Data-driven traffic sensor location and path flow estimation using Wasserstein metric
This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and pr...
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| Veröffentlicht in: | Applied mathematical modelling Jg. 133; S. 211 - 231 |
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| Sprache: | Englisch |
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01.09.2024
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| ISSN: | 0307-904X |
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| Abstract | This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.
•A traffic sensor location and path flow estimation joint optimization model is formulated.•A data-driven path flow measurement method based on Wasserstein metric is proposed.•A customized greedy algorithm by combining a search strategy is developed.•Numerical experiments are conducted based on the real-word transportation network. |
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| AbstractList | This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.
•A traffic sensor location and path flow estimation joint optimization model is formulated.•A data-driven path flow measurement method based on Wasserstein metric is proposed.•A customized greedy algorithm by combining a search strategy is developed.•Numerical experiments are conducted based on the real-word transportation network. |
| Author | Yang, Lixing Gao, Jiaqi Shen, Mengru Yang, Kai |
| Author_xml | – sequence: 1 givenname: Jiaqi surname: Gao fullname: Gao, Jiaqi email: 22120819@bjtu.edu.cn organization: School of Systems Science, Beijing Jiaotong University, Beijing, 100044, China – sequence: 2 givenname: Kai surname: Yang fullname: Yang, Kai email: kaiyang@bjtu.edu.cn organization: School of Systems Science, Beijing Jiaotong University, Beijing, 100044, China – sequence: 3 givenname: Mengru surname: Shen fullname: Shen, Mengru email: mrshen@bjtu.edu.cn organization: School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China – sequence: 4 givenname: Lixing surname: Yang fullname: Yang, Lixing email: lxyang@bjtu.edu.cn organization: School of Systems Science, Beijing Jiaotong University, Beijing, 100044, China |
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| Keywords | Flow estimation Greedy algorithm Sensor location Transportation planning Wasserstein metric |
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| References | He (br0120) 2013; 51 Sun, Zhong, Ma, Liu (br0260) 2020; 38 Fei, Mahmassani, Eisenman (br0220) 2007; 2039 Park, Haghani (br0300) 2015; 55 Shao, Xie, Sun (br0210) 2021; 133 Comert, Cetin (br0180) 2021; 99 Esfahani, Kuhn (br0340) 2018; 171 Jarray (br0350) 2013; 37 Hadavi, Shafahi (br0140) 2016; 89 Zhu, Fu, Zhang, Ma (br0270) 2022; 300 Fu, Lam, Shao, Ma, Chen, Ho (br0360) 2022; 166 Wang, Zhong, Xu (br0250) 2022; 39 Fei, Mahmassani (br0050) 2011; 19 Ye, Wen (br0070) 2017; 18 Castillo, Menéndez, Jiménez (br0060) 2008; 42 Rinaldi, Viti (br0190) 2017; 105 Shao, Lam, Sumalee, Chen, Hazelton (br0290) 2014; 68 Liu, Zhu, Ma, Jia (br0200) 2015; 9 Cascetta, Nguyen (br0280) 1988; 22 Herrera-Quintero, Vega-Alfonso, Banse, Carrillo Zambrano (br0160) 2018; 10 Zhu, Fu, Ma (br0020) 2018; 113 Zhan, Wan, Cheng, Ran (br0080) 2018; 10 Hu, Peeta, Liou (br0150) 2016; 17 Gentili, Cerulli, Cerrone (br0130) 2015; 247 He, Sun (br0110) 2016; 16 Zhang, Pourazarm, Cassandras, Paschalidis (br0380) 2016 Álvarez Bazo, Cerulli, Sánchez-Cambronero, Gentili, Rivas (br0240) 2022; 138 Hu, Peeta, Chu (br0100) 2009; 43 Zhang, Pourazarm, Cassandras, Paschalidis (br0390) 2018; 106 Yao, Hu, Lu, Gao, Zhang (br0040) 2014; 43 Gentili, Mirchandani (br0090) 2005; 136 Owais (br0010) 2022; 208 Hanasusanto, Kuhn (br0330) 2018; 66 Ma, Li, Wen (br0370) 2007; 36 Thike, Lupin, Vagapov (br0400) 2016 Fu, Lam, Shao, Kattan, Salari (br0320) 2022; 157 Barcelo, Gillieron, Linares, Serch, Montero (br0170) 2012; 2308 Fu, Zhu, Ma (br0230) 2017; 102 Simonelli, Marzano, Papola, Vitiello (br0310) 2012; 46 Yang, Zhou (br0030) 1998; 32 Yao (10.1016/j.apm.2024.05.021_br0040) 2014; 43 Hu (10.1016/j.apm.2024.05.021_br0150) 2016; 17 Fu (10.1016/j.apm.2024.05.021_br0360) 2022; 166 Fu (10.1016/j.apm.2024.05.021_br0230) 2017; 102 He (10.1016/j.apm.2024.05.021_br0110) 2016; 16 Cascetta (10.1016/j.apm.2024.05.021_br0280) 1988; 22 Zhang (10.1016/j.apm.2024.05.021_br0390) 2018; 106 Park (10.1016/j.apm.2024.05.021_br0300) 2015; 55 Gentili (10.1016/j.apm.2024.05.021_br0130) 2015; 247 Álvarez Bazo (10.1016/j.apm.2024.05.021_br0240) 2022; 138 Wang (10.1016/j.apm.2024.05.021_br0250) 2022; 39 Yang (10.1016/j.apm.2024.05.021_br0030) 1998; 32 Barcelo (10.1016/j.apm.2024.05.021_br0170) 2012; 2308 Hadavi (10.1016/j.apm.2024.05.021_br0140) 2016; 89 Zhu (10.1016/j.apm.2024.05.021_br0020) 2018; 113 Fei (10.1016/j.apm.2024.05.021_br0050) 2011; 19 Gentili (10.1016/j.apm.2024.05.021_br0090) 2005; 136 Hu (10.1016/j.apm.2024.05.021_br0100) 2009; 43 Shao (10.1016/j.apm.2024.05.021_br0290) 2014; 68 Comert (10.1016/j.apm.2024.05.021_br0180) 2021; 99 Fu (10.1016/j.apm.2024.05.021_br0320) 2022; 157 Ye (10.1016/j.apm.2024.05.021_br0070) 2017; 18 Ma (10.1016/j.apm.2024.05.021_br0370) 2007; 36 Liu (10.1016/j.apm.2024.05.021_br0200) 2015; 9 Sun (10.1016/j.apm.2024.05.021_br0260) 2020; 38 Fei (10.1016/j.apm.2024.05.021_br0220) 2007; 2039 Esfahani (10.1016/j.apm.2024.05.021_br0340) 2018; 171 Zhang (10.1016/j.apm.2024.05.021_br0380) 2016 Zhan (10.1016/j.apm.2024.05.021_br0080) 2018; 10 Castillo (10.1016/j.apm.2024.05.021_br0060) 2008; 42 Rinaldi (10.1016/j.apm.2024.05.021_br0190) 2017; 105 Zhu (10.1016/j.apm.2024.05.021_br0270) 2022; 300 Hanasusanto (10.1016/j.apm.2024.05.021_br0330) 2018; 66 Thike (10.1016/j.apm.2024.05.021_br0400) 2016 Simonelli (10.1016/j.apm.2024.05.021_br0310) 2012; 46 Owais (10.1016/j.apm.2024.05.021_br0010) 2022; 208 He (10.1016/j.apm.2024.05.021_br0120) 2013; 51 Herrera-Quintero (10.1016/j.apm.2024.05.021_br0160) 2018; 10 Shao (10.1016/j.apm.2024.05.021_br0210) 2021; 133 Jarray (10.1016/j.apm.2024.05.021_br0350) 2013; 37 |
| References_xml | – volume: 247 start-page: 618 year: 2015 end-page: 629 ident: br0130 article-title: Vehicle-id sensor location for route flow recognition: models and algorithms publication-title: Eur. J. Oper. Res. – volume: 89 start-page: 82 year: 2016 end-page: 106 ident: br0140 article-title: Vehicle identification sensor models for origin–destination estimation publication-title: Transp. Res., Part B, Methodol. – volume: 17 start-page: 195 year: 2016 end-page: 205 ident: br0150 article-title: Integrated determination of network origin–destination trip matrix and heterogeneous sensor selection and location strategy publication-title: J. Intell. Transp. Syst. – volume: 138 year: 2022 ident: br0240 article-title: An iterative multiparametric approach for determining the location of avi sensors for robust route flow estimation publication-title: Comput. Oper. Res. – volume: 166 start-page: 19 year: 2022 end-page: 47 ident: br0360 article-title: Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects publication-title: Transp. Res., Part B, Methodol. – volume: 37 start-page: 6780 year: 2013 end-page: 6785 ident: br0350 article-title: A lagrangean-based heuristics for the target covering problem in wireless sensor network publication-title: Appl. Math. Model. – volume: 22 start-page: 437 year: 1988 end-page: 455 ident: br0280 article-title: A unified framework for estimating or updating origin/destination matrices from traffic counts publication-title: Transp. Res., Part B, Methodol. – volume: 10 start-page: 17 year: 2018 end-page: 27 ident: br0160 article-title: Smart its sensor for the transportation planning based on iot approaches using serverless and microservices architecture publication-title: IEEE Intell. Transp. Syst. Mag. – volume: 66 start-page: 849 year: 2018 end-page: 869 ident: br0330 article-title: Conic programming reformulations of two-stage distributionally robust linear programs over Wasserstein balls publication-title: Oper. Res. – volume: 106 start-page: 538 year: 2018 end-page: 553 ident: br0390 article-title: The price of anarchy in transportation networks: data-driven evaluation and reduction strategies publication-title: Proc. IEEE – volume: 10 start-page: 134 year: 2018 end-page: 149 ident: br0080 article-title: Methods for multi-type sensor allocations along a freeway corridor publication-title: IEEE Intell. Transp. Syst. Mag. – volume: 39 start-page: 158 year: 2022 end-page: 164 ident: br0250 article-title: Layout of expressway traffic detectors considering failure factors publication-title: J. Highw. Transp. Res. Dev. – volume: 113 start-page: 91 year: 2018 end-page: 120 ident: br0020 article-title: Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model publication-title: Transp. Res., Part B, Methodol. – volume: 43 start-page: 873 year: 2009 end-page: 894 ident: br0100 article-title: Identification of vehicle sensor locations for link-based network traffic applications publication-title: Transp. Res., Part B, Methodol. – volume: 136 start-page: 229 year: 2005 end-page: 257 ident: br0090 article-title: Locating active sensors on traffic networks publication-title: Ann. Oper. Res. – volume: 99 start-page: 418 year: 2021 end-page: 434 ident: br0180 article-title: Queue length estimation from connected vehicles with range measurement sensors at traffic signals publication-title: Appl. Math. Model. – volume: 19 start-page: 440 year: 2011 end-page: 453 ident: br0050 article-title: Structural analysis of near-optimal sensor locations for a stochastic large-scale network publication-title: Transp. Res., Part C, Emerg. Technol. – volume: 2039 start-page: 1 year: 2007 end-page: 15 ident: br0220 article-title: Sensor coverage and location for real-time traffic prediction in large-scale networks publication-title: Transp. Res. Rec. – volume: 42 start-page: 455 year: 2008 end-page: 481 ident: br0060 article-title: Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations publication-title: Transp. Res., Part B, Methodol. – volume: 171 start-page: 115 year: 2018 end-page: 166 ident: br0340 article-title: Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations publication-title: Math. Program. – volume: 55 start-page: 203 year: 2015 end-page: 216 ident: br0300 article-title: Optimal number and location of bluetooth sensors considering stochastic travel time prediction publication-title: Transp. Res., Part C, Emerg. Technol. – start-page: 789 year: 2016 end-page: 794 ident: br0380 article-title: The price of anarchy in transportation networks by estimating user cost functions from actual traffic data publication-title: 2016 IEEE 55th Conference on Decision and Control – volume: 43 start-page: 233 year: 2014 end-page: 248 ident: br0040 article-title: Transit network design based on travel time reliability publication-title: Transp. Res., Part C, Emerg. Technol. – volume: 208 year: 2022 ident: br0010 article-title: Traffic sensor location problem: three decades of research publication-title: Expert Syst. Appl. – volume: 2308 start-page: 17 year: 2012 end-page: 26 ident: br0170 article-title: Exploring link covering and node covering formulations of detection layout problem publication-title: Transp. Res. Rec. – volume: 102 start-page: 210 year: 2017 end-page: 237 ident: br0230 article-title: A stochastic program approach for path reconstruction oriented sensor location model publication-title: Transp. Res., Part B, Methodol. – volume: 157 year: 2022 ident: br0320 article-title: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects publication-title: Transp. Res., Part E, Logist. Transp. Rev. – volume: 68 start-page: 52 year: 2014 end-page: 75 ident: br0290 article-title: Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts publication-title: Transp. Res., Part B, Methodol. – volume: 36 start-page: 235 year: 2007 end-page: 239 ident: br0370 article-title: Research on location of traffic observation points for origin-destination matrix estimation publication-title: Inf. Control – volume: 16 start-page: 6 year: 2016 ident: br0110 article-title: An algebraic and graphic combination approach to solve network sensor location problems publication-title: J. Transp. Syst. Eng. Inf. Technol. – start-page: 264 year: 2016 end-page: 268 ident: br0400 article-title: Implementation of brute force algorithm for topology optimisation of wireless networks publication-title: 2016 International Conference for Students on Applied Engineering (ICSAE) – volume: 46 start-page: 1624 year: 2012 end-page: 1638 ident: br0310 article-title: A network sensor location procedure accounting for o–d matrix estimate variability publication-title: Transp. Res., Part B, Methodol. – volume: 9 start-page: 184 year: 2015 end-page: 192 ident: br0200 article-title: Traffic sensor location approach for flow inference publication-title: IET Intell. Transp. Syst. – volume: 105 start-page: 86 year: 2017 end-page: 119 ident: br0190 article-title: Exact and approximate route set generation for resilient partial observability in sensor location problems publication-title: Transp. Res., Part B, Methodol. – volume: 300 start-page: 428 year: 2022 end-page: 448 ident: br0270 article-title: A network sensor location problem for link flow observability and estimation publication-title: Eur. J. Oper. Res. – volume: 51 start-page: 65 year: 2013 end-page: 76 ident: br0120 article-title: A graphical approach to identify sensor locations for link flow inference publication-title: Transp. Res., Part B, Methodol. – volume: 38 start-page: 76 year: 2020 end-page: 83 ident: br0260 article-title: A study on improved imputation methods for traffic flow data based on spatial topology of road network publication-title: J. Transp. Saf. Secur. – volume: 32 start-page: 524 year: 1998 end-page: 534 ident: br0030 article-title: Optimal traffic counting locations for origin-destination matrix estimation publication-title: Transp. Res., Part B, Methodol. – volume: 18 start-page: 1857 year: 2017 end-page: 1866 ident: br0070 article-title: Optimal traffic sensor location for origin–destination estimation using a compressed sensing framework publication-title: J. Intell. Transp. Syst. – volume: 133 year: 2021 ident: br0210 article-title: Optimization of network sensor location for full link flow observability considering sensor measurement error publication-title: Transp. Res., Part C, Emerg. Technol. – volume: 102 start-page: 210 year: 2017 ident: 10.1016/j.apm.2024.05.021_br0230 article-title: A stochastic program approach for path reconstruction oriented sensor location model publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2017.05.013 – volume: 19 start-page: 440 issue: 3 year: 2011 ident: 10.1016/j.apm.2024.05.021_br0050 article-title: Structural analysis of near-optimal sensor locations for a stochastic large-scale network publication-title: Transp. Res., Part C, Emerg. Technol. doi: 10.1016/j.trc.2010.07.001 – volume: 171 start-page: 115 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0340 article-title: Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations publication-title: Math. Program. doi: 10.1007/s10107-017-1172-1 – volume: 46 start-page: 1624 issue: 10 year: 2012 ident: 10.1016/j.apm.2024.05.021_br0310 article-title: A network sensor location procedure accounting for o–d matrix estimate variability publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2012.08.007 – volume: 138 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0240 article-title: An iterative multiparametric approach for determining the location of avi sensors for robust route flow estimation publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2021.105596 – volume: 133 year: 2021 ident: 10.1016/j.apm.2024.05.021_br0210 article-title: Optimization of network sensor location for full link flow observability considering sensor measurement error publication-title: Transp. Res., Part C, Emerg. Technol. doi: 10.1016/j.trc.2021.103460 – volume: 106 start-page: 538 issue: 4 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0390 article-title: The price of anarchy in transportation networks: data-driven evaluation and reduction strategies publication-title: Proc. IEEE doi: 10.1109/JPROC.2018.2790405 – volume: 22 start-page: 437 issue: 6 year: 1988 ident: 10.1016/j.apm.2024.05.021_br0280 article-title: A unified framework for estimating or updating origin/destination matrices from traffic counts publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/0191-2615(88)90024-0 – volume: 37 start-page: 6780 issue: 10 year: 2013 ident: 10.1016/j.apm.2024.05.021_br0350 article-title: A lagrangean-based heuristics for the target covering problem in wireless sensor network publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2013.02.006 – volume: 55 start-page: 203 year: 2015 ident: 10.1016/j.apm.2024.05.021_br0300 article-title: Optimal number and location of bluetooth sensors considering stochastic travel time prediction publication-title: Transp. Res., Part C, Emerg. Technol. doi: 10.1016/j.trc.2015.03.023 – volume: 42 start-page: 455 issue: 5 year: 2008 ident: 10.1016/j.apm.2024.05.021_br0060 article-title: Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2007.09.004 – volume: 32 start-page: 524 issue: 2 year: 1998 ident: 10.1016/j.apm.2024.05.021_br0030 article-title: Optimal traffic counting locations for origin-destination matrix estimation publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/S0191-2615(97)00016-7 – volume: 166 start-page: 19 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0360 article-title: Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2022.10.006 – volume: 38 start-page: 76 year: 2020 ident: 10.1016/j.apm.2024.05.021_br0260 article-title: A study on improved imputation methods for traffic flow data based on spatial topology of road network publication-title: J. Transp. Saf. Secur. – volume: 89 start-page: 82 year: 2016 ident: 10.1016/j.apm.2024.05.021_br0140 article-title: Vehicle identification sensor models for origin–destination estimation publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2016.03.011 – volume: 17 start-page: 195 issue: 1 year: 2016 ident: 10.1016/j.apm.2024.05.021_br0150 article-title: Integrated determination of network origin–destination trip matrix and heterogeneous sensor selection and location strategy publication-title: J. Intell. Transp. Syst. – volume: 208 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0010 article-title: Traffic sensor location problem: three decades of research publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.118134 – volume: 68 start-page: 52 year: 2014 ident: 10.1016/j.apm.2024.05.021_br0290 article-title: Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2014.06.002 – volume: 66 start-page: 849 issue: 3 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0330 article-title: Conic programming reformulations of two-stage distributionally robust linear programs over Wasserstein balls publication-title: Oper. Res. doi: 10.1287/opre.2017.1698 – volume: 10 start-page: 17 issue: 2 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0160 article-title: Smart its sensor for the transportation planning based on iot approaches using serverless and microservices architecture publication-title: IEEE Intell. Transp. Syst. Mag. doi: 10.1109/MITS.2018.2806620 – volume: 36 start-page: 235 issue: 2 year: 2007 ident: 10.1016/j.apm.2024.05.021_br0370 article-title: Research on location of traffic observation points for origin-destination matrix estimation publication-title: Inf. Control – volume: 2308 start-page: 17 year: 2012 ident: 10.1016/j.apm.2024.05.021_br0170 article-title: Exploring link covering and node covering formulations of detection layout problem publication-title: Transp. Res. Rec. doi: 10.3141/2308-03 – volume: 10 start-page: 134 issue: 2 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0080 article-title: Methods for multi-type sensor allocations along a freeway corridor publication-title: IEEE Intell. Transp. Syst. Mag. doi: 10.1109/MITS.2018.2806639 – volume: 157 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0320 article-title: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects publication-title: Transp. Res., Part E, Logist. Transp. Rev. doi: 10.1016/j.tre.2021.102555 – volume: 18 start-page: 1857 issue: 7 year: 2017 ident: 10.1016/j.apm.2024.05.021_br0070 article-title: Optimal traffic sensor location for origin–destination estimation using a compressed sensing framework publication-title: J. Intell. Transp. Syst. – volume: 247 start-page: 618 issue: 2 year: 2015 ident: 10.1016/j.apm.2024.05.021_br0130 article-title: Vehicle-id sensor location for route flow recognition: models and algorithms publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2015.05.070 – volume: 99 start-page: 418 year: 2021 ident: 10.1016/j.apm.2024.05.021_br0180 article-title: Queue length estimation from connected vehicles with range measurement sensors at traffic signals publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2021.07.003 – volume: 113 start-page: 91 year: 2018 ident: 10.1016/j.apm.2024.05.021_br0020 article-title: Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2018.05.009 – volume: 51 start-page: 65 year: 2013 ident: 10.1016/j.apm.2024.05.021_br0120 article-title: A graphical approach to identify sensor locations for link flow inference publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2013.02.006 – volume: 105 start-page: 86 year: 2017 ident: 10.1016/j.apm.2024.05.021_br0190 article-title: Exact and approximate route set generation for resilient partial observability in sensor location problems publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2017.08.007 – volume: 43 start-page: 233 year: 2014 ident: 10.1016/j.apm.2024.05.021_br0040 article-title: Transit network design based on travel time reliability publication-title: Transp. Res., Part C, Emerg. Technol. doi: 10.1016/j.trc.2013.12.005 – volume: 2039 start-page: 1 issue: 1 year: 2007 ident: 10.1016/j.apm.2024.05.021_br0220 article-title: Sensor coverage and location for real-time traffic prediction in large-scale networks publication-title: Transp. Res. Rec. doi: 10.3141/2039-01 – volume: 136 start-page: 229 year: 2005 ident: 10.1016/j.apm.2024.05.021_br0090 article-title: Locating active sensors on traffic networks publication-title: Ann. Oper. Res. doi: 10.1007/s10479-005-2047-z – volume: 43 start-page: 873 issue: 8 year: 2009 ident: 10.1016/j.apm.2024.05.021_br0100 article-title: Identification of vehicle sensor locations for link-based network traffic applications publication-title: Transp. Res., Part B, Methodol. doi: 10.1016/j.trb.2009.02.008 – volume: 9 start-page: 184 issue: 2 year: 2015 ident: 10.1016/j.apm.2024.05.021_br0200 article-title: Traffic sensor location approach for flow inference publication-title: IET Intell. Transp. Syst. doi: 10.1049/iet-its.2014.0023 – volume: 39 start-page: 158 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0250 article-title: Layout of expressway traffic detectors considering failure factors publication-title: J. Highw. Transp. Res. Dev. – volume: 16 start-page: 6 issue: 5 year: 2016 ident: 10.1016/j.apm.2024.05.021_br0110 article-title: An algebraic and graphic combination approach to solve network sensor location problems publication-title: J. Transp. Syst. Eng. Inf. Technol. – start-page: 789 year: 2016 ident: 10.1016/j.apm.2024.05.021_br0380 article-title: The price of anarchy in transportation networks by estimating user cost functions from actual traffic data – volume: 300 start-page: 428 issue: 2 year: 2022 ident: 10.1016/j.apm.2024.05.021_br0270 article-title: A network sensor location problem for link flow observability and estimation publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2021.10.038 – start-page: 264 year: 2016 ident: 10.1016/j.apm.2024.05.021_br0400 article-title: Implementation of brute force algorithm for topology optimisation of wireless networks |
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| SubjectTerms | Flow estimation Greedy algorithm Sensor location Transportation planning Wasserstein metric |
| Title | Data-driven traffic sensor location and path flow estimation using Wasserstein metric |
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