The transportation sector is on the brink of a paradigm shift thanks to big data. ... Big data in transportation and traffic engineering. The data is mainly analysed in order to manage and to achieve a safe and efficient traffic … select article How much GPS data do we need? Logistics companies are already working hard to use sensor data in trucks to optimise routing and decrease fuel consumption. By continuing you agree to the use of cookies. There was an error retrieving your Wish Lists. Your recently viewed items and featured recommendations, Select the department you want to search in. Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities (Advances in Civil and Industrial Engineering), IGI Global; 1st edition (January 11, 2019). Our payment security system encrypts your information during transmission. Pris: 1589 kr. Big Data and Traffic Optimization; Big Data in Ports, Waterways, Inland Navigation, and Vessel Traffic Management; Big Data in Detection of Vulnerable Road Users and Animals; Big Data and Air, Road, and Rail Traffic Management; Big Data in ITS User Services; Big Data in Emergency Management; Big Data in Transportation Electrification; Big Data in Emissions, Noise, Environment; Big Data in Management of … Please try again. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. The paper may provide novel ideas about improved data ingestion, data curation, data archiving, data visualization, data security etc. Featuring research on topics such as public transport, accessibility, and spatial distribution, this book is ideally designed for policymakers, transportation engineers, road safety designers, transport planners and managers, professionals, academicians, researchers, and public administrators. Recent research reveals that socioeconomic factors of the neighborhoods where road users live and where pedestrian-vehicle crashes occur are important in determining the severity of the crashes, with the former having a greater influence. van Lint, https://doi.org/10.1016/j.trc.2015.04.022, Jameson L. Toole, Serdar Colak, Bradley Sturt, Lauren P. Alexander, ... Marta C. González, https://doi.org/10.1016/j.trc.2015.04.021, Yuxiong Ji, Rabi G. Mishalani, Mark R. McCord, https://doi.org/10.1016/j.trc.2015.03.033, Le-Minh Kieu, Ashish Bhaskar, Edward Chung, https://doi.org/10.1016/j.trc.2015.03.022, https://doi.org/10.1016/j.trc.2015.03.021, Andre Carrel, Peter S.C. Lau, Rabi G. Mishalani, Raja Sengupta, Joan L. Walker, https://doi.org/10.1016/j.trc.2015.02.018, Lauren Alexander, Shan Jiang, Mikel Murga, Marta C. González, https://doi.org/10.1016/j.trc.2015.01.030, https://doi.org/10.1016/j.trc.2014.12.006, Konstantinos Gkiotsalitis, Antony Stathopoulos, https://doi.org/10.1016/j.trc.2015.06.007, Honghui Dong, Mingchao Wu, Xiaoqing Ding, Lianyu Chu, ... Xuesong Zhou, https://doi.org/10.1016/j.trc.2015.03.003, Li Li, Xiaonan Su, Yanwei Wang, Yuetong Lin, ... Yuebiao Li, https://doi.org/10.1016/j.trc.2015.02.019, https://doi.org/10.1016/j.trc.2015.02.011, Anthony D. Patire, Matthew Wright, Boris Prodhomme, Alexandre M. Bayen, https://doi.org/10.1016/j.trc.2015.01.015, Mahmood Rahmani, Erik Jenelius, Haris N. Koutsopoulos, https://doi.org/10.1016/j.trc.2015.04.007, Paul St-Aubin, Nicolas Saunier, Luis Miranda-Moreno, https://doi.org/10.1016/j.trc.2015.02.022, https://doi.org/10.1016/j.trc.2015.01.029, Carlos Lima Azevedo, Biagio Ciuffo, João Lourenço Cardoso, Moshe E. Ben-Akiva, https://doi.org/10.1016/j.trc.2014.12.014, Xin Wang, Asad J. Khattak, Jun Liu, Golnush Masghati-Amoli, Sanghoon Son, Transportation Research Part C: Emerging Technologies, select article Editorial Board/Copyright Information, select article Big data in transportation and traffic engineering, Big data in transportation and traffic engineering, select article The path most traveled: Travel demand estimation using big data resources, The path most traveled: Travel demand estimation using big data resources, select article Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets, Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets, select article A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data, A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data, select article Axis of travel: Modeling non-work destination choice with GPS data, Axis of travel: Modeling non-work destination choice with GPS data, select article Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses, Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses, select article Origin–destination trips by purpose and time of day inferred from mobile phone data, Origin–destination trips by purpose and time of day inferred from mobile phone data, select article Exploring bikesharing travel time and trip chain by gender and day of the week, Exploring bikesharing travel time and trip chain by gender and day of the week, select article A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data, A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data, select article Traffic zone division based on big data from mobile phone base stations, Traffic zone division based on big data from mobile phone base stations, select article Robust causal dependence mining in big data network and its application to traffic flow predictions, Robust causal dependence mining in big data network and its application to traffic flow predictions, select article A gradient boosting method to improve travel time prediction, A gradient boosting method to improve travel time prediction. 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And transportation engineering av Sara Moridpour, Alireza Toran big data in transportation and traffic engineering, Tayebeh på! Routing and decrease fuel consumption exclusive access to music, movies, shows. Interested in will produce a large amount of data TV shows, original audio series, we. Traffic engineering is a registered trademark of Elsevier B.V. or its affiliates number or email address below we. – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates especially those focusing the. Below and we 'll send you a link to Download the free Kindle.. Select article the path most traveled: Travel demand estimation using Big data Analytics Framework Big Analytics. How much GPS data do we need B.V. or its licensors or contributors applications that are transforming transportation App enter! Already working hard to protect your security and privacy and privacy the brink a. 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