Research on Travel Time Prediction under the Condition of Urban Rainstorm in Overpass Area

Abstract:

Overpass is an important hub for urban road network facility, its traffic capacity severely restricts that of the entire road network. Since overpass area is easy to gather water in urban road network, rain water under the overpass is an important incentive for traffic jams. In this paper, a reliable and easily maintainable method is discussed to detect the depth of the road surface water, which designs and implements a monitoring system of urban road network ponding depth. Based on this, technique of predicting travel time has been researched about overpass area under water-logging condition. Through a real example, the technique discussed in this paper has been proved to be highly effective and veracious, and can be used to provide basic data for traffic guidance to plan out sound routes.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 5565-5570
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.5565
• Online since: July 2014
• Authors: Song Bi*, Zhong Cheng Zhao, Guan Wang, Lin Kong, Qi Diao, Cun Wu Han, De Hui Sun
• Keywords: Overpass Area, Ponding Depth Detection, Traffic Guidance, Travel Time Prediction, Urban Road Network

References

[1] Xinmei Sun. The countermeasures to solve the problem of urban traffic Jams [J]. Economist, 2010 (9) 62-63.

[2] Shiquan Zhong, Shoufeng Ma, Guoguang He. Description and simulation of dynamic route guidance system road network [J]. JOURNAL of Systems Engineering, 2009 24 (2) 25-38.

[3] Meng Li, Zhijun Zou, Fanping Bu. Arterial Travel Time Estimation Using VII Probe Data and Point-based Detection Data [C]. 13th International IEEE Annual Conference on Intelligent Transportation Systems, 2010. 1764-1769.

DOI: 10.1109/itsc.2010.5625209

[4] Dewang Chen, Kun Zhang, Tao Liao. Practical Travel Time Prediction Algorithms Based on Neural Network and Data Fusion for Urban Expressway [C]. Sixth International Conference on Natural Computation, 2010. 1754-1758.

DOI: 10.1109/icnc.2010.5584403

[5] Mandoye Ndoye, Virgil F. Totten, James V. Krogmeier, et. al. Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation. IEEE Transactions on Intelligent Transportation Systems, 2011 12 (1) 119-131.

DOI: 10.1109/tits.2010.2092769

[6] Peng Hao, Xuegang Ban, Kristin P. Bennett, et. al. Signal Timing Estimation Using Sample Intersection Travel Times. IEEE Transactions on Intelligent Transportation System, 2012 13 (2) 792-804.

DOI: 10.1109/tits.2012.2187895

[7] Jiancheng Long, Ziyou Gao, W.Y. Szeto. Discretised link travel time models based on cumulative flows: Formulations and properties [J]. Transportation Research Part B: Methodological, 2013 45 (1) 232-254.

DOI: 10.1016/j.trb.2010.05.002

[8] Li Li, Xiqun Chen, Zhiheng Li, et. al. Freeway Travel-Time Estimation Based on Temporal–Spatial Queueing Mode [J]. IEEE Transactions on Intelligent Transportation Systems, 2013 14 (1) 1536-1541.

DOI: 10.1109/tits.2013.2256132

[9] Zhaosheng Yang, Xueying Gao, Ciyun Lin, et. al. The link travel time estimation based on the detecting coil downstream sections [J]. Journal of Beijing University of Technology, 2010 36 (6) 790-795.

[10] Wei Zhang, Simin Li, Guangyin Sun. Effect mechanism of urban rainstorm water-logging formation in China [J]. Urban Development Studies. 2013 20 (1) 120-122.

[11] Yun Zhang, Ou Huang, Xiangrui Chen, et. al. Water accumulation causes caused by the urban water-logging of the recessed overpass in Beijing and control measures [J]. China Municipal Engineering. 2013 168 62-64.

[12] Shihong Chen, Saiqiong Peng. Application of ArcGIS on the problem of transportation route selection in urban water-logging [J]. Computer Knowledge and Technology. 2013 9 (1) 205-206.

[13] Yongbo Wang, Guohui Tao, Xueyi Jing, et, al. Research of urban rainstorm water-logging monitoring and early warning system in Harbin [C]. The 26th annual meeting at the Chinese academy of meteorological disaster and weather event early warning, forecasting and disaster prevention and mitigation at the venue. 2009. 1192-1197.