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Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector of Traffic management and control. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network. Researches rely on three different informations. Historical and recent information of a traffic network about its density and flow, a model of the transport network infrastructure and algorithms referring to both spatial and temporal dimensions. The final objective is to provide a better optimization of the traffic infrastructure such as traffic lights. Those optimizations should result into a decrease of the travel times, pollution and fuel consumption.
To survey and manage traffic infrastructures, cities can provide themselves with Intelligent transportation system (ITS) which are especially meaningful in densely urbanized areas. They provide the possibility to better analyze and manage a transport network impact of external factors within a short-term vision, with the daily fluctuate density of the transport network.[1] And over a long term vision, with changes as th increase of motorization, urbanization, population growth and changes in population density. At another end, motorists can use Advanced traveler information system (ATIS) which bring processed data to the end user to help him taking the best directions.
Researchers work on different level to make progress into traffic analysis, by collecting traffic data from different sources, modeling traffic flows and network, and developing algorithms to either predict traffic states in a far or a short-term future.[1][2]
^ abLippi, Marco; Bertini, Matteo; Frasconi, Paolo (2010-09-20). "Collective Traffic Forecasting". In Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides; Sebag, Michèle (eds.). Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 6322. Springer Berlin Heidelberg. pp. 259–273. doi:10.1007/978-3-642-15883-4_17. ISBN 978-3-642-15882-7.
^Su, Haowei; Yu, Shu (2007-11-22). "Hybrid GA Based Online Support Vector Machine Model for Short-Term Traffic Flow Forecasting". In Xu, Ming; Zhan, Yinwei; Cao, Jiannong; Liu, Yijun (eds.). Advanced Parallel Processing Technologies. Lecture Notes in Computer Science. Vol. 4847. Springer Berlin Heidelberg. pp. 743–752. doi:10.1007/978-3-540-76837-1_80. ISBN 978-3-540-76836-4.
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