A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data

Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS...

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Vydané v:	Automation in construction Ročník 72; s. 237 - 246
Hlavní autori:	Zhan, Xianyuan, Ukkusuri, Satish V., Yang, Chao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.12.2016 Elsevier BV
Predmet:	Bayesian analysis Bayesian mixture model Economic models EM algorithm Estimation Path inference Probabilistic methods Probability theory Scale (ratio) Short term Short-term average link travel time estimation Spatial dependencies Traffic engineering Traffic flow Traffic information Traffic management Traffic models Travel Travel time Trip based data with partial information Trip estimation Variance (statistics) Welfare economics Short-term average link travel time estimation Bayesian mixture model EM algorithm Trip based data with partial information Path inference
ISSN:	0926-5805, 1872-7891
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Abstract	Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework.
AbstractList	Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30 min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework. Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework.
Author	Zhan, Xianyuan Yang, Chao Ukkusuri, Satish V.
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Cites_doi	10.1016/j.trb.2007.08.005 10.1016/j.trc.2015.06.001 10.1111/j.2517-6161.1977.tb01600.x 10.1016/S0965-8564(01)00046-5 10.1109/TITS.2004.837813 10.1016/j.autcon.2006.11.001 10.1109/TPDS.2008.147 10.1016/j.trc.2011.05.014 10.1016/j.trc.2013.04.001 10.1016/j.trc.2009.04.003 10.3141/2192-13 10.1016/S0968-090X(03)00026-3 10.1016/j.trb.2005.11.003 10.3141/1617-23 10.1287/mnsc.17.11.712 10.1016/j.trc.2009.10.006 10.1016/j.trc.2012.04.007 10.1162/089976600300015664
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Keywords	Short-term average link travel time estimation Bayesian mixture model EM algorithm Trip based data with partial information Path inference
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References	De Freitas, Niranjan, Gee, Doucet (bb0020) 2000; 12 Yen (bb0115) 1971; 17 Li, Rose (bb0075) 2011; 19 Zhan, Li, Ukkusuri (bb0130) 2015; 57 Beijing Municipal Committee (bb0005) July 2012 Wu, Ho, Lee (bb0110) 2004; 5 Bishop (bb0010) 2006 Kurihara, Nakada, Yosui, Matsumoto (bb0065) 2001 Lu, Chen, Shen, Lam, Liu (bb0080) 2007; 16 Herrera, Work, Ban, Herring, Jacobson, Bayen (bb0040) 2010; 18 Coifman (bb0015) 2002; 36 Zhang, Rice (bb0135) 2003; 11 Park, Rilett (bb0095) 1998 Yeon, Elefteriadou, Lawphongpanich (bb0120) 2008; 42 Government of Hong Kong (bb0035) 2013 Zheng, Van Zuylen (bb0145) 2013; 31 NYCTLC (bb0085) 2012 Herring, Hofleitner, Abbeel (bb0045) September, 2010 Kwong, Kavaler, Rajagopal, Varaiya (bb0070) 2009; 17 Zhu, Li, Zhu, Ni (bb0150) 2009; 20 Zhan, Hasan, Ukkusuri, Kamga (bb0125) 2013; 33 Sherali, Desai, Rakha (bb0105) 2006; 40 Zheng, Van Zuylen (bb0140) 2010; 2192 Oh, J.S., Jayakrishnan, R., Recker, W., 2003. Section Travel Time Estimation from Point Detection Data. 82nd Annual Meeting of Transportation Research Board, Washington, DC, USA. Dempster, Laird, Rubin (bb0025) 1977 Rakha, El-Shawarby, Arafeh, Dion (bb0100) 2006 Hunter, Herring, Abbeel (bb0050) December 2009 Beijing Municipal Committee (10.1016/j.autcon.2015.12.007_bb0005) Kurihara (10.1016/j.autcon.2015.12.007_bb0065) 2001 Zheng (10.1016/j.autcon.2015.12.007_bb0140) 2010; 2192 Zhan (10.1016/j.autcon.2015.12.007_bb0125) 2013; 33 Sherali (10.1016/j.autcon.2015.12.007_bb0105) 2006; 40 Hunter (10.1016/j.autcon.2015.12.007_bb0050) 2009 Li (10.1016/j.autcon.2015.12.007_bb0075) 2011; 19 Government of Hong Kong (10.1016/j.autcon.2015.12.007_bb0035) 2013 Rakha (10.1016/j.autcon.2015.12.007_bb0100) 2006 Herring (10.1016/j.autcon.2015.12.007_bb0045) 2010 Bishop (10.1016/j.autcon.2015.12.007_bb0010) 2006 Coifman (10.1016/j.autcon.2015.12.007_bb0015) 2002; 36 Zhan (10.1016/j.autcon.2015.12.007_bb0130) 2015; 57 De Freitas (10.1016/j.autcon.2015.12.007_bb0020) 2000; 12 Yeon (10.1016/j.autcon.2015.12.007_bb0120) 2008; 42 Park (10.1016/j.autcon.2015.12.007_bb0095) 1998 Kwong (10.1016/j.autcon.2015.12.007_bb0070) 2009; 17 NYCTLC (10.1016/j.autcon.2015.12.007_bb0085) 2012 Zhu (10.1016/j.autcon.2015.12.007_bb0150) 2009; 20 Yen (10.1016/j.autcon.2015.12.007_bb0115) 1971; 17 Lu (10.1016/j.autcon.2015.12.007_bb0080) 2007; 16 Zheng (10.1016/j.autcon.2015.12.007_bb0145) 2013; 31 Dempster (10.1016/j.autcon.2015.12.007_bb0025) 1977 10.1016/j.autcon.2015.12.007_bb0090 Wu (10.1016/j.autcon.2015.12.007_bb0110) 2004; 5 Herrera (10.1016/j.autcon.2015.12.007_bb0040) 2010; 18 Zhang (10.1016/j.autcon.2015.12.007_bb0135) 2003; 11
References_xml	– volume: 5 start-page: 276 year: 2004 end-page: 281 ident: bb0110 article-title: Travel-time prediction with support vector regression publication-title: IEEE Trans. Intell. Transp. Syst. – year: 2012 ident: bb0085 article-title: New York City Taxi and Limousine Commission 2012 Annual Report – volume: 42 start-page: 325 year: 2008 end-page: 338 ident: bb0120 article-title: Travel time estimation on a freeway using discrete time Markov chains publication-title: Transp. Res. B Methodol. – year: 2013 ident: bb0035 article-title: Transport — Hong Kong: the Facts – start-page: 163 year: 1998 end-page: 170 ident: bb0095 article-title: Forecasting multiple-period freeway link travel times using modular neural networks publication-title: J. Transp. Res. Board – volume: 18 start-page: 568 year: 2010 end-page: 583 ident: bb0040 article-title: Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment publication-title: Transp. Res. C Emerg. Technol. – year: 2006 ident: bb0010 article-title: Pattern Recognition and Machine Learning – start-page: 19 year: September, 2010 end-page: 22 ident: bb0045 article-title: Estimating Arterial Traffic Conditions Using Sparse Probe Data publication-title: Proceedings of the ITS – volume: 12 start-page: 955 year: 2000 end-page: 993 ident: bb0020 article-title: Sequential Monte Carlo methods to train neural network models publication-title: Neural Comput. – start-page: 1 year: 1977 end-page: 38 ident: bb0025 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B Methodol. – volume: 11 start-page: 187 year: 2003 end-page: 210 ident: bb0135 article-title: Short-term travel time prediction publication-title: Transp. Res. Part C Emerg. Technol. – volume: 36 start-page: 899 year: 2002 end-page: 917 ident: bb0015 article-title: Vehicle reidentification and travel time measurement on congested freeways publication-title: Transp. Res. A Policy Pract. – volume: 20 start-page: 740 year: 2009 end-page: 752 ident: bb0150 article-title: HERO: online real-time vehicle tracking publication-title: IEEE Trans. Parallel and Distrib. Syst. – year: July 2012 ident: bb0005 article-title: The twelfth five-year plan for the transportation development of Beijing – start-page: 163 year: 2001 end-page: 172 ident: bb0065 article-title: Bayesian On-line Learning: A Sequential Monte Carlo with Importance Resampling publication-title: Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop – volume: 17 start-page: 712 year: 1971 end-page: 716 ident: bb0115 article-title: Finding the K shortest loopless paths in a network publication-title: Manag. Sci. – volume: 57 start-page: 85 year: 2015 end-page: 102 ident: bb0130 article-title: Lane-based real time queue length estimation using license plate recognition data publication-title: Transp. Res. Part C Emerg. Technol. – volume: 31 start-page: 145 year: 2013 end-page: 157 ident: bb0145 article-title: Urban link travel time estimation based on sparse probe vehicle data publication-title: Transp. Res. Part C Emerg. Technol. – volume: 17 start-page: 586 year: 2009 end-page: 606 ident: bb0070 article-title: Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors publication-title: Transp. Res. Part C Emerg. Technol. – year: 2006 ident: bb0100 article-title: Estimating Path Travel-time Reliability publication-title: Proceedings of the IEEE Intelligent Transportation Systems Conference 2006 – volume: 2192 start-page: 136 year: 2010 end-page: 146 ident: bb0140 article-title: Uncertainty and predictability of urban link travel time publication-title: Transp. Res. Rec. J. Transp. Res. Board – volume: 16 start-page: 647 year: 2007 end-page: 656 ident: bb0080 article-title: Positioning and tracking construction vehicles in highly dense urban areas and building construction sites publication-title: Autom. Constr. – volume: 19 start-page: 1006 year: 2011 end-page: 1018 ident: bb0075 article-title: Incorporating uncertainty into short-term travel time predictions publication-title: Transp. Res. Part C Emerg. Technol. – reference: Oh, J.S., Jayakrishnan, R., Recker, W., 2003. Section Travel Time Estimation from Point Detection Data. 82nd Annual Meeting of Transportation Research Board, Washington, DC, USA. – volume: 40 start-page: 857 year: 2006 end-page: 871 ident: bb0105 article-title: A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times publication-title: Transp. Res. B Methodol. – volume: 33 start-page: 37 year: 2013 end-page: 49 ident: bb0125 article-title: Urban link travel time estimation using large-scale taxi data with partial information publication-title: Transp. Res. Part C Emerg. Technol. – year: December 2009 ident: bb0050 article-title: Path and Travel Time Inference from GPS Probe Vehicle Data publication-title: Neural Information Processing Systems Foundation (NIPS) – volume: 42 start-page: 325 issue: 4 year: 2008 ident: 10.1016/j.autcon.2015.12.007_bb0120 article-title: Travel time estimation on a freeway using discrete time Markov chains publication-title: Transp. Res. B Methodol. doi: 10.1016/j.trb.2007.08.005 – year: 2013 ident: 10.1016/j.autcon.2015.12.007_bb0035 – volume: 57 start-page: 85 year: 2015 ident: 10.1016/j.autcon.2015.12.007_bb0130 article-title: Lane-based real time queue length estimation using license plate recognition data publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2015.06.001 – start-page: 1 year: 1977 ident: 10.1016/j.autcon.2015.12.007_bb0025 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B Methodol. doi: 10.1111/j.2517-6161.1977.tb01600.x – ident: 10.1016/j.autcon.2015.12.007_bb0005 – volume: 36 start-page: 899 issue: 10 year: 2002 ident: 10.1016/j.autcon.2015.12.007_bb0015 article-title: Vehicle reidentification and travel time measurement on congested freeways publication-title: Transp. Res. A Policy Pract. doi: 10.1016/S0965-8564(01)00046-5 – start-page: 19 year: 2010 ident: 10.1016/j.autcon.2015.12.007_bb0045 article-title: Estimating Arterial Traffic Conditions Using Sparse Probe Data publication-title: Proceedings of the ITS – year: 2012 ident: 10.1016/j.autcon.2015.12.007_bb0085 – volume: 5 start-page: 276 issue: 4 year: 2004 ident: 10.1016/j.autcon.2015.12.007_bb0110 article-title: Travel-time prediction with support vector regression publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2004.837813 – volume: 16 start-page: 647 year: 2007 ident: 10.1016/j.autcon.2015.12.007_bb0080 article-title: Positioning and tracking construction vehicles in highly dense urban areas and building construction sites publication-title: Autom. Constr. doi: 10.1016/j.autcon.2006.11.001 – volume: 20 start-page: 740 year: 2009 ident: 10.1016/j.autcon.2015.12.007_bb0150 article-title: HERO: online real-time vehicle tracking publication-title: IEEE Trans. Parallel and Distrib. Syst. doi: 10.1109/TPDS.2008.147 – volume: 19 start-page: 1006 issue: 6 year: 2011 ident: 10.1016/j.autcon.2015.12.007_bb0075 article-title: Incorporating uncertainty into short-term travel time predictions publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2011.05.014 – year: 2006 ident: 10.1016/j.autcon.2015.12.007_bb0100 article-title: Estimating Path Travel-time Reliability – volume: 33 start-page: 37 year: 2013 ident: 10.1016/j.autcon.2015.12.007_bb0125 article-title: Urban link travel time estimation using large-scale taxi data with partial information publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2013.04.001 – volume: 17 start-page: 586 year: 2009 ident: 10.1016/j.autcon.2015.12.007_bb0070 article-title: Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2009.04.003 – volume: 2192 start-page: 136 year: 2010 ident: 10.1016/j.autcon.2015.12.007_bb0140 article-title: Uncertainty and predictability of urban link travel time publication-title: Transp. Res. Rec. J. Transp. Res. Board doi: 10.3141/2192-13 – volume: 11 start-page: 187 issue: 3–4 year: 2003 ident: 10.1016/j.autcon.2015.12.007_bb0135 article-title: Short-term travel time prediction publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/S0968-090X(03)00026-3 – volume: 40 start-page: 857 issue: 10 year: 2006 ident: 10.1016/j.autcon.2015.12.007_bb0105 article-title: A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times publication-title: Transp. Res. B Methodol. doi: 10.1016/j.trb.2005.11.003 – year: 2009 ident: 10.1016/j.autcon.2015.12.007_bb0050 article-title: Path and Travel Time Inference from GPS Probe Vehicle Data publication-title: Neural Information Processing Systems Foundation (NIPS) – start-page: 163 issue: 98 year: 1998 ident: 10.1016/j.autcon.2015.12.007_bb0095 article-title: Forecasting multiple-period freeway link travel times using modular neural networks publication-title: J. Transp. Res. Board doi: 10.3141/1617-23 – volume: 17 start-page: 712 year: 1971 ident: 10.1016/j.autcon.2015.12.007_bb0115 article-title: Finding the K shortest loopless paths in a network publication-title: Manag. Sci. doi: 10.1287/mnsc.17.11.712 – volume: 18 start-page: 568 year: 2010 ident: 10.1016/j.autcon.2015.12.007_bb0040 article-title: Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment publication-title: Transp. Res. C Emerg. Technol. doi: 10.1016/j.trc.2009.10.006 – volume: 31 start-page: 145 year: 2013 ident: 10.1016/j.autcon.2015.12.007_bb0145 article-title: Urban link travel time estimation based on sparse probe vehicle data publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2012.04.007 – year: 2006 ident: 10.1016/j.autcon.2015.12.007_bb0010 – ident: 10.1016/j.autcon.2015.12.007_bb0090 – start-page: 163 year: 2001 ident: 10.1016/j.autcon.2015.12.007_bb0065 article-title: Bayesian On-line Learning: A Sequential Monte Carlo with Importance Resampling – volume: 12 start-page: 955 issue: 4 year: 2000 ident: 10.1016/j.autcon.2015.12.007_bb0020 article-title: Sequential Monte Carlo methods to train neural network models publication-title: Neural Comput. doi: 10.1162/089976600300015664
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Snippet	Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture...
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SubjectTerms	Bayesian analysis Bayesian mixture model Economic models EM algorithm Estimation Path inference Probabilistic methods Probability theory Scale (ratio) Short term Short-term average link travel time estimation Spatial dependencies Traffic engineering Traffic flow Traffic information Traffic management Traffic models Travel Travel time Trip based data with partial information Trip estimation Variance (statistics) Welfare economics
Title	A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data
URI	https://dx.doi.org/10.1016/j.autcon.2015.12.007 https://www.proquest.com/docview/1919588798
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