile Big Data-driven Pandemic Simulation
    9.2 Real Case of COVID19

    10. Mobile Big Data in Pandemic Prediction
    10.1 Pandemic Prediction Models
    10.2 Real Case of COVID19

    Part III: Urban Sustainability Development

    11. Mobile Big Data in Bicycle Travel Behaviour
    11.1 Urban Planning and Bicycle Travel Attraction
    11.2 Real Cases of Major Cites in Japan

    12. Mobile Big Data in Railway Travel Behaviour
    12.1 TOD and Railway Travel Attraction
    12.2 Real Case of Tokyo

    13. Mobile Big Data in Mobility Inequality
    13.1 Mobility Inequality Indicator and Spatio-temporal Analysis
    13.2 Real Case of Japan

    14. Mobile Big Data in Road Pollution Inequality
    14.1 Road Pollution Inequality Indicator and Spatio-temporal Analysis
    14.2 Real Case of Tokyo

    15. Mobile Big Data in Light Pollution Inequality
    15.1 Light Pollution Inequality Indicator and Spatio-temporal Analysis
    15.2 Real Case of Tokyo

Product details

  • No. of pages: 300
  • Language: English
  • Copyright: © Elsevier 2023
  • Published: January 1, 2023
  • Imprint: Elsevier
  • Paperback ISBN: 9780323958929

About the Editor

Haoran Zhang

Haoran (Ronan) Zhang is Assistant Professor in the Center for Spatial Information Science at the University of Tokyo, a Researcher at the School of Business Society and Engineering at Mälardalen University in Sweden, and Senior Scientist at Locationmind Inc. in Japan. His research includes smart supply chain technologies, GPS data in shared transportation, urban sustainable performance, GIS technologies in renewable energy systems, and smart cities. He is author of numerous journal articles and Editorial Board Member of several international academic journals. He has Ph.D.’s in both Engineering and Sociocultural Environment and was awarded Excellent Young Researcher by Japan’s Ministry of Education, Culture, Sports, Science and Technology.

Affiliations and Expertise

Assistant Professor, Center for Spatial Information Science, University of Tokyo, Tokyo, Japan; Researcher, School of Business Society and Engineering, Mälardalen University, Sweden; Senior Scientist, Locationmind Inc., Tokyo, Japan

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