Highlights The proposed model brings thus the following practical and scientific contributions. A new approach that allows modeling trip chain and activity-based demand within e online Dynamic OD Estimation framework. Differently from other models in the literature, which have been proposed for the offline DODE, it does not require a heavy simulation in order to be implemented. Moreover, static assignment matrices can be used within the proposed method. The proposed approach increases the observability of the demand since correlations between different OD pairs in time and space is explicitly considered. The proposed approach helps to have results that are more consistent over the 24 h periods.
Abstract Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations.
Incorporating trip chaining within online demand estimation
Transportation Research Part B: Methodological ; 132 ; 171-187
2019-05-20
17 pages
Article (Journal)
Electronic Resource
English
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