Aurélien Duret

Lane distribution of traffic near merging zones influence of variable speed limits

The congestion at on-ramps of motorways is due to too many vehicles wanting to merge onto the same lane. Ramp metering is usually used as control measure to influence the flows, but a variable speed limit can also have large consequences for the merging process. This paper discusses the change in lane distribution due to a VSL and explicitly considers the influence of an on-ramp. To this end, the lane distribution just upstream of an on-ramp is compared with the lane distribution elsewhere. Just upstream of an on-ramp, a significantly lower fraction of the flow uses the outside (right) compared to a part of the road without any ramps. This holds both for a situation with VSL as without VSL. Besides, VSL increases the use of the outside lane near capacity. This way, VSL influences not only the speed but also the lane distribution, and thereby possibly also the merging ratio. The consequences of this changed lane distribution are site-dependent and should be taken into account when deciding on installing a system of variable speed limits.

Estimating Individual Speed-Spacing Relationship and Assessing Ability of Newell's Car-Following Model to Reproduce Trajectories

Capturing variability within flow is an important task for traffic flow models. The linearity of the congested part of the fundamental diagram induces a linear speed-spacing relationship at an individual level, characterized by two parameters. This study assumes that most intervehicle variability can be accounted for by estimating these two parameters for each vehicle. Two methods are presented to quantify individual linear speed-spacing relationships. The first method is based on data: it estimates the speed-spacing relationship by fitting the experimental speed-spacing scatter plot with a straight line. The second method is based on simulation: it computes the optimum parameters so that the simulated trajectories obtained by Newells car-following algorithm reproduce as closely as possible the experimental vehicles trajectories. Both proposed methods are implemented on the Next Generation Simulation trajectory data set recorded on I-80. The individual parameters for the speed-spacing relationship are quantified, and their distributions are specified. The need to distinguish driver behavior on a lane-by-lane basis is discussed. The results tend to prove that taking into account individual variability between drivers can improve the accuracy of simulated trajectories.

Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion

Abstract: Passing rate measurements of backward-moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane-changing and impact of lane-changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane-changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane-changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane-changing activities do not induce a long-term change in traffic states; traffic streams are perturbed temporarily by lane-changing maneuvers but return to the initial states after relaxations.

Onset of Congestion from Low-Speed Merging Maneuvers Within Free-Flow Traffic Stream: Analytical Solution

Low-speed merging maneuvers performed within a free-flow stream are believed to trigger congestion. These accelerating moving bottlenecks introduce local constraints that can disturb the flow at a local or global scale. Low-speed merging maneuvers are also suspected to cause capacity drop. Using the kinematic wave theory, this paper explores the analytical solution of a simple first-order model when moving boundary conditions are introduced. The paper shows that shock waves initiated by low-speed merging maneuvers are a linear transformation of the moving boundary conditions, no matter the shape of the moving boundary condition. These results are then applied to typical situations to show that the interaction of two moving boundaries can modify the analytical solution of the problem. The results are then extended to multiple merging maneuvers to show that they can interact. Every possible interaction between two identical merging maneuvers is explored to identify the conditions that lead to global congestion. Finally, these results are used to propose an analytical formulation of the capacity drop for multiple merging maneuvers at a single location. It is shown that capacity drop is related to the demands on the minor and major streams and to the speed of the merging vehicle.

Data Assimilation Using a Mesoscopic Lighthill–Whitham–Richards Model and Loop Detector Data: Methodology and Large-Scale Network Application

Traffic managers and operators need decision support systems able to provide online traffic flow monitoring and short-term traffic predictions on large-scale networks. Data assimilation (DA) techniques are used to combine observed data and a traffic model. This paper proposes a comprehensive data assimilation framework, based on a mesoscopic Lighthill–Whitham–Richards model, that has short computational times, is well suited for network discontinuities, provides individual vehicle tracking, and can easily be coupled with any dynamic traffic assignment model. The framework also relies on state variables that require adjustments of the DA framework. The requirements proposed by the paper are concerned with (a) the model numerical scheme, (b) the traffic state transformation operators, and (c) updating of the model. The proposed DA framework is first applied to a simplified network. It validates the ability of the proposed framework to update and propagate traffic states accordingly. The proposed framework is then applied to a real large-scale network. The results demonstrate its ability to monitor and forecast traffic conditions with online capabilities.

Calibration of the Fundamental Diagram Based on Loop and Probe Data

The fundamental diagram is a key component of traffic flow. This diagram describes equilibrium traffic states and their propagation on a traffic network. Knowledge of traffic parameters is of paramount importance to understand traffic properties and characteristics. It is also critical to calibrate the elements of dynamic traffic flow simulation models and reproduce traffic states on road networks. This paper describes the development of a method for estimating fundamental diagram parameters that combines loop data and probe data. Loop data are considered boundary conditions of the problem. The travel times between any points located in the loop can be estimated on the basis of the fundamentals of the kinematic wave theory. The optimal fundamental diagram parameters are computed so that the discrepancy between estimated travel times and the actual travel times determined with probe vehicles is minimal. The method is validated by the use of simulated error-free data. The results demonstrate the accuracy of the method when it is applied to an error-free data set. The method is then implemented with realistic data, that is, data that were aggregated and noised beforehand. The robustness of the method is demonstrated, and the results are encouraging for the development of an algorithm that calibrates fundamental diagram parameters online and automatically.

Halphen Distribution System, Toolbox for Modeling Travel Time Variability: Some Insights from Mesoscopic Simulation

This paper introduces the Halphen system as a toolbox for modeling travel time variability. We present the three distributions of the Halphen family and the Moment-Ratio Diagram (MRD), a graphical tool for selecting the best distribution given a specific data set. Then, we explore the mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation. A comprehensive supply-demand scenario enables the comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.This paper introduces the Halphen system as a toolbox for modeling travel time variability. The three distributions of the Halphen family and the moment–ratio diagram (MRD), a graphical tool for selecting the best distribution given a specific data set, are presented. The mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation was explored. A comprehensive supply–demand scenario enabled a comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.

Halphen Distribution System, Toolbox for Modeling Travel Time Variability

This paper introduces the Halphen system as a toolbox for modeling travel time variability. We present the three distributions of the Halphen family and the Moment-Ratio Diagram (MRD), a graphical tool for selecting the best distribution given a specific data set. Then, we explore the mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation. A comprehensive supply-demand scenario enables the comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.This paper introduces the Halphen system as a toolbox for modeling travel time variability. The three distributions of the Halphen family and the moment–ratio diagram (MRD), a graphical tool for selecting the best distribution given a specific data set, are presented. The mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation was explored. A comprehensive supply–demand scenario enabled a comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.

The Halphen distribution system, a toolbox for modeling travel time variability: some insights based on mesoscopic simulation

This paper introduces the Halphen system as a toolbox for modeling travel time variability. We present the three distributions of the Halphen family and the Moment-Ratio Diagram (MRD), a graphical tool for selecting the best distribution given a specific data set. Then, we explore the mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation. A comprehensive supply-demand scenario enables the comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.This paper introduces the Halphen system as a toolbox for modeling travel time variability. The three distributions of the Halphen family and the moment–ratio diagram (MRD), a graphical tool for selecting the best distribution given a specific data set, are presented. The mapping between the positions of travel time samples on the MRD and traffic conditions based on mesoscopic traffic simulation was explored. A comprehensive supply–demand scenario enabled a comparison between day-to-day and hour-by-hour travel time variability in terms of selection of the optimal distribution. The results highlight the capability of the Halphen system to model a wide range of traffic conditions. This flexible tool has the potential to be integrated into a practice-ready decision support system for modeling travel time variability at the network scale.