Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data
•Urban network-level traffic states inference model using partially available data.•Complete solution for link queue lengths and travel times inference using LPR data.•New framework combines traffic flow theory and customized machine learning models.•Applicable to LPR data and other similar vehicle...
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| Veröffentlicht in: | Transportation research. Part C, Emerging technologies Jg. 117; S. 102660 |
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| Sprache: | Englisch |
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| Abstract | •Urban network-level traffic states inference model using partially available data.•Complete solution for link queue lengths and travel times inference using LPR data.•New framework combines traffic flow theory and customized machine learning models.•Applicable to LPR data and other similar vehicle re-identification data.•The framework is efficient, calibration-free and easily deployable in real world.
License-plate recognition (LPR) data are emerging data sources in urban transportation systems which contain rich information. Large-scale LPR systems have seen rapid development in many parts of the world. However, limited by privacy considerations, LPR data are seldom available to the research community, which lead to huge research gap in data-driven applications. In this study, we propose a complete solution using LPR data for link-based traffic state estimation and prediction for arterial networks. The proposed integrative data-driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network. The framework contains three novel data-driven sub-components that are highly customized based on the characteristics of LPR data, including: a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences; a light-weighted queue length approximation model to estimate lane-based cycle maximum queue lengths and a network-wide traffic state inference model to perform network-level estimation and prediction using partially observed data. This study exploits and utilizes the unique features of LPR data and other similar vehicle re-identification data for urban network-wide link-based traffic state estimation and prediction. A six days’ LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link-level field experiment dataset are used to validate the model. Numerical results show that the framework provides good estimation and prediction accuracy. The proposed framework is efficient and calibration-free, which can be easily implemented in urban networks for various real-time traffic monitoring and control applications. |
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| AbstractList | •Urban network-level traffic states inference model using partially available data.•Complete solution for link queue lengths and travel times inference using LPR data.•New framework combines traffic flow theory and customized machine learning models.•Applicable to LPR data and other similar vehicle re-identification data.•The framework is efficient, calibration-free and easily deployable in real world.
License-plate recognition (LPR) data are emerging data sources in urban transportation systems which contain rich information. Large-scale LPR systems have seen rapid development in many parts of the world. However, limited by privacy considerations, LPR data are seldom available to the research community, which lead to huge research gap in data-driven applications. In this study, we propose a complete solution using LPR data for link-based traffic state estimation and prediction for arterial networks. The proposed integrative data-driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network. The framework contains three novel data-driven sub-components that are highly customized based on the characteristics of LPR data, including: a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences; a light-weighted queue length approximation model to estimate lane-based cycle maximum queue lengths and a network-wide traffic state inference model to perform network-level estimation and prediction using partially observed data. This study exploits and utilizes the unique features of LPR data and other similar vehicle re-identification data for urban network-wide link-based traffic state estimation and prediction. A six days’ LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link-level field experiment dataset are used to validate the model. Numerical results show that the framework provides good estimation and prediction accuracy. The proposed framework is efficient and calibration-free, which can be easily implemented in urban networks for various real-time traffic monitoring and control applications. |
| ArticleNumber | 102660 |
| Author | Li, Ruimin Zhan, Xianyuan Ukkusuri, Satish V. |
| Author_xml | – sequence: 1 givenname: Xianyuan surname: Zhan fullname: Zhan, Xianyuan email: zhanxianyuan@jd.com organization: JD Intelligent City Research, JD Digits, Beijing 101111, China – sequence: 2 givenname: Ruimin surname: Li fullname: Li, Ruimin email: lrmin@tsinghua.edu.cn organization: Department of Civil Engineering, Tsinghua University, Beijing 100084, China – sequence: 3 givenname: Satish V. surname: Ukkusuri fullname: Ukkusuri, Satish V. email: sukkusur@purdue.edu organization: Lyles school of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA |
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| Cites_doi | 10.1109/TPDS.2008.147 10.1016/j.trc.2011.05.014 10.1016/j.trc.2013.04.001 10.1016/j.trc.2015.06.001 10.1016/j.trpro.2017.05.214 10.1109/TITS.2015.2405759 10.1016/j.trc.2011.01.002 10.1109/TITS.2012.2187895 10.1007/s11116-014-9541-6 10.1016/j.trc.2009.04.003 10.1016/j.trc.2009.02.003 10.1109/ITSC.2010.5624994 10.1016/j.trb.2007.08.005 10.1016/j.trc.2012.04.007 10.1145/1014052.1014089 10.1109/TITS.2010.2049105 10.1016/j.trb.2005.11.003 10.1016/j.trc.2017.07.006 10.1016/j.trc.2007.03.001 10.1145/2623330.2623656 10.1155/2017/1738085 10.3141/1617-23 10.1162/089976699300016674 10.1016/j.aap.2016.07.030 10.1098/rsta.2011.0550 10.1109/TITS.2007.899720 10.1016/S0968-090X(03)00026-3 10.7551/mitpress/3206.001.0001 10.1080/15472450802023337 10.1016/j.trc.2007.06.002 10.1007/BFb0053999 10.1016/j.trc.2009.10.006 10.1016/j.trb.2012.03.006 10.1177/0361198119844756 10.1023/A:1018628609742 10.3141/2035-08 10.1016/j.autcon.2015.12.007 10.3141/2024-11 10.1109/ACCESS.2018.2873569 10.1109/TKDE.2016.2621104 10.1109/TITS.2014.2323341 10.1109/TITS.2004.837813 |
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| Keywords | Arterial network Traffic state estimation Gaussian process License plate recognition data Hybrid dynamic Bayesian network Signal timing inference Queue length approximation |
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| References | Zhan, Zheng, Yi, Ukkusuri (b0280) 2017; 29 Zhan, Li, Ukkusuri (b0270) 2015; 57 Beijing Traffic Management Bureau, 2017. Location table of fixed traffic monitoring equipment. Zhan, Hasan, Ukkusuri, Kamga (b0265) 2013; 33 Dempster, Laird, Rubin (b0060) 1977 Huang, Darwiche (b0105) 1994; 11 Berger (b0025) 2013 Kwong, Kavaler, Rajagopal, Varaiya (b0130) 2009; 17 Axer, Friedrich (b0010) 2017; 25 Koller, Friedman (b0125) 2009 Hao, Ban, Bennett, Ji, Sun (b0080) 2012; 13 Zhang, Rice (b0285) 2003; 11 Bertini, Lasky, Monsere (b0030) 2005 Murphy (b0150) 2001; 33 Murphy (b0155) 1998 Yasin, Karim, Abdullah (b0255) 2010; 8 Zhang, Xie (b0290) 2007; 2024 Wu, Ho, Lee (b0240) 2004; 5 Ghahramani, Z., 1998. Learning dynamic bayesian networks, in: Adaptive processing of sequences and data structures. Springer, pp. 168–197. Vigos, Papageorgiou, Wang (b0225) 2008; 16 Skabardonis, Geroliminis (b0205) 2008; 12 Xumei, Huibo, Wang (b0250) 2012; 12 Zhan, Ukkusuri, Yang (b0275) 2016; 72 . Williams, C.K., Rasmussen, C.E., 2006. Gaussian processes for machine learning. the MIT Press 2, 4. Coifman, Cassidy (b0045) 2002; 36 Roweis, Ghahramani (b0185) 1999; 11 Cowell, Dawid, Lauritzen, Spiegelhalter (b0055) 2006 Zhu, Li, Zhu, Ni (b0300) 2009; 20 Ahmadi, Jahangiri, Berardi, Machiani (b0005) 2018 Vickrey (b0220) 1969; 59 Wang, Y., Zheng, Y., Xue, Y., 2014. Travel time estimation of a path using sparse trajectories, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 25–34. Bishop (b0035) 2006 Du, Yan, Zhu, Sun (b0065) 2019; 2673 Mo, Li, Zhan (b0145) 2017; 82 Fayazi, Vahidi, Mahler, Winckler (b0070) 2014; 16 Jahangiri, Rakha (b0115) 2015; 16 Oh, Ritchie, Jeng (b0170) 2007; 8 Hunter, T., Herring, R., Abbeel, P., Bayen, A., 2009. Path and travel time inference from gps probe vehicle data. NIPS Analyzing Networks and Learning with Graphs 12. He, X., Das, R., 2015. RFID in China 2015-2025: forecasts, players, opportunities. Technical Report. IDTechEX Research. Tseng, Hsueh, Tseng, Yang, Chao, Chou (b0215) 2018; 6 Chen, Yang, Xu (b0040) 2017; 2017 Li, Rose (b0135) 2011; 19 Suykens, Vandewalle (b0210) 1999; 9 Murphy (b0160) 2002 Roberts, Osborne, Ebden, Reece, Gibson, Aigrain (b0180) 2013; 371 Shafique, Hato (b0190) 2015; 42 Sherali, Desai, Rakha (b0200) 2006; 40 Wu, X., Srihari, R., 2004. Incorporating prior knowledge with weighted margin support vector machines, in: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 326–333. Herring, R., Hofleitner, A., Abbeel, P., Bayen, A., 2010. Estimating arterial traffic conditions using sparse probe data, in: Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, IEEE. pp. 929–936. Yeon, Elefteriadou, Lawphongpanich (b0260) 2008; 42 Herrera, Work, Herring, Ban, Jacobson, Bayen (b0090) 2010; 18 Liu, Wu, Ma, Hu (b0140) 2009; 17 Park, Rilett (b0175) 1998 Hofleitner, Herring, Bayen (b0100) 2012; 46 Ni, Wang, Sun, Li (b0165) 2016; 96 Ban, Hao, Sun (b0015) 2011; 19 Jeng, Tok, Ritchie (b0120) 2010; 11 Zheng, VanZuylen (b0295) 2013; 31 Sharma, Bullock, Bonneson (b0195) 2007 Coifman, Krishnamurthy (b0050) 2007; 15 Zhu (10.1016/j.trc.2020.102660_b0300) 2009; 20 Park (10.1016/j.trc.2020.102660_b0175) 1998 10.1016/j.trc.2020.102660_b0020 Cowell (10.1016/j.trc.2020.102660_b0055) 2006 Murphy (10.1016/j.trc.2020.102660_b0155) 1998 Huang (10.1016/j.trc.2020.102660_b0105) 1994; 11 Mo (10.1016/j.trc.2020.102660_b0145) 2017; 82 Berger (10.1016/j.trc.2020.102660_b0025) 2013 Chen (10.1016/j.trc.2020.102660_b0040) 2017; 2017 Tseng (10.1016/j.trc.2020.102660_b0215) 2018; 6 Murphy (10.1016/j.trc.2020.102660_b0150) 2001; 33 Axer (10.1016/j.trc.2020.102660_b0010) 2017; 25 10.1016/j.trc.2020.102660_b0235 10.1016/j.trc.2020.102660_b0230 10.1016/j.trc.2020.102660_b0110 Fayazi (10.1016/j.trc.2020.102660_b0070) 2014; 16 10.1016/j.trc.2020.102660_b0075 Ahmadi (10.1016/j.trc.2020.102660_b0005) 2018 Yeon (10.1016/j.trc.2020.102660_b0260) 2008; 42 Zhan (10.1016/j.trc.2020.102660_b0270) 2015; 57 Shafique (10.1016/j.trc.2020.102660_b0190) 2015; 42 Wu (10.1016/j.trc.2020.102660_b0240) 2004; 5 Zhan (10.1016/j.trc.2020.102660_b0275) 2016; 72 Jahangiri (10.1016/j.trc.2020.102660_b0115) 2015; 16 Dempster (10.1016/j.trc.2020.102660_b0060) 1977 Liu (10.1016/j.trc.2020.102660_b0140) 2009; 17 Coifman (10.1016/j.trc.2020.102660_b0050) 2007; 15 Hao (10.1016/j.trc.2020.102660_b0080) 2012; 13 Oh (10.1016/j.trc.2020.102660_b0170) 2007; 8 Du (10.1016/j.trc.2020.102660_b0065) 2019; 2673 Zhang (10.1016/j.trc.2020.102660_b0290) 2007; 2024 Jeng (10.1016/j.trc.2020.102660_b0120) 2010; 11 Li (10.1016/j.trc.2020.102660_b0135) 2011; 19 Bishop (10.1016/j.trc.2020.102660_b0035) 2006 10.1016/j.trc.2020.102660_b0245 Hofleitner (10.1016/j.trc.2020.102660_b0100) 2012; 46 Sherali (10.1016/j.trc.2020.102660_b0200) 2006; 40 Vigos (10.1016/j.trc.2020.102660_b0225) 2008; 16 Zhan (10.1016/j.trc.2020.102660_b0280) 2017; 29 Ban (10.1016/j.trc.2020.102660_b0015) 2011; 19 Koller (10.1016/j.trc.2020.102660_b0125) 2009 10.1016/j.trc.2020.102660_b0085 Vickrey (10.1016/j.trc.2020.102660_b0220) 1969; 59 Roberts (10.1016/j.trc.2020.102660_b0180) 2013; 371 Suykens (10.1016/j.trc.2020.102660_b0210) 1999; 9 Coifman (10.1016/j.trc.2020.102660_b0045) 2002; 36 Skabardonis (10.1016/j.trc.2020.102660_b0205) 2008; 12 Herrera (10.1016/j.trc.2020.102660_b0090) 2010; 18 Roweis (10.1016/j.trc.2020.102660_b0185) 1999; 11 Murphy (10.1016/j.trc.2020.102660_b0160) 2002 Zheng (10.1016/j.trc.2020.102660_b0295) 2013; 31 Sharma (10.1016/j.trc.2020.102660_b0195) 2007 10.1016/j.trc.2020.102660_b0095 Bertini (10.1016/j.trc.2020.102660_b0030) 2005 Xumei (10.1016/j.trc.2020.102660_b0250) 2012; 12 Ni (10.1016/j.trc.2020.102660_b0165) 2016; 96 Zhang (10.1016/j.trc.2020.102660_b0285) 2003; 11 Kwong (10.1016/j.trc.2020.102660_b0130) 2009; 17 Yasin (10.1016/j.trc.2020.102660_b0255) 2010; 8 Zhan (10.1016/j.trc.2020.102660_b0265) 2013; 33 |
| References_xml | – year: 2009 ident: b0125 article-title: Probabilistic graphical models: principles and techniques – year: 1998 ident: b0155 article-title: Inference and learning in hybrid Bayesian networks – volume: 2017 start-page: 1738085 year: 2017 ident: b0040 article-title: Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data publication-title: J. Adv. Transport. – volume: 13 start-page: 792 year: 2012 end-page: 804 ident: b0080 article-title: Signal timing estimation using sample intersection travel times publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 9 start-page: 293 year: 1999 end-page: 300 ident: b0210 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. – start-page: 163 year: 1998 end-page: 170 ident: b0175 article-title: Forecasting multiple-period freeway link travel times using modular neural networks publication-title: Transport. Res. Rec.: J. Transp. Res. Board – year: 2006 ident: b0055 article-title: Probabilistic networks and expert systems: Exact computational methods for Bayesian networks – reference: He, X., Das, R., 2015. RFID in China 2015-2025: forecasts, players, opportunities. Technical Report. IDTechEX Research. – volume: 42 start-page: 163 year: 2015 end-page: 188 ident: b0190 article-title: Use of acceleration data for transportation mode prediction publication-title: Transportation – volume: 72 start-page: 237 year: 2016 end-page: 246 ident: b0275 article-title: A bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data publication-title: Autom. Constr. – volume: 2024 start-page: 92 year: 2007 end-page: 99 ident: b0290 article-title: Forecasting of short-term freeway volume with v-support vector machines publication-title: Transp. Res. Rec. – volume: 33 start-page: 37 year: 2013 end-page: 49 ident: b0265 article-title: Urban link travel time estimation using large-scale taxi data with partial information publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 15 start-page: 135 year: 2007 end-page: 153 ident: b0050 article-title: Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 82 start-page: 358 year: 2017 end-page: 378 ident: b0145 article-title: Speed profile estimation using license plate recognition data publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 11 start-page: 187 year: 2003 end-page: 210 ident: b0285 article-title: Short-term travel time prediction publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 46 start-page: 1097 year: 2012 end-page: 1122 ident: b0100 article-title: Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning publication-title: Transport. Res. Part B: Methodol. – volume: 29 start-page: 272 year: 2017 end-page: 285 ident: b0280 article-title: Citywide traffic volume estimation using trajectory data publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 1 year: 2018 end-page: 25 ident: b0005 article-title: Crash severity analysis of rear-end crashes in california using statistical and machine learning classification methods publication-title: J. Transport. Safety Secur. – volume: 8 start-page: 460 year: 2007 end-page: 469 ident: b0170 article-title: Anonymous vehicle reidentification using heterogeneous detection systems publication-title: IEEE Trans. Intell. Transp. Syst. – start-page: 1 year: 1977 end-page: 38 ident: b0060 article-title: Maximum likelihood from incomplete data via the em algorithm publication-title: J. Royal Stat. Soc. Ser. B Methodol. – volume: 18 start-page: 568 year: 2010 end-page: 583 ident: b0090 article-title: Evaluation of traffic data obtained via gps-enabled mobile phones: The mobile century field experiment publication-title: Transport. Res. Part C: Emerg. Technol. – reference: Herring, R., Hofleitner, A., Abbeel, P., Bayen, A., 2010. Estimating arterial traffic conditions using sparse probe data, in: Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, IEEE. pp. 929–936. – start-page: 69 year: 2007 end-page: 80 ident: b0195 article-title: Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections publication-title: Transport. Res. Rec.: J. Transp. Res. Board – volume: 19 start-page: 1006 year: 2011 end-page: 1018 ident: b0135 article-title: Incorporating uncertainty into short-term travel time predictions publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 12 start-page: 29 year: 2012 end-page: 34 ident: b0250 article-title: Brt vehicle travel time prediction based on svm and kalman filter publication-title: J. Transport. Syst. Eng. Inform. Technol. – reference: Wang, Y., Zheng, Y., Xue, Y., 2014. Travel time estimation of a path using sparse trajectories, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 25–34. – volume: 59 start-page: 251 year: 1969 end-page: 260 ident: b0220 article-title: Congestion theory and transport investment publication-title: Am. Econ. Rev. – volume: 2673 start-page: 189 year: 2019 end-page: 201 ident: b0065 article-title: Signal timing parameters estimation for intersections using floating car data publication-title: Transport. Res. Rec. – reference: Wu, X., Srihari, R., 2004. Incorporating prior knowledge with weighted margin support vector machines, in: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 326–333. – volume: 16 start-page: 19 year: 2014 end-page: 28 ident: b0070 article-title: Traffic signal phase and timing estimation from low-frequency transit bus data publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 17 start-page: 586 year: 2009 end-page: 606 ident: b0130 article-title: Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 11 start-page: 639 year: 2010 end-page: 646 ident: b0120 article-title: Freeway corridor performance measurement based on vehicle reidentification publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 17 start-page: 412 year: 2009 end-page: 427 ident: b0140 article-title: Real-time queue length estimation for congested signalized intersections publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 20 start-page: 740 year: 2009 end-page: 752 ident: b0300 article-title: Hero: Online real-time vehicle tracking publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 12 start-page: 64 year: 2008 end-page: 74 ident: b0205 article-title: Real-time monitoring and control on signalized arterials publication-title: J. Intell. Transport. Syst. – volume: 33 start-page: 1024 year: 2001 end-page: 1034 ident: b0150 article-title: The bayes net toolbox for matlab publication-title: Comput. Sci. Stat. – volume: 42 start-page: 325 year: 2008 end-page: 338 ident: b0260 article-title: Travel time estimation on a freeway using discrete time markov chains publication-title: Transport. Res. Part B: Methodol. – year: 2002 ident: b0160 article-title: Dynamic bayesian networks: representation, inference and learning. Ph.D. thesis – volume: 31 start-page: 145 year: 2013 end-page: 157 ident: b0295 article-title: Urban link travel time estimation based on sparse probe vehicle data publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 19 start-page: 1133 year: 2011 end-page: 1156 ident: b0015 article-title: Real time queue length estimation for signalized intersections using travel times from mobile sensors publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 6 start-page: 57311 year: 2018 end-page: 57323 ident: b0215 article-title: Congestion prediction with big data for real-time highway traffic publication-title: IEEE Access – volume: 16 start-page: 18 year: 2008 end-page: 35 ident: b0225 article-title: Real-time estimation of vehicle-count within signalized links publication-title: Transport. Res. Part C: Emerg. Technol. – volume: 25 start-page: 1645 year: 2017 end-page: 1661 ident: b0010 article-title: Signal timing estimation based on low frequency floating car data publication-title: Transport. Res. Proc. – reference: Beijing Traffic Management Bureau, 2017. Location table of fixed traffic monitoring equipment. – year: 2013 ident: b0025 article-title: Statistical decision theory and Bayesian analysis – volume: 40 start-page: 857 year: 2006 end-page: 871 ident: b0200 article-title: A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times publication-title: Transport. Res. Part B: Methodol. – year: 2006 ident: b0035 article-title: Pattern Recognition and Machine Learning – volume: 16 start-page: 2406 year: 2015 end-page: 2417 ident: b0115 article-title: Applying machine learning techniques to transportation mode recognition using mobile phone sensor data publication-title: IEEE Trans. Intell. Transport. Syst. – volume: 8 start-page: 1738 year: 2010 end-page: 1751 ident: b0255 article-title: Travel time measurement in real-time using automatic number plate recognition for Malaysian environment publication-title: J. East. Asia Soc. Transport. Stud. – volume: 371 start-page: 20110550 year: 2013 ident: b0180 article-title: Gaussian processes for time-series modelling publication-title: Phil. Trans. R. Soc. A – volume: 36 start-page: 899 year: 2002 end-page: 917 ident: b0045 article-title: Vehicle reidentification and travel time measurement on congested freeways publication-title: Transport. Res. Part A: Policy Pract. – reference: Hunter, T., Herring, R., Abbeel, P., Bayen, A., 2009. Path and travel time inference from gps probe vehicle data. NIPS Analyzing Networks and Learning with Graphs 12. – volume: 96 start-page: 118 year: 2016 end-page: 129 ident: b0165 article-title: Evaluation of pedestrian safety at intersections: a theoretical framework based on pedestrian-vehicle interaction patterns publication-title: Acc. Anal. Prevent. – reference: . – volume: 57 start-page: 85 year: 2015 end-page: 102 ident: b0270 article-title: Lane-based real-time queue length estimation using license plate recognition data publication-title: Transport. Res. Part C: Emerg. Technol. – start-page: 296 year: 2005 end-page: 301 ident: b0030 article-title: Validating predicted rural corridor travel times from an automated license plate recognition system: Oregon’s frontier project publication-title: IEEE Proc. Intell. Transport. Syst. – reference: Williams, C.K., Rasmussen, C.E., 2006. Gaussian processes for machine learning. the MIT Press 2, 4. – volume: 11 start-page: 305 year: 1999 end-page: 345 ident: b0185 article-title: A unifying review of linear gaussian models publication-title: Neural Comput. – volume: 5 start-page: 276 year: 2004 end-page: 281 ident: b0240 article-title: Travel-time prediction with support vector regression publication-title: IEEE Trans. Intell. Transport. Syst. – reference: Ghahramani, Z., 1998. Learning dynamic bayesian networks, in: Adaptive processing of sequences and data structures. Springer, pp. 168–197. – volume: 11 start-page: 158 year: 1994 ident: b0105 article-title: Inference in belief networks: a procedural guide publication-title: Int. J. Approx. Reason. – volume: 20 start-page: 740 year: 2009 ident: 10.1016/j.trc.2020.102660_b0300 article-title: Hero: Online real-time vehicle tracking publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2008.147 – volume: 36 start-page: 899 year: 2002 ident: 10.1016/j.trc.2020.102660_b0045 article-title: Vehicle reidentification and travel time measurement on congested freeways publication-title: Transport. Res. Part A: Policy Pract. – volume: 19 start-page: 1006 year: 2011 ident: 10.1016/j.trc.2020.102660_b0135 article-title: Incorporating uncertainty into short-term travel time predictions publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2011.05.014 – volume: 33 start-page: 37 year: 2013 ident: 10.1016/j.trc.2020.102660_b0265 article-title: Urban link travel time estimation using large-scale taxi data with partial information publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2013.04.001 – volume: 57 start-page: 85 year: 2015 ident: 10.1016/j.trc.2020.102660_b0270 article-title: Lane-based real-time queue length estimation using license plate recognition data publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2015.06.001 – volume: 25 start-page: 1645 year: 2017 ident: 10.1016/j.trc.2020.102660_b0010 article-title: Signal timing estimation based on low frequency floating car data publication-title: Transport. Res. Proc. doi: 10.1016/j.trpro.2017.05.214 – volume: 16 start-page: 2406 year: 2015 ident: 10.1016/j.trc.2020.102660_b0115 article-title: Applying machine learning techniques to transportation mode recognition using mobile phone sensor data publication-title: IEEE Trans. Intell. Transport. Syst. doi: 10.1109/TITS.2015.2405759 – volume: 19 start-page: 1133 year: 2011 ident: 10.1016/j.trc.2020.102660_b0015 article-title: Real time queue length estimation for signalized intersections using travel times from mobile sensors publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2011.01.002 – volume: 13 start-page: 792 year: 2012 ident: 10.1016/j.trc.2020.102660_b0080 article-title: Signal timing estimation using sample intersection travel times publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2012.2187895 – volume: 33 start-page: 1024 year: 2001 ident: 10.1016/j.trc.2020.102660_b0150 article-title: The bayes net toolbox for matlab publication-title: Comput. Sci. Stat. – volume: 42 start-page: 163 year: 2015 ident: 10.1016/j.trc.2020.102660_b0190 article-title: Use of acceleration data for transportation mode prediction publication-title: Transportation doi: 10.1007/s11116-014-9541-6 – volume: 17 start-page: 586 year: 2009 ident: 10.1016/j.trc.2020.102660_b0130 article-title: Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2009.04.003 – volume: 17 start-page: 412 year: 2009 ident: 10.1016/j.trc.2020.102660_b0140 article-title: Real-time queue length estimation for congested signalized intersections publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2009.02.003 – year: 2013 ident: 10.1016/j.trc.2020.102660_b0025 – ident: 10.1016/j.trc.2020.102660_b0085 – volume: 12 start-page: 29 year: 2012 ident: 10.1016/j.trc.2020.102660_b0250 article-title: Brt vehicle travel time prediction based on svm and kalman filter publication-title: J. Transport. Syst. Eng. Inform. Technol. – ident: 10.1016/j.trc.2020.102660_b0095 doi: 10.1109/ITSC.2010.5624994 – volume: 42 start-page: 325 year: 2008 ident: 10.1016/j.trc.2020.102660_b0260 article-title: Travel time estimation on a freeway using discrete time markov chains publication-title: Transport. Res. Part B: Methodol. doi: 10.1016/j.trb.2007.08.005 – volume: 31 start-page: 145 year: 2013 ident: 10.1016/j.trc.2020.102660_b0295 article-title: Urban link travel time estimation based on sparse probe vehicle data publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2012.04.007 – ident: 10.1016/j.trc.2020.102660_b0110 – ident: 10.1016/j.trc.2020.102660_b0245 doi: 10.1145/1014052.1014089 – year: 2006 ident: 10.1016/j.trc.2020.102660_b0035 – volume: 11 start-page: 639 year: 2010 ident: 10.1016/j.trc.2020.102660_b0120 article-title: Freeway corridor performance measurement based on vehicle reidentification publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2010.2049105 – volume: 40 start-page: 857 year: 2006 ident: 10.1016/j.trc.2020.102660_b0200 article-title: A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times publication-title: Transport. Res. Part B: Methodol. doi: 10.1016/j.trb.2005.11.003 – start-page: 296 year: 2005 ident: 10.1016/j.trc.2020.102660_b0030 article-title: Validating predicted rural corridor travel times from an automated license plate recognition system: Oregon’s frontier project publication-title: IEEE Proc. Intell. Transport. Syst. – volume: 82 start-page: 358 year: 2017 ident: 10.1016/j.trc.2020.102660_b0145 article-title: Speed profile estimation using license plate recognition data publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2017.07.006 – year: 2002 ident: 10.1016/j.trc.2020.102660_b0160 – volume: 15 start-page: 135 year: 2007 ident: 10.1016/j.trc.2020.102660_b0050 article-title: Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2007.03.001 – ident: 10.1016/j.trc.2020.102660_b0230 doi: 10.1145/2623330.2623656 – volume: 2017 start-page: 1738085 year: 2017 ident: 10.1016/j.trc.2020.102660_b0040 article-title: Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data publication-title: J. Adv. Transport. doi: 10.1155/2017/1738085 – start-page: 163 year: 1998 ident: 10.1016/j.trc.2020.102660_b0175 article-title: Forecasting multiple-period freeway link travel times using modular neural networks publication-title: Transport. Res. Rec.: J. Transp. Res. Board doi: 10.3141/1617-23 – start-page: 1 year: 1977 ident: 10.1016/j.trc.2020.102660_b0060 article-title: Maximum likelihood from incomplete data via the em algorithm publication-title: J. Royal Stat. Soc. Ser. B Methodol. – volume: 11 start-page: 305 year: 1999 ident: 10.1016/j.trc.2020.102660_b0185 article-title: A unifying review of linear gaussian models publication-title: Neural Comput. doi: 10.1162/089976699300016674 – year: 1998 ident: 10.1016/j.trc.2020.102660_b0155 – volume: 8 start-page: 1738 year: 2010 ident: 10.1016/j.trc.2020.102660_b0255 article-title: Travel time measurement in real-time using automatic number plate recognition for Malaysian environment publication-title: J. East. Asia Soc. Transport. Stud. – volume: 96 start-page: 118 year: 2016 ident: 10.1016/j.trc.2020.102660_b0165 article-title: Evaluation of pedestrian safety at intersections: a theoretical framework based on pedestrian-vehicle interaction patterns publication-title: Acc. Anal. Prevent. doi: 10.1016/j.aap.2016.07.030 – volume: 371 start-page: 20110550 year: 2013 ident: 10.1016/j.trc.2020.102660_b0180 article-title: Gaussian processes for time-series modelling publication-title: Phil. Trans. R. Soc. A doi: 10.1098/rsta.2011.0550 – volume: 8 start-page: 460 year: 2007 ident: 10.1016/j.trc.2020.102660_b0170 article-title: Anonymous vehicle reidentification using heterogeneous detection systems publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2007.899720 – start-page: 1 year: 2018 ident: 10.1016/j.trc.2020.102660_b0005 article-title: Crash severity analysis of rear-end crashes in california using statistical and machine learning classification methods publication-title: J. Transport. Safety Secur. – volume: 11 start-page: 187 year: 2003 ident: 10.1016/j.trc.2020.102660_b0285 article-title: Short-term travel time prediction publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/S0968-090X(03)00026-3 – ident: 10.1016/j.trc.2020.102660_b0235 doi: 10.7551/mitpress/3206.001.0001 – volume: 12 start-page: 64 year: 2008 ident: 10.1016/j.trc.2020.102660_b0205 article-title: Real-time monitoring and control on signalized arterials publication-title: J. Intell. Transport. Syst. doi: 10.1080/15472450802023337 – volume: 11 start-page: 158 year: 1994 ident: 10.1016/j.trc.2020.102660_b0105 article-title: Inference in belief networks: a procedural guide publication-title: Int. J. Approx. Reason. – volume: 16 start-page: 18 year: 2008 ident: 10.1016/j.trc.2020.102660_b0225 article-title: Real-time estimation of vehicle-count within signalized links publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2007.06.002 – ident: 10.1016/j.trc.2020.102660_b0075 doi: 10.1007/BFb0053999 – volume: 59 start-page: 251 year: 1969 ident: 10.1016/j.trc.2020.102660_b0220 article-title: Congestion theory and transport investment publication-title: Am. Econ. Rev. – volume: 18 start-page: 568 year: 2010 ident: 10.1016/j.trc.2020.102660_b0090 article-title: Evaluation of traffic data obtained via gps-enabled mobile phones: The mobile century field experiment publication-title: Transport. Res. Part C: Emerg. Technol. doi: 10.1016/j.trc.2009.10.006 – volume: 46 start-page: 1097 year: 2012 ident: 10.1016/j.trc.2020.102660_b0100 article-title: Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning publication-title: Transport. Res. Part B: Methodol. doi: 10.1016/j.trb.2012.03.006 – year: 2009 ident: 10.1016/j.trc.2020.102660_b0125 – volume: 2673 start-page: 189 year: 2019 ident: 10.1016/j.trc.2020.102660_b0065 article-title: Signal timing parameters estimation for intersections using floating car data publication-title: Transport. Res. Rec. doi: 10.1177/0361198119844756 – volume: 9 start-page: 293 year: 1999 ident: 10.1016/j.trc.2020.102660_b0210 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. doi: 10.1023/A:1018628609742 – start-page: 69 year: 2007 ident: 10.1016/j.trc.2020.102660_b0195 article-title: Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections publication-title: Transport. Res. Rec.: J. Transp. Res. Board doi: 10.3141/2035-08 – volume: 72 start-page: 237 year: 2016 ident: 10.1016/j.trc.2020.102660_b0275 article-title: A bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data publication-title: Autom. Constr. doi: 10.1016/j.autcon.2015.12.007 – volume: 2024 start-page: 92 year: 2007 ident: 10.1016/j.trc.2020.102660_b0290 article-title: Forecasting of short-term freeway volume with v-support vector machines publication-title: Transp. Res. Rec. doi: 10.3141/2024-11 – volume: 6 start-page: 57311 year: 2018 ident: 10.1016/j.trc.2020.102660_b0215 article-title: Congestion prediction with big data for real-time highway traffic publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2873569 – volume: 29 start-page: 272 year: 2017 ident: 10.1016/j.trc.2020.102660_b0280 article-title: Citywide traffic volume estimation using trajectory data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2016.2621104 – year: 2006 ident: 10.1016/j.trc.2020.102660_b0055 – ident: 10.1016/j.trc.2020.102660_b0020 – volume: 16 start-page: 19 year: 2014 ident: 10.1016/j.trc.2020.102660_b0070 article-title: Traffic signal phase and timing estimation from low-frequency transit bus data publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2014.2323341 – volume: 5 start-page: 276 year: 2004 ident: 10.1016/j.trc.2020.102660_b0240 article-title: Travel-time prediction with support vector regression publication-title: IEEE Trans. Intell. Transport. Syst. doi: 10.1109/TITS.2004.837813 |
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