A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data
In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-depende...
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/4971 |
| _version_ | 1797525209659473920 |
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| author | Hang Yu Senlai Zhu Jie Yang Yuntao Guo Tianpei Tang |
| author_facet | Hang Yu Senlai Zhu Jie Yang Yuntao Guo Tianpei Tang |
| author_sort | Hang Yu |
| collection | DOAJ |
| description | In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy. |
| first_indexed | 2024-03-10T09:09:30Z |
| format | Article |
| id | doaj.art-c5bd85945ec74178bb8e433de3b77272 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:09:30Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c5bd85945ec74178bb8e433de3b772722023-11-22T06:08:29ZengMDPI AGSensors1424-82202021-07-012115497110.3390/s21154971A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of DataHang Yu0Senlai Zhu1Jie Yang2Yuntao Guo3Tianpei Tang4School of Transportation and Civil Engineering, Nantong University, Se Yuan Road #9, Nantong 226019, ChinaSchool of Transportation and Civil Engineering, Nantong University, Se Yuan Road #9, Nantong 226019, ChinaSchool of Transportation and Civil Engineering, Nantong University, Se Yuan Road #9, Nantong 226019, ChinaKey Laboratory of Road and Traffic Engineering, Ministry of Education, Department of Traffic Engineering Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaSchool of Transportation and Civil Engineering, Nantong University, Se Yuan Road #9, Nantong 226019, ChinaIn this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.https://www.mdpi.com/1424-8220/21/15/4971dynamic O–D estimationBayesian statisticsynthesizing datastepwise algorithm |
| spellingShingle | Hang Yu Senlai Zhu Jie Yang Yuntao Guo Tianpei Tang A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data Sensors dynamic O–D estimation Bayesian statistic synthesizing data stepwise algorithm |
| title | A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data |
| title_full | A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data |
| title_fullStr | A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data |
| title_full_unstemmed | A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data |
| title_short | A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data |
| title_sort | bayesian method for dynamic origin destination demand estimation synthesizing multiple sources of data |
| topic | dynamic O–D estimation Bayesian statistic synthesizing data stepwise algorithm |
| url | https://www.mdpi.com/1424-8220/21/15/4971 |
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