Willem Himpe

Dynamic Origin–Destination Matrix Estimation on Large-Scale Congested Networks Using a Hierarchical Decomposition Scheme

Despite its ever-increasing computing power, dynamic origin–destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires the calculation of the sensitivity of the link flows to all origin–destination flows, in order to incorporate the effects of congestion spillback. This is, however, computationally infeasible for large-scale networks. To overcome this issue, we propose a hierarchical approach for off-line application that decomposes the dynamic OD estimation procedure in space. The main idea is to perform a more accurate dynamic OD estimation only on subareas where there is congestion spillback. The output of this estimation is then used as input for the OD estimation on the whole network. This hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods. The main advantage is that different OD estimation method can be used for different parts of the network as necessary. This allows applying more advanced and accurate, but more time-consuming, methods only where necessary. The hierarchical approach is tested on a study network and on a real network. In both cases the proposed methodology performs better than traditional OD estimation approaches, indicating its merit.

An implicit solution scheme for the Link Transmission Model

Dynamic network loading (DNL) models are at the core of a wide variety of optimization schemes and network analysis tools. In practice this calls for fast and efficient methods to calculate traffic states for various levels of accuracy and numerous adaptations to the boundary conditions. In this paper, we describe the Implicit Link Transmission Model (I-LTM) a dynamic network loading algorithm that avoids small update steps and is able to calculate adaptations of an existing solution efficiently. Within each update step, an implicit consistency problem between flow propagation and network constraints is formulated, resulting in a fixed point solution with appropriate network delays. In an iterative scheme, this consistency problem is solved using the constraints of a previous iteration. The algorithm is further optimized by limiting calculations to the part of the network that has changed. I-LTM allows for fast sensitivity analyses, optimization algorithms and calibration methods and it avoids numerical instabilities related to large time steps, typically observed in most DNL algorithms. This makes it beneficial in terms of calculation effort and robustness of the result.

A Parsimonious Method for Offline Freeway Travel Time Estimation From Sectional Speed Detectors

Online travel time estimation is receiving increasing attention. However, offline algorithms remain important, as databases of historical travel times are needed for many intelligent transportation systems applications. In this paper a parsimonious method is presented for estimating off-line travel times based on local speed measurements along a route with uninterrupted flow, for example, on motorways. The interpolation between the different detector locations is done by reconstructing trajectories based on traffic flow theoretical principles. The proposed method is efficient, easy to calibrate, and shows reliable results even when detector spacing is relatively high. The method is compared to other existing methods and validated on a 40-km highway.

An Integrated Perspective on Traffic Management and Logistic Optimization

Traffic Management and Logistic Optimization have been extensively studied as two separate classes of problems, for which numerous methodologies, mathematical models and algorithmic solutions were made available in literature. However, little attention has been devoted to the interactions between the variables involved in these problems and the consequences of the decision making processes carried independently by Traffic Managers and Logistic Players. We believe this to be of considerable importance, since partial or incomplete knowledge on one anothers decisions might yield sub-optimality for either or both of them. In this work, we propose an integrated view on both classes of problems, providing mathematical formulations to support the assessment of the impact which the two players may have on each other.