Marcello Montanino

Making NGSIM Data Usable for Studies on Traffic Flow Theory: Multistep Method for Vehicle Trajectory Reconstruction

Despite the importance of NGSIM data for research on traffic flow theory, these data proved to be massively affected by measurement errors in the vehicles spatial coordinates, errors that were further amplified in the differentiation process when speed and acceleration values were calculated. If not properly accounted for, these errors would make NGSIM data unusable for any study on traffic flow theory. However, the techniques applied in the literature to correct vehicle trajectory data are not suitable for the scope; these techniques do not treat the cause of the bias appropriately and are limited to smoothing out the effects, which are the high-and medium-frequency disturbances in the data. Therefore, in this study the mechanism that was the root of the NGSIM data errors was illustrated, and the limits of available techniques were shown. Then, clarification that extremely high errors, the outliers, need special treatment to be fixed was provided. A multistep filtering procedure aimed at (a) eliminating outliers giving rise to unphysical values for acceleration by local reconstruction of the vehicle trajectory and (b) cutting off the residual random disturbances from the signal while still preserving the driving dynamics was proposed. Both operations were performed, with the requirement for internal consistency of the trajectory being taken into account. Results related to a single vehicles trajectory from the NGSIM I-80 data set and results from the application to the complete set of trajectories from the same data set are presented. The results necessitated correction of NGSIM data before further processing.

Can Results of Car-Following Model Calibration Based on Trajectory Data Be Trusted?

Calibration of car-following models against trajectory data has been widely applied as the basis for studies ranging from model investigation and benchmarking to parameter correlation analysis. Other theoretical issues, such as inter- and intradriver heterogeneity or multianticipative driving behavior, are also addressed in such studies. However, very few of these studies attempted to analyze and quantify the uncertainty entailed in the calibration process and its impacts on the accuracy and reliability of results. A thorough understanding of the whole calibration problem (against trajectory data), as well as of the mutual effect of the specific problems raised in the field literature, does not yet exist. In this view, a general methodology to assess a calibration procedure was proposed and applied to the calibration of the Gipps’ car-following model. Compact indicators were proposed to evaluate the capability of a calibration setting to find the known global solution regarding the accuracy and the robustness against the variation of the starting conditions of the optimization algorithm. Then a graphical inspection method, based on cobweb plots, was proposed to explore the existence and nature of the local minima found by the algorithms, as well as to give insights into the measures of performance and the goodness-of-fit functions used in the calibration experiments. The methodology was applied to all calibration settings (i.e., combinations of algorithms, measures of performance, and goodness-of-fit functions) used in the field literature so far. The study allowed the highlighting and motivation, for the model under investigation, of the limits of some of these calibration settings. Research directions for the definition of robust settings for the problem of car-following model calibration based on real trajectory data are outlined.

Do We Really Need to Calibrate All the Parameters? Variance-Based Sensitivity Analysis to Simplify Microscopic Traffic Flow Models

Automated calibration of microscopic traffic flow models is all but simple for a number of reasons, including the computational complexity of black-box optimization and the asymmetric importance of parameters in influencing model performances. The main objective of this paper is therefore to provide a robust methodology to simplify car-following models, that is, to reduce the number of parameters (to calibrate) without sensibly affecting the capability of reproducing reality. To this aim, variance-based sensitivity analysis is proposed and formulated in a “factor fixing” setting. Among the novel contributions are a robust design of the Monte Carlo framework that also includes, as an analysis factor, the main nonparametric input of car-following models, i.e., the leaders trajectory, and a set of criteria for “data assimilation” in car-following models. The methodology was applied to the intelligent driver model (IDM) and to all the trajectories in the “reconstructed” Next Generation SIMulation (NGSIM) I80-1 data set. The analysis unveiled that the leaders trajectory is considerably more important than the parameters in affecting the variability of model performances. Sensitivity analysis also returned the importance ranking of the IDM parameters. Basing on this, a simplified model version with three (out of six) parameters is proposed. After calibrations, the full model and the simplified model show comparable performances, in face of a sensibly faster convergence of the simplified version.

Thirty Years of Gipps’ Car-Following Model: Applications, Developments, and New Features

Researchers and practitioners commonly use car-following models for road traffic studies. Although dozens of models have been presented so far, the one proposed by Peter G. Gipps in 1981 is still one of the most extensively used. However, many features of the model are still not well known or neglected in common applications. In this context, the current study summarizes and analyzes the main findings available in the scientific literature for the Gipps’ car-following model and introduces some of its novel features that may improve its capability to reproduce real trajectory data. In particular, the structure of the acceleration component of the model is analytically investigated for what concerns the meaning of some parameters that in common practice are usually kept to some empirically derived fixed values. Possible versions of Gipps’ model are presented, and their performance to reproduce real vehicle trajectories is evaluated and compared. The results achieved show the necessity for these parameters to be calibrated to improve the models predictive capabilities.

Gaussian Process Metamodels for Sensitivity Analysis of Traffic Simulation Models Case Study of AIMSUN Mesoscopic Model

This study adopted a metamodel-based technique for model sensitivity analysis and applied it to the AIMSUN mesoscopic model. The application of sensitivity analysis is crucial for the true comprehension and correct use of the traffic simulation model, although the main obstacle to an extensive use of the most sophisticated techniques is the high number of model runs such techniques usually require. For this reason, the possibility of performing a sensitivity analysis was tested not on a model but on its metamodel approximation. Important issues concerning metamodel estimation were investigated and commented on in the specific application to the AIMSUN model. Among these issues are the importance of selecting a proper sampling strategy based on low-discrepancy random number sequences and the importance of selecting a class of metamodels able to reproduce the inputs–outputs relationship in a robust and reliable way. Sobol sequences and Gaussian process metamodels were recognized as the appropriate choices. The proposed methodology was assessed by comparing the results of the application of variance-based sensitivity analysis techniques with the simulation model and with a metamodel estimated with 512 model runs for a variety of traffic scenarios and model outputs. Results confirmed the power of the proposed methodology and also made a more extensive application of sensitivity analysis techniques available for complex traffic simulation models.

Empirical Analysis of Effects of Automated Section Speed Enforcement System on Traffic Flow at Freeway Bottlenecks

Because speed has been recognized as the most important contributory factor in fatal road crashes, speed management has been widely implemented in several countries to improve road safety. The automated section speed enforcement system is increasingly being used as an effective way to tackle speeding, especially on motorways and in tunnels. However, the effects on traffic flow patterns of such systems remain controversial. An empirical analysis of traffic flow patterns before and after the introduction of such a system along a freeway section with recurrent congestion is presented. Gathered data consisted of point measurements at detectors and average travel speeds of each vehicle crossing the stretch. The main observed features were (a) a strong homogenization of individual speeds and of mean speeds among the lanes, (b) a reduction in the strength of the bottleneck, (c) the emergence of significant oscillations in time of traffic characteristics, and (d) a sensible reduction of travel times during the congestion pattern caused by the bottleneck moving downstream of the section. Empirical evidence suggests that driver compliance with speed limits is the key factor in analysis of such speed management systems and that their concurrent application with dynamic speed limit strategies should be thoroughly evaluated with a particular focus on this measure.

Parameter sampling strategies in traffic microsimulation

The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results revealed that model performances in case of sampling from normal pdfs are extremely poor. In turn, results proved that parameter correlation, as well as the parameter distribution model, entail a big impact on model performances and should be properly take into account in the microsimulation practice.