Sensor deployment strategy and expansion inference of mobile phone data for traffic demand estimation

Since the trip data based traffic origin-destination（OD）demand needs to be expanded to the whole travelers′level counts，the sensor deployment strategy and expansion factor inference are studied using mathematical programming theory. The sensor deployment model is presented to determine the optimal quantity and locations of sensors through considering the principle of maximum both link and route fl ow coverage information. Based on the link flows observed from the deployed sensors，the bi-level expansion factor inference model is built. The objective function of upper-level model minimizes the distances between the observed and estimated traffic flows，and the constraints are the relationships between expansion factor，OD Demand and link flow. The stochastic user equilibrium（SUE）is adopted as the lower-level model to derive the OD-link proportions. The sequential identifying sensor location algorithm and iterative algorithm are designed to solve the sensor deployment strategy and expansion factor inference model，respectively. Numerical examples demonstrate that the accuracy of values estimated by integrating sensor deployment strategy and expansion factor inference model can reach to 0.01；the built sensor deployment strategy can also be used to determine the optimal scheme of refitting sensors；and the designed algorithms can make convergence to the equilibrium solutions rapidly. This research has significant promoting effects on developing the theory of mobile phone data based OD demand estimations. © 2023 Editorial Board of Jilin University. All rights reserved.