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Abstract

Traffic congestion is a major problem in many big cities around the world. According to a study performed by the world bank in Egypt in 2010 and concluded in 2012, the traffic congestion was estimated to 14 Billion EGP in the Cairo metropolitan area and to 50 Billion EGP (4\% of the GDP) in the entire Egypt. Few of the reasons of the high monetary cost of the traffic congestion are: (1) travel time delay, (2) travel time unreliability, and (3) excess fuel consumption. Smart traffic management addresses some of the causes and consequences of traffic congestion. It can predict congested routes, take preventive decisions to reduce congestion, disseminate information about accidents and work zones, and identify the alternate routes that can be taken. In this project, we develop a real-time and scalable data storage and analysis framework for traffic prediction and management. The input to this system is a stream of GPS and/or cellular data that has been cleaned and mapped to the road network. Our proposed framework allows us to (1) predict the roads that will suffer from traffic congestion in the near future, and traffic management decisions that can relieve this congestion; and (2) a what-if traffic system that is used to simulate what will happen if a traffic management or planning decision is taken. For example, it answers questions, such as: "What will happen if an additional ring road is built to surround Cairo?" or "What will happen if point of interest X is moved away from the downtown to the outskirts of the city. This framework has the following three characteristics. First, it predicts the flow of the vehicles in the road based on historical data. This is done by tracking vehicles every day trajectories and using them in a statistical model to predict the vehicles movement on the road. It then predicts the congested traffic zones based on the current vehicles in the road and their predicted paths. Second, historical traffic data are heavily exploited in the approach we use to predict traffic flow and traffic congestion. Therefore, we develop new techniques to efficiently store traffic data in the form of graphs for fast retrieval. Third, it is required to update the traffic flow of vehicles and predict congested areas in real-time, therefore we deploy our framework in the cloud and employ optimization technique to speedup the execution of our algorithms.

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/content/papers/10.5339/qfarc.2014.ITPP0688
2014-11-18
2020-04-07
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2014.ITPP0688
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