Abstract
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.
Highlights
This paper proposes an integrated model of detectors layout and maximum likelihood traffic demand estimation
(9), the maximum likelihood traffic demand estimation model is established by using the bi-level programming theory, the upper model uses the maximum likelihood method to solve the traffic demand, and the lower model uses the stochastic user equilibrium (SUE) model to solve the path selection probability and the upper level is as follows: upper level: max [lnL(d)]
The target function values of the three models increase with the information: ∑ ∈ v z, a detector layout model in this paper, and the objective function increase in the number of detectors, which means that more observation information can only considers path coverage information: ∑ ∈ ∑ ∈ f Ψ ) after the detector layout obtain a higher coverage information detector deployment scheme
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. (3) The lack of an analytical relationship between traffic demand and traffic flow from the perspective of traffic network theory and the relationship is helpful to establish the maximum likelihood traffic demand estimation model To address these deficiencies, this paper proposes an integrated model of detectors layout and maximum likelihood traffic demand estimation. This paper proposes an integrated model of detectors layout and maximum likelihood traffic demand estimation The former calculates the optimal number and location of the detector layout by maximizing the road section and path coverage information and uses minimum variance weighted average technology to fuse the detected multisource data to use the maximum likelihood method to estimate the traffic demand. The contributions of this paper mainly contains: (1) proposed an integrated model of detectors layout and maximum likelihood traffic demand estimation. The contributions of this paper mainly contains: (1) proposed an integrated model of detectors layout and maximum likelihood traffic demand estimation. (2) designed the successive detector identification algorithm and iterative algorithm
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