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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 time-dependent 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.

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