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Big data in transportation and traffic engineering
Transportation Research Part C 58 (2015) 161
Contents lists available at ScienceDirect
Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Editorial
Big data in transportation and traffic engineering While Big Data is seen as an emerging technology to both practitioners and researchers, significant challenges may arise for academia, Federal and State agencies, industry, and other organizations. Discovering novel ways to manage and analyze big data to create value would increase the accuracy of predictions, improve the management and security of transportation infrastructure and enable informed decision-making. It is these challenges that may drive new insights and opportunities and transform the way we perceive transportation and traffic engineering phenomena. Big Data has been rapidly expanding into the transportation arena. However, the methods, models and algorithms that are used today in our domain to mine and explore data – think of estimation, prediction, validation of traffic and transportation theories and models – may not scale and/or perform well under these new conditions. In fact, in many disciplines in our field, particularly those that are classically ‘‘data-poor and assumption-rich” (e.g. activity scheduling and choice modelling), big data may lead us to rethinking existing theories and models altogether. The aim of this special issue was to highlight recent research in Big Data applications that could help establishing fundamental knowledge, concepts and technologies related to transportation and traffic engineering. This special issue includes seventeen outstanding contributions covering various transportation applications from travel demand estimation to real time traffic operations and safety monitoring. Broadly speaking, these seventeen papers can be categorized into three groups: (1) transportation planning; (2) traffic operations; and (3) safety. The first category dealing with transportation planning includes nine papers. These papers utilize mobile phone’s call detail records, smart card systems, automatic passenger count systems, GPS, smart phone and vehicle location services, bike-sharing and social media data, and present big data applications, such as travel demand estimation, transit origin– destination estimation, daily travel pattern analysis, non-work destination choice, transit travel experience, origin– destination estimation by trip purpose and time of day, willingness to travel by activity types, and traffic zoning. With respect to the second category papers dealing with traffic operations, four papers are included. These papers exploit big data sources from GPS, Bluetooth reader, loop detector, private sector travel time, and floating cars for traffic flow prediction, travel time prediction, addressing GPS data requirements, and route travel time distribution. Finally, the third category of papers dealing with safety includes four papers. Big data sources used in this category come from video, microwave vehicle detection system, GPS, and vehicle trajectory data. This section includes applications on proactive road safety analysis, traffic operations and safety monitoring, calibration of traffic simulation model for safety assessment, and instantaneous driving decisions modeling. We dedicate this issue to the loving memory of Matthew G. Karlaftis, EiC of Transportation Research Part C, a thoughtful friend and an inspiring colleague, who met an untimely death before the completion of this issue. Eleni I. Vlahogianni National Technical University of Athens, Greece E-mail address: [email protected] Byungkyu Brian Park University of Virginia, United States E-mail address: [email protected] J.W.C. van Lint Delft University of Technology, Netherlands E-mail address: [email protected]
http://dx.doi.org/10.1016/j.trc.2015.08.006 0968-090X/Ó 2015 Published by Elsevier Ltd.
Contents lists available at ScienceDirect
Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Editorial
Big data in transportation and traffic engineering While Big Data is seen as an emerging technology to both practitioners and researchers, significant challenges may arise for academia, Federal and State agencies, industry, and other organizations. Discovering novel ways to manage and analyze big data to create value would increase the accuracy of predictions, improve the management and security of transportation infrastructure and enable informed decision-making. It is these challenges that may drive new insights and opportunities and transform the way we perceive transportation and traffic engineering phenomena. Big Data has been rapidly expanding into the transportation arena. However, the methods, models and algorithms that are used today in our domain to mine and explore data – think of estimation, prediction, validation of traffic and transportation theories and models – may not scale and/or perform well under these new conditions. In fact, in many disciplines in our field, particularly those that are classically ‘‘data-poor and assumption-rich” (e.g. activity scheduling and choice modelling), big data may lead us to rethinking existing theories and models altogether. The aim of this special issue was to highlight recent research in Big Data applications that could help establishing fundamental knowledge, concepts and technologies related to transportation and traffic engineering. This special issue includes seventeen outstanding contributions covering various transportation applications from travel demand estimation to real time traffic operations and safety monitoring. Broadly speaking, these seventeen papers can be categorized into three groups: (1) transportation planning; (2) traffic operations; and (3) safety. The first category dealing with transportation planning includes nine papers. These papers utilize mobile phone’s call detail records, smart card systems, automatic passenger count systems, GPS, smart phone and vehicle location services, bike-sharing and social media data, and present big data applications, such as travel demand estimation, transit origin– destination estimation, daily travel pattern analysis, non-work destination choice, transit travel experience, origin– destination estimation by trip purpose and time of day, willingness to travel by activity types, and traffic zoning. With respect to the second category papers dealing with traffic operations, four papers are included. These papers exploit big data sources from GPS, Bluetooth reader, loop detector, private sector travel time, and floating cars for traffic flow prediction, travel time prediction, addressing GPS data requirements, and route travel time distribution. Finally, the third category of papers dealing with safety includes four papers. Big data sources used in this category come from video, microwave vehicle detection system, GPS, and vehicle trajectory data. This section includes applications on proactive road safety analysis, traffic operations and safety monitoring, calibration of traffic simulation model for safety assessment, and instantaneous driving decisions modeling. We dedicate this issue to the loving memory of Matthew G. Karlaftis, EiC of Transportation Research Part C, a thoughtful friend and an inspiring colleague, who met an untimely death before the completion of this issue. Eleni I. Vlahogianni National Technical University of Athens, Greece E-mail address: [email protected] Byungkyu Brian Park University of Virginia, United States E-mail address: [email protected] J.W.C. van Lint Delft University of Technology, Netherlands E-mail address: [email protected]
http://dx.doi.org/10.1016/j.trc.2015.08.006 0968-090X/Ó 2015 Published by Elsevier Ltd.