
@article{yu:2020,
    title = {Mobile phone data in urban bicycle-sharing: {Market}-oriented sub-area division and spatial analysis on emission reduction potentials},
    volume = {254},
    issn = {0959-6526},
    shorttitle = {Mobile phone data in urban bicycle-sharing},
    url = {https://www.sciencedirect.com/science/article/pii/S0959652620300214},
    doi = {10.1016/j.jclepro.2020.119974},
    abstract = {In recent years, Internet-supported non-dock bicycle-sharing service had emerged and gradually become warmly applauded by the public. In the non-dock bicycle-sharing system planning, the definition of coverage area and the division of sub service areas are highly essential and fundamental, which get little attention in the existing studies. In this paper, we propose a market-oriented methodology for non-dock bicycle-sharing system planning. Firstly, the potential demand of bicycle-sharing use is identified using mobile phone data. Then, a link network is constructed to represent the spatial distribution of potential demand. The algorithm of community detection is applied to the link network to discover the densely connected nodes in the area and define the division of sub service areas. Finally, the potential patterns of bicycle-sharing use, the spatial distribution of the potential demand, and the potential emission reduction are analysed based on the resulting division of sub service areas. The proposed method is tested using a data set of approximately 34 million GPS trajectories obtained in Tokyo. The area of Tokyo is subdivided into 21 sub service areas. Suggestions for bicycle management, infrastructure development, and the bicycle-sharing system planning are given regarding sub service areas with different properties. The potential emission reduction for the introduction of bicycle-sharing system is calculated in the area of Tokyo.},
    language = {en},
    urldate = {2021-12-02},
    journal = {Journal of Cleaner Production},
    author = {Yu, Qing and Zhang, Haoran and Li, Weifeng and Sui, Yi and Song, Xuan and Yang, Dongyuan and Shibasaki, Ryosuke and Jiang, Wenxiao},
    month = may,
    year = {2020},
    keywords = {Bicycle-sharing, Climate change mitigation, Community detection, Emission reduction potentials, Spatial analysis, Sub-area division},
    pages = {119974},
}

@article{yu:2020-1,
    title = {Mobile phone {GPS} data in urban customized bus: {Dynamic} line design and emission reduction potentials analysis},
    volume = {272},
    issn = {0959-6526},
    shorttitle = {Mobile phone {GPS} data in urban customized bus},
    url = {https://www.sciencedirect.com/science/article/pii/S095965262032518X},
    doi = {10.1016/j.jclepro.2020.122471},
    abstract = {Customized Bus is a new mode of Internet-supported public transportation. It is regarded as one of the major strategies to reduce the usage of private cars and mitigate greenhouse gas emissions from road traffic. For a customized bus system, a dynamic bus line planning system based on the demand can largely improve the performance and promote the public acceptance of customized bus service. This paper introduces a method to generate planning suggestions for bus lines and stops based on massive demand data. A link network is generated from the input to represent the sharing route of the demand. With community detection, the link network is segmented into communities with similar travel routes. By examining the core-peripheral structure and matching the core part of communities with the road network, the customized bus lines are generated. Boarding and alighting hotspots are identified as the suggestion for customized bus stops. The methodology is tested by using mobile phone data in Tokyo. With the input of one-day sample, the algorithm can generate the result in approximately 1 min and extract 29 bus lines. According to the shape and spatial location of the bus lines, three types of bus lines serving different travel patterns are classified: radiation type lines, ring-type lines, and suburban lines. Analyzing the emission reduction potential of the extracted bus lines manifestes that bus line planning of the proposed method has the potential to relieve emission pressure on urban expressways and to reduce approximately 13\% of road traffic emission.},
    language = {en},
    urldate = {2021-12-02},
    journal = {Journal of Cleaner Production},
    author = {Yu, Qing and Zhang, Haoran and Li, Weifeng and Song, Xuan and Yang, Dongyuan and Shibasaki, Ryosuke},
    month = nov,
    year = {2020},
    keywords = {Customized bus system, Dynamic bus line planning, Emission reduction potentials, GPS data},
    pages = {122471},
}

@book{zhang:2021,
    title = {Big {Data} and {Mobility} as a {Service}},
    isbn = {978-0-323-90170-3},
    abstract = {Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners. Summarizes current fundamental MaaS technologies Shows how to utilize anonymous big data for transportation analysis and problem-solving Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework},
    language = {en},
    publisher = {Elsevier},
    author = {Zhang, Haoran and Song, Xuan and Shibasaki, Ryosuke},
    month = oct,
    year = {2021},
    note = {Google-Books-ID: Fb88EAAAQBAJ},
    keywords = {Transportation / General},
}

@article{harrison:2020,
    title = {New and emerging data forms in transportation planning and policy: {Opportunities} and challenges for “{Track} and {Trace}” data},
    volume = {117},
    issn = {0968-090X},
    shorttitle = {New and emerging data forms in transportation planning and policy},
    url = {https://www.sciencedirect.com/science/article/pii/S0968090X20305878},
    doi = {10.1016/j.trc.2020.102672},
    abstract = {High quality, reliable data and robust models are central to the development and appraisal of transportation planning and policy. Although conventional data may offer good ‘content’, it is widely observed that it lacks context i.e. who and why people are travelling. Transportation modelling has developed within these boundaries, with implications for the planning, design and management of transportation systems and policy-making. This paper establishes the potential of passively collected GPS-based “Track \& Trace” (T\&T) datasets of individual mobility profiles towards enhancing transportation modelling and policy-making. T\&T is a type of New and Emerging Data Form (NEDF), lying within the broader ‘Big Data’ paradigm, and is typically collected using mobile phone sensors and related technologies. These capture highly grained mobility content and can be linked to the phone owner/user behavioural choices and other individual context. Our meta-analysis of existing literature related to spatio-temporal mobile phone data demonstrates that NEDF’s, and in particular T\&T data, have had little mention to date within an applied transportation planning and policy context. We thus establish there is an opportunity for policy-makers, transportation modellers, researchers and a wide range of stakeholders to collaborate in developing new analytic approaches, revise existing models and build the skills and related capacity needed to lever greatest value from the data, as well as to adopt new business models that could revolutionise citizen participation in policy-making. This is of particular importance due to the growing awareness in many countries for a need to develop and monitor efficient cross-sectoral policies to deliver sustainable communities.},
    language = {en},
    urldate = {2021-12-02},
    journal = {Transportation Research Part C: Emerging Technologies},
    author = {Harrison, Gillian and Grant-Muller, Susan M. and Hodgson, Frances C.},
    month = aug,
    year = {2020},
    keywords = {Big Data, Mobile phone data, Mobility profile, Track and Trace, Transport policy},
    pages = {102672},
}

@article{zhang:2020,
    title = {Mobile phone {GPS} data in urban ride-sharing: {An} assessment method for emission reduction potential},
    volume = {269},
    issn = {0306-2619},
    shorttitle = {Mobile phone {GPS} data in urban ride-sharing},
    url = {https://www.sciencedirect.com/science/article/pii/S030626192030550X},
    doi = {10.1016/j.apenergy.2020.115038},
    abstract = {Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing ofcarjourneys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97\% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO2 is estimated to be reduced by 84.52\%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56\% of CO2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers.},
    language = {en},
    urldate = {2021-12-02},
    journal = {Applied Energy},
    author = {Zhang, Haoran and Chen, Jinyu and Li, Wenjing and Song, Xuan and Shibasaki, Ryosuke},
    month = jul,
    year = {2020},
    keywords = {Deep learning, Potential emission reduction, Ride-sharing, Users matching},
    pages = {115038},
}

@article{chen:2021,
    title = {{GPS} data in urban online ride-hailing: {A} simulation method to evaluate impact of user scale on emission performance of system},
    volume = {287},
    issn = {0959-6526},
    shorttitle = {{GPS} data in urban online ride-hailing},
    url = {https://www.sciencedirect.com/science/article/pii/S0959652620356134},
    doi = {10.1016/j.jclepro.2020.125567},
    abstract = {With the spread of the ride-hailing service over the world, many scholars still argue whether ride-hailing is an effective travel mode for emission reduction. Since rider-hailing is a crowdsourcing system, the user scale can have a great impact on the emission performance of the system. A clearer pattern of the impact of user scale on emission performance of the ride-hailing system should be provided for better local market development and policymaking. In this study, based on massive Didi GPS records, we proposed a cross simulation model to evaluate the impact of user scale on the emission performance of the ride-hailing system and adapted the Gibbs sampling for a comprehensive computation. The result shows a strong impact of user scale on the emission performance. The mean of void cruising distance proportion varies from 2.12\% to 44.58\% under all situation simulation. Moreover, according to the simulation results under different day conditions, the relationship between the user scale and emission performance is not concerned with the day condition but the local regular travel pattern. Based on this relationship, we provided approximate user scales under expected thresholds of the emission and efficiency performance of ride-hailing. This work can be a foundation and guideline for future decision making on ride-hailing.},
    language = {en},
    urldate = {2021-12-02},
    journal = {Journal of Cleaner Production},
    author = {Chen, Jinyu and Li, Wenjing and Zhang, Haoran and Cai, Zekun and Sui, Yi and Long, Yin and Song, Xuan and Shibasaki, Ryosuke},
    month = mar,
    year = {2021},
    keywords = {Emission performance, GPS Data, Ride-hailing, User scale},
    pages = {125567},
}

