Spatial and Temporal Analyses for Estimation of Origin-Destination Demands by Time of Day Over Year

Abstract

This paper proposes a two-stage model for the estimation of origin-destination (OD) demands by the time of day over the year with the use of offline traffic data from the real-time travel information system. In the first stage, a travel time recursive function is proposed to use the offline travel speed data for the investigation of the spatial and temporal relationships between time-dependent OD demands and traffic counts. As such, it is not required to carry out the time-consuming dynamic traffic assignment (DTA) process which is frequently used in the conventional time-dependent OD estimation models. Using the results in the first stage together with the available traffic count data, a least-squares method is adopted to formulate the time-dependent OD demand estimation problem as a quadratic programming model in the second stage. A solution algorithm is adapted for solving the proposed model. Then, the proposed method is easy for implementation in practice. Particularly, when the traffic accident occurs in the network, the estimated timedependent OD demands can be helpful for understanding the complex travel behavior (e.g., departure time choice) under uncertainty condition. The numerical examples are presented to illustrate the applications of the proposed model. INDEX TERMS OD demand estimation, dynamic traffic assignment, real-time traffic information, least squares, quadratic programming.

Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs

“…Estimating the probabilistic dynamic OD demand (PDOD) is challenging, and the reasons are three-fold: 1) PDODE problem requires modeling the dynamic traffic networks in the probabilistic space, hence a number of existing models need to be adapted or re-formulated (Shao, Lam, and Tam 2006, Nakayama and Watling 2014, Watling et al 2015, Ma and Qian 2017; 2) estimating the probabilistic OD demand is an under-determined problem, and the problem dimension of PDODE is much higher than that for DDODE (Shao et al 2015, Ma and Qian 2018b, Yang, Fan, and Royset 2019; 3) solving PDODE problem is more computationally intensive than solving the DDODE problem, and hence new approaches need to be developed to improve the efficiency of the solution algorithm (Flötteröd 2017, Ma and Qian 2018a, Shen et al 2019.…”