[1]HUANG Shunlun,DU Chun,SONG Baoquan,et al.Urban link travel time estimation using taxi data[J].CAAI Transactions on Intelligent Systems,2017,12(6):790-798.[doi:10.11992/tis.201706071]
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Urban link travel time estimation using taxi data
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 6
Page number:
790-798
Column:
学术论文—机器学习
Public date:
2017-12-25
- Title:
- Urban link travel time estimation using taxi data
- CLC:
- TP311
- DOI:
- 10.11992/tis.201706071
- Abstract:
- The accurate estimation of urban link travel time plays a significant role in urban traffic monitoring and supervision. Using taxicab GPS trip data, which contains origin and destination locations, travel time, and distances, this paper establishes a model to estimate average short-term urban link travel times. Firstly, the urban road network is divided into many segments based on crossings, and the running route of the driver was analyzed using the k-shortest path search algorithm. Then, for each road segment, a polynomial incidence relation model of the travel time in double lanes is proposed; this increases precision and avoids the overfitting of the travel time of the road network caused by insufficient training data. Finally, by minimizing the mean square error between the expected path travel time and the observed path travel time as the optimization objective, the travel time of the road network is fitted. The results of experiments conducted on New York taxi datasets show that, relative to the traditional single-lane estimation method, the proposed model and method more efficiently estimate the travel time of the road segments in urban road networks.
- References:
-
[1] COIFMAN B, CASSIDY M. Vehicle reidentification and travel time measurement on congested freeways[J]. Transportation research part a: policy and practice, 2002, 36(10): 899-917.
[2] ZHANG X, ZHANG B, LIU L, et al. Estimating foliar nitrogen concentration with hyperspectral remote sensing image[C]//Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space. Beijing, 2003: 187-193.
[3] WU C C, LEE W M G. Control of vaporous naphthalene by scrubbing with surfactants[J]. Journal of environmental engineering, 2004, 130(3): 276-281.
[4] PARK D, RILETT L. Forecasting multiple-period freeway link travel times using modular neural networks[J]. Transportation research record: journal of the transportation research board, 1998(1617): 163-170.
[5] LI R, ROSE G. Incorporating uncertainty into short-term travel time predictions[J]. Transportation research part c: emerging technologies, 2011, 19(6): 1006-1018.
[6] YEON J, ELEFTERIADOU L, LAWPHONGPANICH S. Travel time estimation on a freeway using discrete time Markov chains[J]. Transportation research part B: methodological, 2008, 42(4): 325-338.
[7] HASAN S, CHOUDHURY C, BEN-AKIVA M, et al. Modeling of travel time variations on urban links in London[J]. Transportation research record: journal of the transportation research board, 2011(2260): 1-7.
[8] HERRERA J C, WORK D B, HERRING R, et al. Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment[J]. Transportation research part c: emerging technologies, 2010, 18(4): 568-583.
[9] ZHAN X, ZHENG Y, Yi X, et al. Citywide traffic volume estimation using trajectory data[J]. IEEE transactions on knowledge and data engineering, 2017, 29(2): 272-285.
[10] ZHENG F, VAN ZUYLEN H. Urban link travel time estimation based on sparse probe vehicle data[J]. Transportation research part c: emerging technologies, 2013, 31(0): 145-157.
[11] HUNTER T, HERRING R, ABBEEL P, et al. Path and travel time inference from GPS probe vehicle data[J]. NIPS analyzing networks and learning with graphs, 2009, 12(1).
[12] HERRING R, HOFLEITNER A, ABBEEL P, et al. Estimating arterial traffic conditions using sparse probe data[C]//13th International IEEE Conference on Intelligent Transportation Systems. Madeira Island, Portugal, 2010: 929-936.
[13] Taxi data from new york taxi and limousine commission. [2016-05-14] http://www.nyc.gov/html/tlc/html/home/home.shtml.
[14] ZHAN X, HASAN S, UKKUSURI S V, et al. Urban link travel time estimation using large-scale taxi data with partial information[J]. Transportation research part c: emerging technologies, 2013, 33(0): 37-49.
[15] ZHAN X, UKKUSURI S V, YANG C. A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data[J]. Automation in construction, 2016, 72: 237-246.
[16] YEN J Y. Finding the k shortest loopless paths in a network[J]. Management science, 1971, 17(11): 712-716.
[17] DAGANZO C. Multinomial probit: the theory and its application to demand forecasting[M]. Elsevier, 2014.
[18] CHEN Y, OLIVER D S. Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification[J]. Computational geosciences, 2013, 17(4): 689-703.
[19] BONNANS J F, GILBERT J C, LEMARéCHAL C, et al. Numerical optimization: theoretical and practical aspects [M]. Springer Science and Business Media, 2013.
[20] KING D A, PETERS J R, DAUS M W. Taxicabs for improved urban mobility: are we missing an opportunity [C]//Transportation Research Board 91st Annual Meeting. Washington DC, USA, 2012 (12-2097).
[21] FOSGERAU M, FUKUDA D. Valuing travel time variability: Characteristics of the travel time distribution on an urban road[J]. Transportation research part c: emerging technologies, 2012, 24(0): 83-101.
[22] 孙锋, 黄玲, 叶盈,等. 混行条件下直线式公交站点停靠车辆数优化[J]. 哈尔滨工程大学学报. 2015, 36(2): 152-155.
SUN Feng, HUANG Ling, YE Ying, et al. Optimizing the number of buses stopping at on-line stops under mixed traffic conditions[J]. Journal of Harbin Engineering University, 2015, 36(2): 152-155.
[23] 徐程, 曲昭伟, 陶鹏飞,等. 动态交通数据异常值的实时筛选与恢复方法[J]. 哈尔滨工程大学学报, 2016. 37(2): 211-217.
XU Cheng, QU Zhaowei, TAO Pengfei, et al. Methods of real-time screening and reconstruction for dynamic traffic abnormal data[J]. Journal of Harbin Engineering University, 2016, 37(2): 211-217.
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Last Update:
2018-01-03