Operational and environmental evaluation of traffic movement on urban streets using GPS floating-car data
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2014-12-24 https://doi.org/10.14419/ijet.v4i1.3794
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Environmental Evaluation, Floating Car, Global Positioning System (GPS), Level of Service and Level of Congestion, Traffic Conditions on Urban Streets. -
Abstract
Evaluating traffic networks is vital for the management of traffic systems. Nowadays, Global Positioning System (GPS) technology, by independent GPS devices or GPS enabled cellular phones, is properly used in most vehicles, especially on urban streets, due to its cost effectiveness, ease of use, and real-time services. With its ability to detect the time position of the floating car, GPS devices introduces a new prospective to gather vehicle information. Collected information can be utilized for traffic management and for Intelligent Transportation System (ITS) to deduce evaluation indicators, and to achieve suitable measures. This paper brings up a framework for using real-time data collected by GPS-floating car technique for evaluating traffic conditions on urban streets. It utilizes GPS data of time, longitude, latitude to estimate evaluation indicators of street segments. This incorporates operational evaluation of street segment by characterizing Level of Service and level of congestion, and incorporates environmental evaluation by estimating the road-side concentration of pollutants emitted by traffic. Framework formation has been described. Different models used within every step of the framework have been investigated. Including models used in GPS data sample analysis, through models used to identify of street segments, and finally models used for street segments evaluation.
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References
[1] Zhou Xiangyu, Wenjun Wang, and Long Yu. "Traffic Flow Analysis and Prediction Based on GPS Data of Floating Cars." Proceedings of the International Conference on Information Technology and Software Engineering. Springer Berlin Heidelberg 2013.â€
[2] Bar-Gera, H. "Evaluation of a cellular phone-based system for measurements of trafï¬c speeds and travel times: a case study from Israel", Transportation Research C 15 (6), 380–391., 2007. http://dx.doi.org/10.1016/j.trc.2007.06.003.
[3] Xiaohui, S., Jianping, X., Jun, Z., Lei, Z., & Weiye, L., "Application of dynamic traffic flow map by using real time GPS data equipped vehicles". In ITS Telecommunications Proceedings, 6th International Conference on (pp. 1191-1194). IEEE. 2006.
[4] Herrera, Juan C., Work, D. B., Herring, R., Ban, X. J., Jacobson, Q., & Bayen, A. M., "Evaluation of traffic data obtained via GPS-enabled mobile phones: The< i> Mobile Century</i> field experiment." Transportation Research Part C: Emerging Technologies 18.4 (2010): 568-583.†http://dx.doi.org/10.1016/j.trc.2009.10.006.
[5] W.-H. Lee, S.-S. Tseng, S.-H. Tsai, "knowledge based real-time travel time prediction system for urban network, Expert Systems with Applications". ELSEVIER 36 (2009) 4239–4247. http://dx.doi.org/10.1016/j.eswa.2008.03.018.
[6] Byon, Y-J., Amer Shalaby, and Baher Abdulhai. "Travel time collection and traffic monitoring via GPS technologies." Intelligent Transportation Systems Conference, 2006. ITSC'06. IEEE. IEEE, 2006.â€
[7] Yong-chuan, Zhang, Zuo Xiao-qing, and Chen Zhen-ting. "Traffic Congestion Detection Based On GPS Floating-Car Data." Procedia Engineering 15 (2011): 5541-5546.†http://dx.doi.org/10.1016/j.proeng.2011.08.1028.
[8] Sun, Zhanbo, and Xuegang Jeff Ban., "Vehicle classification using GPS data.†Transportation Research Part C: Emerging Technologies 37 (2013): 102-117.†http://dx.doi.org/10.1016/j.trc.2013.09.015.
[9] Jiménez-Meza, A., J. Arámburo-Lizárraga, and E. de la Fuente, "Framework for Estimating Travel Time, Distance, Speed, and Street Segment Level of Service (LOS), based on GPS Data", Procedia Technology 7 (2013): 61-70.†http://dx.doi.org/10.1016/j.protcy.2013.04.008.
[10] Pang, L. X., Chawla, S., Liu, W., & Zheng, Y., "On detection of emerging anomalous traffic patterns use GPS data", Data & Knowledge Engineering 87 (2013): 357-373.†http://dx.doi.org/10.1016/j.datak.2013.05.002.
[11] Wuttiet Tafesse and Tesfaye Gobena, Lecture Notes for Environmental Health Science Students – Surveying, Haramaya University, 2005.
[12] Dowling, Richard G., et al., "NCHRP Report 616: multimodal level of service analysis for urban streets", Transportation Research Board of the National Academies, Washington, DC (2008).â€
[13] Transportation Rresearch Board, Highway capacity manual, National Research Council (2000).â€
[14] Liu, M., Yu, L., Guo, J., Guo, S., Guo, J., & Wen, H. "Fuzzy logic-based urban traffic congestion evaluation models and application", Proc., 1st Int. Conf. on Transportation Engineering. New York: ASCE, 2007.â€
[15] Gumusay, M. Umit, Alper Unal, and Rukiye Aydın., "Use of geographical information systems in analyzing vehicle emissions: Istanbul as a case study", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37 (2008): 997-1000.â€
[16] Elkafoury, A., Negm, A. M., Bady, M., & Aly, M. H. F., "Review of transport emission modeling and monitoring in urban areas—Challenge for developing countries", Advanced Logistics and Transport (ICALT), 2014 International Conference on. IEEE, 2014.â€
[17] Pu, Yichao, and Chao Yang., "Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements", Environmental Pollution 192 (2014): 300-307.†http://dx.doi.org/10.1016/j.envpol.2014.05.019.
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How to Cite
Elkafoury, A., Negm, A., Hafez, M., Bady, M., & Ichimurac, T. (2014). Operational and environmental evaluation of traffic movement on urban streets using GPS floating-car data. International Journal of Engineering & Technology, 4(1), 20-25. https://doi.org/10.14419/ijet.v4i1.3794Received date: 2014-11-03
Accepted date: 2014-12-02
Published date: 2014-12-24