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Article

Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

by 1,2, 3,* and 4
1
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
2
Department of Infrastructure Development, National Development and Reform Commission, Beijing 100045, China
3
Research Institute for Road Safety of MPS, Beijing 100062, China
4
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaobei Jiang, Haixiang Lin, Fei Yan and Qian Cheng
Sustainability 2021, 13(23), 13057; https://doi.org/10.3390/su132313057
Received: 25 September 2021 / Revised: 14 November 2021 / Accepted: 19 November 2021 / Published: 25 November 2021
Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems. View Full-Text
Keywords: traffic demand estimation; multisource data; sensor deployment; sequential identification; iterative algorithm traffic demand estimation; multisource data; sensor deployment; sequential identification; iterative algorithm
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MDPI and ACS Style

Chen, H.; Chu, Z.; Sun, C. Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data. Sustainability 2021, 13, 13057. https://doi.org/10.3390/su132313057

AMA Style

Chen H, Chu Z, Sun C. Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data. Sustainability. 2021; 13(23):13057. https://doi.org/10.3390/su132313057

Chicago/Turabian Style

Chen, Hui, Zhaoming Chu, and Chao Sun. 2021. "Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data" Sustainability 13, no. 23: 13057. https://doi.org/10.3390/su132313057

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