Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat, Khatun E.; Choudhury, Charisma F.

doi:10.1007/s41745-019-00125-9

Cited by 63 publications

(29 citation statements)

References 76 publications

(123 reference statements)

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“…Beside origin-destination matrices, multiple studies are dealing with the application of telecom big data in public transport planning [24][25][26][27], urban mobility planning [28,29], transport mode detection [27,[30][31][32], urban mobility estimation [33], traffic flow analysis [34][35][36][37] and the reconstruction of human mobility in general [38][39][40][41][42].…”

Section: Overview Of Previous Researchmentioning

confidence: 99%

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

et al. 2021

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The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

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Section: Overview Of Previous Researchmentioning

confidence: 99%

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

et al. 2021

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“…Cellular network data and smart card data can be used to obtain large samples of observations, which we can use to fit the parameters of a model more exactly (Jánošíková et al, 2014). The large sample size of observations could also allow making travel behaviour models more detailed and include more parameters such as dynamic events and the influence of weather as in Zannat and Choudhury (2019). In particular, for agent-based traffic models, which in detail model individual travel patterns, large-scale observations of real travel patterns make it possible to calibrate and validate the models in detail.…”

Section: Usage Of the Extracted Travel Patternsmentioning

confidence: 99%

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

Breyer

2021

Linköping Studies in Science and Technology. Dissertations

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First, I would like to thank my main supervisor Clas Rydergren and my co-supervisor David Gundlegård. I very much appreciate that you have always been available and gave me guidance and inspiration. Thank you for sharing your knowledge in lengthy discussions and taking the time to give feedback that challenged me to continuously improve. I very much enjoyed working together with you! I would also like to thank Lars Sköld, Simon Moritz, Ida Kristofferson and Di Yuan, and all others who made this work possible with their engagement.The research in this thesis has been to a large extend funded by the projects Mobile Network Origin Destination Estimation (MODE) and Mobile Network Data in Future Transport Systems (MOFT), both financed by Vinnova and Demand model estimation based on combination of active and passive data collection (DEMOPAN) funded by Trafikverket.I also want to thank all my colleagues at the division of Communication and Transportation Systems (KTS). I really appreciated being part of a group this international, with different research fields and perspectives. The last year of working from home made it clear how much I miss the interaction and informal discussion with you! In particular, I want to thank the group of PhD students for the great company, fun times and all mutual support. Special thanks go to Niki and Mats for their commitment to represent the PhD students at KTS! A big thank also to Morten Eltved, Jesper Bláfoss Ingvardson and Otto Anker Nielsen at DTU in Copenhagen. I really enjoyed working with you, even though my research visit turned out to be two and a half only weeks instead of three months, due to the pandemic. Yet, two fredagsøl were enough to start appreciating the Danish culture.Finally, I would like to thank all my friends, my sister Annalena and my dear parents Marita and Gerd-Herbert, for their unconditional support. Thank you, Fanny, for all your love and for reminding me that there are other things in life than research.

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“…Types of big data in PT are: (1) smart card data, (2) mobile phone data, (3) GPS data, and (4) Automatic Vehicle Location (AVL). Only a quality mix of different data collection can gain feasible results for integrated public transport system [30].…”

Section: Planning Public Passenger Transport Servicesmentioning

confidence: 99%

Integrated Passenger Transport System in Rural Areas – A Literature Review

Maretić

Abramović

2020

PROMET

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The planning and organisation of public passenger transport in rural areas is a complex process. The transport demand in rural areas is often low, which makes it hard to establish and run a financially sustainable public transport system. A solution is integrated passenger transport that eliminates deficiencies and provides benefits for all participants in the public passenger transport process. This paper describes the impact of integrated passenger transport on mobility in rural areas and critically evaluates different literature sources. Integration of passenger transport in urban areas has been described in the context of rural areas, and the challenges of integration of public passenger transport specific to rural areas have been analysed. Through the application in urban and rural areas, the planning of integrated and non-integrated passenger transport has been functionally analysed. The analysis found an increase in the degree of mobility in the areas that use integrated passenger transport compared to the non-integrated one. This research of the literature review has identified the rural areas of mobility as under-researched. The mobility research can set up a more efficient passenger transport planning system in rural areas.

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Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Cited by 63 publications

References 76 publications

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

Integrated Passenger Transport System in Rural Areas – A Literature Review

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