Volume 4, Issue 3, September 2018, Page: 100-105
Analysis and Prediction of Urban Traffic Congestion Based on Big Data
Zhenhua Wang, School of Information Engineering, China University of Geosciences, Beijing, China
Yangsen Yu, Geological Survey Institute, China University of Geosciences, Beijing, China
Dangchen Ju, School of Information Engineering, China University of Geosciences, Beijing, China
Received: Sep. 28, 2018;       Accepted: Oct. 10, 2018;       Published: Oct. 30, 2018
DOI: 10.11648/j.ijdst.20180403.14      View  1246      Downloads  74
Abstract
With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.
Keywords
Traffic Congestion, Big Data, Intelligent Transportation System, Road Capacity
To cite this article
Zhenhua Wang, Yangsen Yu, Dangchen Ju, Analysis and Prediction of Urban Traffic Congestion Based on Big Data, International Journal on Data Science and Technology. Vol. 4, No. 3, 2018, pp. 100-105. doi: 10.11648/j.ijdst.20180403.14
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Pedro Wences; Alicia Martinez; Hugo Estrada; et al. Decision-Making Intelligent System for Passenger of Urban Transports [J]. Ubiquitous Computing and Ambient Intelligence, 2017, Vol. 10586: 128-139.
[2]
Guan, Shuqi; Yun, Jun; Zhang, Qinkang; et al. Intelligent transportation system contributions to the operating efficiency of Urban traffic [J]. Journal of Intelligent & Fuzzy Systems, 2016, Vol. 31(4): 2213-2220.
[3]
Li Zhijie, Li Yuanxiang, Wang Feng, et al. Overview of online learning algorithms for big data analysis [J]. Computer Research and Development. 2015(08).
[4]
Hu Peifeng. Research on short-term forecasting method of traffic flow [D]. Beijing Jiaotong University 2007.
[5]
Qin Ming, Zhang Wenqiang, Zhong Xianfei, et al. Space-time optimization of intersection based on Kalman filter traffic prediction [J]. Science and Technology Plaza. 2015(01).
[6]
Yang Qingfang, Zhang Wei, Gao Peng. Short-term Forecasting Method of Traffic Volume Based on Improved Dynamic Recurrent Neural Network [J]. Journal of Jilin University (Engineering Science). 2012(04).
[7]
Dong Rui, Jia Yuanhua, Qi Guchang. Traffic Flow Prediction Based on Wavelet Denoising and Chaotic Time Series [J]. Science Technology and Engineering. 2010(31).
[8]
Jiao Guangting, Xu Jianbiao, Ma Yonghong. Research on Traffic Volume Forecasting Method Based on QPSO-RBF [J]. Transportation and Computer. 2008(04).
[9]
Tan Manchun, Li Yingjun, Xu Jianbiao. Combined Traffic Flow Prediction Based on ARIMA and SVM Based on Wavelet Denoising [J]. Journal on Highway and Transport Engineering. 2009(07).
[10]
Catalin Gosman; Tudor Cornea; Ciprian Dobre; et al. Controlling and filtering users data in Intelligent Transportation System [J]. Future Generation Computer Systems, 2018, Vol. 78: 807-816.
[11]
Chin, Cheng Siong; Zhong, Xionghu; Hamdan, Mohammad; et al. Intelligent Autonomous Transport Systems Design and Simulation [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2018, 1-2.
[12]
LIN Chi, XU Bo, XUE Yuwei, YU Cheng. Experimental Platform for Intelligent Data Protection of Big Data in Intelligent Transportation [J]. Laboratory Research and Exploration, 2017, Vol. 36 (7): 39-42, 49.
[13]
The Experience and Enlightenment of American Intelligent Traffic Management Experience [J]. Road traffic science and technology, 2016, (6): 42-50.
[14]
Wen Huimin, Quan Yuxiang, Sun Jianping. The Development Direction of Urban Intelligent Transportation System in the Age of Big Data [J]. Urban Transport, 2017, Vol. 15 (5): 20-25.
[15]
Agachai Sumalee; Hung Wai Ho. Smarter and more connected: Future intelligent transportation system [J]. IATSS Research, 2018.
[16]
Zhao Na, Yuan Jiabin, Xu Wei. Summary of Intelligent Transportation System [J]. computer science, 2014, (11): 7-11,45.
[17]
LU Huapu, SUN Zhiyuan, QU Wencong. A Review of Big Data and Its Application in Urban Intelligent Transportation Systems [J]. Journal of Transportation Systems Engineering and Engineering, 2015, Vol. 15 (5): 45-52.
[18]
Shaojun Zhang; Tianlin Niu; Ye Wu; et al. Fine-grained vehicle emission management using intelligent transportation system data [J]. Environmental Pollution, 2018.
[19]
Bao Yuchen, Xie Xiaoyan, Gao Lei. Data technology of intelligent transportation in the era of big data [J]. Chinese information, 2018, (5): 5.
[20]
Feng Kai. Development status and trend of urban intelligent transportation system [J]. Global market information guide, 2017, (1): 111-112.
[21]
Jinchao Wu; Bokui Chen; Kai Zhang; et al. Ant pheromone route guidance strategy in intelligent transportation systems [J]. Physica A: Statistical Mechanics and its Applications, 2018, Vol. 503: 591-603.
[22]
Wei Yanfang, Chen Peng. Discussion on the theory of urban intelligent transportation [J]. urban construction under the Internet + background (2017), (31): 201-202.
[23]
Zhang Hong, Wang Xiaoming, Cao Jie, et al. Intelligent Transportation System Architecture Based on Big Data [J]. Journal of Lanzhou University of Technology, 2015, Vol. 41 (2): 112-115.
[24]
Chilà Giovanna; Musolino Giuseppe; Polimeni Antonio; et al. Transport models and intelligent transportation system to support urban evacuation planning process [J]. IET Intelligent Transport Systems, 2016, Vol. 10(4): 279-286.
[25]
Yang Yong. Henan Intelligent Traffic Management System Based on Big Data [D]. Beijing University of Technology, 2016.
[26]
Zhang Peng. Analysis of Intelligent Traffic Signal Control System Based on Traffic Big Data [J]. Science and Informatization, 2018, (6): 17-18.
[27]
Sergey Sheptunov; Yuriy Solomentsev; Nataliya Suhanova; et al. Improvement of the urban transport management system with elements of artificial intelligence. [J]. Bulletin of Bryansk State Technical University, 2016,: 93-98.
[28]
Wang Peng. Research on urban intelligent traffic controller based on embedded system [D]. University of Science and Technology Liaoning, 2016.
[29]
Hacène Fouchal; Emilien Bourdy; Geoffrey Wilhelm; et al. A validation tool for cooperative intelligent transport systems [J]. Journal of Computational Science, 2017, Vol. 22: 283-288.
[30]
Yen-Wen Lin; Yuan-Kai Hsiao; Zih-Shiuan Yeh. A New Mobility Management Scheme for Intelligent Transportation Systems [J]. Wireless Personal Communications, 2017, Vol. 96(2): 3081-3112.
[31]
Alfred Daniel; Anand Paul; Awais Ahmad; et al. Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS) [J]. Wireless Personal Communications, 2016, Vol. 87(2): 461-484.
[32]
J Jariyasunant; A Carrel; V Ekambaram; et al. The Quantified Traveler: Changing transport behavior with personalized travel data feedback [J]. The Ninth China (International) urban intelligent transportation Forum, 2012.
[33]
Lu Hua Pu, Sun Zhiyuan, Qu Wen cog. Large data and its application in urban intelligent transportation system [J]. transportation system engineering and information, 2015, Fifteenth volumes (5): 45-52.
[34]
Wen Huimin, Yu Xiang, Sun Jianping. The development direction of Urban Intelligent Transportation System in the age of big data [J]. urban traffic, 2017, Fifteenth volume (5): 20-25.
[35]
Li Jun. Research on dynamic path planning in urban intelligent transportation [D]. Hangzhou Dianzi University, 2016.
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