Case-based Urban Road Travel Time Prediction

Abstract

Abstract:

Most existing travel time prediction methods focus on road segment forecasting for highways, urban expressways and trunk roads, while researches on path travel time prediction for route of urban common roads are relatively few. This paper proposes a case-based travel time prediction method. Based on trajectory data modeling, rich history travel paths with more attributes form into dynamic sample cases database, and a matching case are searched from the case database according to the travel time and environmental information. Finally, certain amendments are made to predict travel time. Experimental results show that the algorithm is weakly influenced by the impact of space network and data difference and can have a strong robustness and portability; in addition, the algorithm shows suitability to low trajectory data coverage, few trajectory data sample cases, as well as time accuracy and reliability, compared with the referenced existing road segment travel time prediction method.

Key words: intelligent transportation, travel time prediction, case- based reasoning, GPS trajectory, cases retrieving

摘要：

已有旅行时间预测方法多是针对高速公路、城市快速路和主干道路的路段，而针对城市一般道路，及路径旅行时间预测的研究则相对不足.本文提出一种基于案例的旅行时间预测算法.算法的基本原理为，在轨迹数据建模的基础上，建立动态更新的、包含多个属性的历史旅行样本数据库，然后根据出行时间及环境信息从案例库查找匹配案例，其结果经过一定修正用于预测路径旅行时间.与基于路段的旅行时间预测方法相比，该算法具有较强的鲁棒性和可移植性，受空间路网和数据样本量的影响较弱；并且在相同数据样本量和路网空间覆盖率的情况下，该算法预测精度高于对比算法.

关键词: 智能交通, 时间预测, 案例推理, GPS轨迹, 案例检索

CLC Number:

U495

XU Xian-rui, PENG Zhong-ren. Case-based Urban Road Travel Time Prediction[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4): 199-205.

徐先瑞，彭仲仁. 基于案例的城市道路行程时间预测[J]. 交通运输系统工程与信息, 2016, 16(4): 199-205.

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URL: http://www.tseit.org.cn/EN/abstract/abstract19246.shtml

http://www.tseit.org.cn/EN/Y2016/V16/I4/199

References

[1] 孙占全，刘威，朱效民. 大规模交通流预测方法研究[J]. 交通运输系统工程与信息, 2013,13(3): 121- 125. [SUN Z Q, LIU W, ZHU X M, et al. Traffic flow forecasting based on large scale traffic flow data[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(3):121-125.]

[2] YAN Z, PARENT C, SPACCAPIETRA S, et al. A hybrid model and computing platform for spatiosemantic trajectories[C]. In Proceedings of the 7th Extended Semantic Web Conference (ESWC), Heraklion, Greece, 2010.

[3] 赵建东，王浩，刘文辉，等. 基于收费数据的高速公路站间旅行时间预测[J]. 同济大学学报（自然科学版）, 2013, 41(12): 1849-1854. [ZHAO J D, WANG H, LIU W H, et al. Highway travel time prediction between stations based on toll ticket data[J]. Journal of Tongji University(Natural Science), 2013, 41(12): 1849-1854.]

[4] BAJWA S, CHUNG E, KUWAHARA M. Performance evaluation of an adaptive travel time prediction model[C]. In Proceeding of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, 2005.

[5] ZHENG F, ZUYLEN H V. Urban link travel time estimation based on sparse probe vehicle data[J]. Transportation Research Part C, 2013, 31(2): 145-157.

[6] LIU H, VAN ZUYLEN H, ZHANG K. Two distinct ways of using kalman filters to predict urban arterial travel time[C]// In Proceeding of the Intelligent Transportation Systems Conference, 2006, ITSC '06, IEEE, 2006:845- 850.

[7] 王翔，陈小鸿，杨祥妹. 基于K最近邻算法的高速公路短时行程时间预测[J]. 中国公路学报, 2015, 28(1): 102-111. [WANG X, CHEN X H, YANG X M. Short term prediction of expressway travel time based on k nearest neighbor algorithm[J]. China Journal of Highway and Transport, 2015, 28(1): 102-111.]

[8] CLARK S. Traffic prediction using multivariate nonparametric regression[J]. Journal of Transportation Engineering ASCE, 2003, 129(2): 161-168.

[9] AAMODT A, PLAZA E. Case- based reasoning: foundational issues, methodological variations and system approaches[J]. AI Communications, 1994, 7(1): 39-59.

[10] KWON J, BARKLEY T, HRANAC R, et al. Decomposition of travel time reliability into various sources: incidents, weather, work zones, special events, and base capacity[C]. In Proceedings of 90th Transportation Research Board(TRB) Annual Meeting, Washington D C, 2010.

[11] TU H, VAN LINT J W C, VAN ZUYLEN H J. The impact of adverse weather on travel time variability of freeway corridors[C]. In Proceedings of 86th Transportation Research Board(TRB) Annual Meeting, Washington D C, 2007.

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