Mining method of floating car data based on link travel time estimation

LI Hui-bing; YANG Xiao-guang; LUO Li-hua

Volume 14 Issue 6

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LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.

Citation:

LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.

LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.

Citation:

LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.

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Mining method of floating car data based on link travel time estimation

1.
School of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
2.
School of Transportation Engineering, Tongji University, Shanghai 201804, China

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Author Bio:
LI Hui-bing (1983-), male, lecturer, PhD, +86-21-38282347, hbli@shmtu.edu.cn
Received Date: 2014-06-28
Publish Date: 2014-12-25

Abstract

Abstract

Based on floating car data, a link travel time estimation method without signal timing data was proposed.The method consisted of four modules, which were intersection boundary dynamic partition module, link influence range partition module, floating car data extraction module, and link travel time estimation module, and the implementation of each module relied greatly on the output of previous one.According to vehicle travel state under the influence of signal control, link unit was divided into different segments by using density method in intersection boundary dynamic partition module and link influence range partition module.According to link travel time estimation mechanism, floating car data that were seriously affected by signal control were filtered off in floating car data extraction module, so the target floating car data could be obtained.Historical floating car data were excavated in link travel time estimation module, and floating car data were divided into 3 types according to different exsited regions of target data.Corresponding section travel time estimation methods were used for different types of data, and corresponding section travel time estimation models were established.Link travel time estimation method was simulated and verified by using software VISSIM, and its result was compared with the results of direct and indirect methods.Analysis result indicates that for coarse-grained floating car data, the average absolute error and average relative error of link travel time estimation method are 12 sand 8.67% respectively, so it performs better than traditional direct and indirect methods.
- intelligent transportation system,
- link travel time estimation,
- floating car data,
- signal timing,
- coarse-grained data

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References(20)

References

[1]	WANG Jian-qiang, NIU Hui-min. Graded-information feedback strategy in two-route systems under ATIS[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1 (2): 138-145. doi: 10.1016/S2095-7564(15)30098-2
[2]	WU C H, HO J M, LEE D T. Travel time prediction with support vector regression[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5 (4): 276-281. doi: 10.1109/TITS.2004.837813
[3]	SUN Lu, YANG Jun, MAHMASSANI H. Travel time estimation based on piecewise truncated quadratic speed trajectory[J]. Transportation Research Part A: Policy and Practice, 2008, 42 (1): 173-186. doi: 10.1016/j.tra.2007.08.004
[4]	ZHENG Fang-fang, ZUYLEN H. Comparison of urban link travel time estimation models based on probe vehicle data[C]∥MAO Bao-hua, TIAN Zong-zhong, HUANG Hai-jun, et al. Seventh International Conference on Traffic and Transportation Studies. Kunming: ASCE, 2010: 615-626.
[5]	ZHENG Fang-fang, ZUYLEN H. Urban link travel time estimation based on sparse probe vehicle data[J]. Transportation Research Part C: Emerging Technologies, 2013, 31 (1): 145-157. https://www.sciencedirect.com/science/article/pii/S0968090X12000575
[6]	BI Song, WANG Zhi-jian, HAN Cun-wu, et al. Estimation of left-turning travel time at traffic intersection[J]. The Journal of China Universities of Posts and Telecommunications, 2013, 20 (S1): 10-14. https://www.sciencedirect.com/science/article/pii/S1005888513602575
[7]	HAN Shu, LIN Hang-fei, CHEN Xiao-hong. The application of link aggregating algorithm in travel time estimate on signalized arterial network[C]∥PENG Qi-yuan, WANG K C P, QIU Yan-jun, et al. Second International Conference on Transportation Engineering. Chengdu: ASCE, 2009: 2315-2321.
[8]	JIANG Zhou, ZHANG Cun-bao, XIA Yin-xia. Travel time prediction model for urban road network based on multisource data[J]. Procedia-Social and Behavioral Sciences, 2014, 138: 811-818. doi: 10.1016/j.sbspro.2014.07.230
[9]	LI Jin-yan, ZHU Zheng-yu, LIU Lin, et al. Multi-step Kalman filtering travel time estimation method based on simplified road network model[J]. Systems Engineering—Theory and Practice, 2013, 33 (5): 1289-1297. (in Chinese). doi: 10.3969/j.issn.1000-6788.2013.05.026
[10]	ZHENG Fang-fang, WAN Yu, WU Ping-heng. Link traveltime prediction using extended exponential smoothing and Kalman filter in dynamic networks[C]∥LIU Rong-fang, ZHANG Jin, GUAN Chang-qian. The Eighth International Conference of Chinese Logistics and Transportation Professionals. Chengdu: ASCE, 2008: 3753-3759.
[11]	LIU H X, MA Wen-teng. A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials[J]. Transportation Research Part C: Emerging Technologies, 2009, 17 (1): 11-26. doi: 10.1016/j.trc.2008.05.002
[12]	JENELIUS E, KOUTSOPOULOS H N. Travel time estimation for urban road networks using low frequency probe vehicle data[J]. Transportation Research Part B: Methodological, 2013, 53: 64-81. doi: 10.1016/j.trb.2013.03.008
[13]	LI Jia, LIU Chun-hua, HU Sai-yang, et al. A travel time prediction model based on traffic data fusion technology[J]. Journal of Hunan University: Natural Sciences, 2014, 41 (1): 33-38. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201401006.htm
[14]	FANG Lu-ping, CHEN Shi-xiao, ZHAO Fei-fan. Average link travel time estimation based on floating car of small sample size[J]. Computer Simulation, 2012, 29 (9): 367-370. (in Chinese). doi: 10.3969/j.issn.1006-9348.2012.09.091
[15]	JIANG Gui-yan, CHANG An-de, ZHANG Wei. Comparison of link travel-time estimation methods based on GPS equipped floating car[J]. Journal of Jilin University: Engineering and Technology Edition, 2009, 39 (S2): 182-186. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY2009S2039.htm
[16]	JIANG Gui-yan, CHANG An-de, ZHANG Wei. Physical link travel-time estimation method based on GPS equipped floating car[J]. Highway, 2009 (11): 87-90. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL200911019.htm
[17]	JIANG Gui-yan, CHANG An-de, WU Chao-teng. Traffic information collection method based on GPS equipped floating car[J]. Journal of Jilin University: Engineering and Technology Edition, 2010, 40 (4): 971-975. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201004017.htm
[18]	HAN Shu, LIN Hang-fei, XIN Fei-fei. Method of merging urban road subsections based on floating car data[J]. Computer and Communications, 2007, 25 (5): 105-109. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200705031.htm
[19]	XIONG Ying-ge, XU Zhuo-li, LIU Hao-de. Methods of intersection dynamic subsections based on floating car data[J]. Journal of Transport Information and Safety, 2009, 27 (5): 38-43. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200905010.htm
[20]	HU Xiao-wen. Link travel time estimation based on probe data and fixed detector data[D]. Shanghai: Tongji University, 2008. (in Chinese).