Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

[1]  Hao Liang,et al.  Whether to charge an electric vehicle or not? A near-optimal online approach , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[2]  Giuseppe Manfredi,et al.  An algorithm for the recognition of levels of congestion in road traffic problems , 2009, Math. Comput. Simul..

[3]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[4]  Concetto Spampinato,et al.  Integrating Location Tracking, Traffic Monitoring and Semantics in a Layered ITS Architecture , 2011 .

[5]  M. Schulze,et al.  Impact of electrical vehicles on strategic planning of energy infrastructure , 2010, 2010 International Conference on Power System Technology.

[6]  D. Metz.  The Myth of Travel Time Saving , 2008 .

[7]  M R Flynn,et al.  Self-sustained nonlinear waves in traffic flow. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Hussein Dia,et al.  An object-oriented neural network approach to short-term traffic forecasting , 2001, Eur. J. Oper. Res..

[9]  Yi Lu Murphey,et al.  Real time vehicle speed prediction using a Neural Network Traffic Model , 2011, The 2011 International Joint Conference on Neural Networks.

[10]  Lin He,et al.  Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research , 2016, Comput. Intell. Neurosci..

[11]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Yeong-Il Park,et al.  Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition , 2002 .

[13]  Feng Xia,et al.  Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data , 2018, IEEE Transactions on Vehicular Technology.

[14]  Ilya V. Kolmanovsky,et al.  Optimally controlling Hybrid Electric Vehicles using path forecasting , 2009, 2009 American Control Conference.

[15]  Yang Bin,et al.  Multi-information integrated trip specific optimal power management for plug-in hybrid electric vehicles , 2009, 2009 American Control Conference.

[16]  G. Dimitrakopoulos,et al.  Intelligent Transportation Systems , 2010, IEEE Vehicular Technology Magazine.

[17]  Lei Wang,et al.  Offloading in Internet of Vehicles: A Fog-Enabled Real-Time Traffic Management System , 2018, IEEE Transactions on Industrial Informatics.

[18]  Gábor Stépán,et al.  Traffic jams: dynamics and control , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Latha Venkataraman.  Viewpoint: Benzene provides the missing link in molecular junctions , 2008 .

[20]  Guo Shuxia.  Cumulative Logistic Regression-Based Measurement Models of Road Traffic Congestion Intensity , 2008 .

[21]  Vincent Moulton,et al.  Analyzing mRNA expression identifies Smad3 as a microRNA-140 target regulated only at protein level. , 2010, RNA.

[22]  Guozhen Tan,et al.  Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms , 2012 .