Ruben Corthout

New Gradient Approximation Method for Dynamic Origin-Destination Matrix Estimation on Congested Networks

In origin–destination (O-D) estimation methods, the relationship between the link flows and the O-D flows is typically approximated by a linear function described by the assignment matrix that corresponds with the current estimate of the O-D flows. However, this relationship implicitly assumes the link flows to be separable; this assumption leads to biased results in congested networks. The use of a different linear approximation of the relationship between O-D flows and link flows has been suggested to take into account link flows being nonseparable. However, deriving this relationship is cumbersome in terms of computation time. In this paper, the use of marginal computation (MaC) is proposed. MaC is a computationally efficient method that performs a perturbation analysis, with the use of kinematic wave theory principles, to derive this relationship. The use of MaC for dynamic O-D estimation was tested on a study network and on a real network. In both cases the proposed methodology performed better than traditional O-D estimation approaches, and thereby showed its merit.

Marginal Incident Computation: An Efficient Algorithm to Determine Congestion Spillback due to Incidents

In studies on the influence of incidents on travel time, researchers rely on Monte Carlo simulation. Because this procedure is demanding computationally, the research scope is limited. This paper presents a highly efficient method for approximately quantifying congestion spillback due to incidents: marginal incident computation (MIC). MIC superimposes the effect of an incident on a single base simulation run (without incidents) instead of carrying out a complete dynamic network loading with the incident, which would involve many calculations identical to the base simulation (e.g., before or far away from the incident). Whereas the results obtained with MIC vary only slightly from the outcome of a complete dynamic network loading, the gain in computation time is significant: a factor > 1,100 for a case study of the Sioux Falls, South Dakota, benchmark network.

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.

Integrated Predictive Control of Freeway Networks Using the Extended Link Transmission Model

In this paper, the recently developed link transmission model (LTM) is utilized in an online hybrid model-based predictive control (MPC) framework. The model is extended to include the effects of ramp metering and variable speed limits. Next, an integrated freeway traffic control based on the new model is presented in order to minimize the total time spent in the network. The integrated scheme has the capability of controlling large-scale freeway networks in real time as the model is computationally efficient, and it is yet accurate enough for our control purposes. In addition, the extended model is reformulated as a system of linear inequalities with mixed binary and real variables. The reformulated model along with the linearized total travel time objective function establish a mixed-integer linear optimization problem that is more tractable and even faster than the original optimization problem integrated in the MPC scheme. Finally, to investigate the performance of the proposed approaches (nonlinear MPC and the mixed-integer linear counterpart), a freeway network layout based on the Leuven Corridor in Belgium is selected. The extended LTM is calibrated for this network using microsimulation data and then is used for prediction and control of the large network. Microsimulation results show that the proposed methods are able to efficiently improve the total travel time.

Stochastic Dynamic Networks Loading for Travel Time Variability Due to Incidents

This chapter presents a new method that allows fast Monte Carlo simulation of incidents on a road network. The marginal incident computation (MIC) model presented in this chapter applies similar link and node models as the link transmission model (LTM), a multi-commodity dynamic network loading (DNL) model that combines realistic queue propagation and congestion spillback (consistent with first order kinematic wave theory) with high computational efficiency. The MIC algorithm determines the congestion effects caused by an incident in an approximate way, superimposing these effects onto a single base simulation run with the LTM or an other existing DNL model. The base cumulative vehicle number are altered, according to the congesting arising from the incident. Calculations are only carried out for the affected links, not for the entire network. A significant computation advantage is achieved compared to full explicit simulation, where identical traffic flows are recalculated for different Monte Carlo samples. In large networks, the computation can be reduced to less than 0.1 percent of explicit simulation.

Variable speed limit control based on extended link transmission model

In this paper the link transmission model (LTM) is extended to include the effects of variable speed limit (VSL) and consequently to provide VSL control for traffic networks modeled by the LTM. The LTM was recently developed for route assignment, but in this study the LTM was modified to be used for control purposes. This modification achieved a model that provides a balanced trade-off between accuracy and computational complexity, and therefore the model is useful for online model-based traffic control. Nevertheless the extension of the model for ramp metering and speed limit control needed careful attention. Because the LTM lacks explicit velocity equations, the focus was on other potential sources that could imitate the influences of VSL. The delays inside the model were manipulated to achieve the mentioned goal. Moreover, different situations were taken into account that might occur in reality on the basis of changes in VSL and different traffic conditions. Finally, the total extensions were verified with simulation and real data. For that aim to be achieved, the VSL extension integrated in the LTM was verified with simulations for a benchmark case study (to show the performance of the extended LTM clearly). Next, the LTM was calibrated by real data collected from the A12 freeway in the Netherlands. The optimal parameters of the model were identified with a global optimization method. Comparison with real data from a period of time when VSL installed on the freeway was active showed the acceptable performance of the total extended and calibrated LTM.