Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance
Accurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of dis...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Transportation Research Board of the National Academies
2014
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| Online Access: | http://hdl.handle.net/1721.1/89062 |
| _version_ | 1826203168432717824 |
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| author | Vaze, Vikrant Antoniou, Constantinos Wen, Yang Ben-Akiva, Moshe E. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Vaze, Vikrant Antoniou, Constantinos Wen, Yang Ben-Akiva, Moshe E. |
| author_sort | Vaze, Vikrant |
| collection | MIT |
| description | Accurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy. |
| first_indexed | 2024-09-23T12:32:43Z |
| format | Article |
| id | mit-1721.1/89062 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:32:43Z |
| publishDate | 2014 |
| publisher | Transportation Research Board of the National Academies |
| record_format | dspace |
| spelling | mit-1721.1/890622022-10-01T09:40:10Z Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance Vaze, Vikrant Antoniou, Constantinos Wen, Yang Ben-Akiva, Moshe E. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Vaze, Vikrant Wen, Yang Ben-Akiva, Moshe E. Accurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy. 2014-08-26T15:42:37Z 2014-08-26T15:42:37Z 2009-07 Article http://purl.org/eprint/type/JournalArticle 0361-1981 http://hdl.handle.net/1721.1/89062 Vaze, Vikrant, Constantinos Antoniou, Yang Wen, and Moshe Ben-Akiva. “Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance.” Transportation Research Record: Journal of the Transportation Research Board 2090, no. 1 (July 30, 2009): 1–9. en_US http://dx.doi.org/10.3141/2090-01 Transportation Research Record: Journal of the Transportation Research Board Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Transportation Research Board of the National Academies Transportation Research Record |
| spellingShingle | Vaze, Vikrant Antoniou, Constantinos Wen, Yang Ben-Akiva, Moshe E. Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title | Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title_full | Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title_fullStr | Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title_full_unstemmed | Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title_short | Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance |
| title_sort | calibration of dynamic traffic assignment models with point to point traffic surveillance |
| url | http://hdl.handle.net/1721.1/89062 |
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