Biagio Ciuffo

Integration of Driving and Traffic Simulation: Issues and First Solutions

Driving simulators are very suitable test beds for the evaluation and development of intelligent transportation systems (ITSs). However, the impact of such systems on the behavior of individual drivers can properly be analyzed through driving simulators only if autonomous vehicles in the driving scenario move according to the system under evaluation. This condition means that the simulation of the traffic surrounding the interactive vehicle should already take into account the drivers behavior as affected by the system under analysis. Currently, this “loop” is not properly tackled, because the effects on individuals and traffic are, in general, separately and, often, independently evaluated. The integration of traffic and driving simulations, instead, may provide a more consistent solution to this challenging evaluation problem. It also opens up new scenarios for enhancing the credibility of both traffic modeling and driving simulation and for their combined development. For instance, because drivers directly interact with driver/traffic models in a driving simulation environment, such models may also be tested against nonnormative behavior, and this case seems the only way to test driver/traffic models for safety applications. Based on this idea, this paper describes the integration of a driving simulation engine known as SCANeR and a traffic-flow microsimulation model known as AIMSUN. Methodological and technical issues of such integration are first presented, and future enhancements for higher consistency of the simulation environments are finally envisaged.

Gaseous Emissions from Light-Duty Vehicles: Moving from NEDC to the New WLTP Test Procedure.

The Worldwide Harmonized Light Duty Test Procedure (WLTP), recently issued as GTR15 by UNECE-WP29, is designed to check the pollutant emission compliance of Light Duty Vehicles (LDVs) around the world and to establish the reference vehicle fuel consumption and CO2 performance. In the course of the development of WLTP, the Joint Research Center (JRC) of the European Commission has tested gaseous emissions of twenty-one Euro 4-6 gasoline and diesel vehicles, on both the current European type approval test procedure (NEDC) and the progressive versions of the WLTP. The results, which should be regarded just as an initial and qualitative indication of the trends, demonstrated minimal average differences between CO2 emissions over the NEDC and WLTP. On the other hand, CO2 emissions measured at JRC on the NEDC were on average 9% higher than the respective type approval values, therefore suggesting that for the tested vehicles, CO2 emissions over WLTP were almost 10% higher than the respective NEDC type approval values. That difference is likely to increase with application of the full WLTP test procedure. Measured THC emissions from most vehicles stayed below the legal emission limits and in general were lower under the WLTP compared to NEDC. Moving from NEDC to WLTP did not have much impact on NOx from gasoline vehicles and CO from diesel vehicles. On the contrary, NOx from diesel vehicles and CO from low-powered gasoline vehicles were significantly higher over the more dynamic WLTP and in several cases exceeded the emission limits. Results from this study can be considered indicative of emission patterns of modern technology vehicles and useful to both policy makers and vehicle manufacturers in developing future emission policy/technology strategies.

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.

Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models

Parameter calibration of traffic microsimulation models usually takes the form of a simulation-based optimization problem, that is, an optimization in which every objective function evaluation calls for a simulation. It is recognized that such a problem is computationally intractable. Running time grows exponentially both in the number of parameters and in the digits accuracy. In addition, considerable computing time is required by each objective function evaluation. This means that only heuristic techniques can be applied. Accordingly, results of the application of the OptQuest/Multistart algorithm to the calibration of AIMSUN microsimulation model parameters on a freeway network are presented. Furthermore, it is claimed that the search for an effective solution to the calibration problem cannot be exhausted by the choice of the most efficient optimization algorithm. The use of available information concerning the phenomenon could allow calibration performance to be enhanced, for example, by reducing dimensions of the domain of feasible solutions. It is argued that this goal could be achieved by using information from the stationary counterpart of microscopic traffic-flow models that depict the aggregate variables of traffic flows as a function of drivers’ microscopic parameters. Because they have a closed analytical formulation, they are well suited for faster calibrations. Results show that values of parameters from stationary model-based calibrations are not far from the optimal ones. Thus the integration of the two approaches cannot be excluded but is worth investigating.

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.

“No Free Lunch” Theorems Applied to the Calibration of Traffic Simulation Models

In 1997, Wolpert and Macready derived “No free lunch theorems for optimization.” They basically state that “the expected performance of any pair of optimization algorithms across all possible problems is identical.” This is to say that there is no algorithm that outperforms the others over the entire domain of problems. In other words, the choice of the most appropriate algorithm depends upon the specific problem under investigation, and a certain algorithm, while providing good performance (both in terms of solution quality and convergence speed) on certain problems, may reveal weak on certain others. This apparently straightforward concept is not always acknowledged by optimization practitioners. A typical example, in the field of traffic simulation, concerns the calibration of traffic models. In this paper, a general method for verifying the robustness of a calibration procedure (suitable, in general, for any simulation optimization) is proposed based on a test with synthetic data. The main obstacle to this methodology is the significant computation time required by all the necessary simulations. For this reason, a Kriging approximation of the simulation model is proposed instead. The methodology is tested on a specific case study, where the effect on the optimization problem of different combinations of parameters, optimization algorithms, measures of goodness of fit, and levels of noise in the data is also investigated. Results show the clear dependence between the performance of a calibration procedure and the case study under analysis and ascertain the need for global solutions in simulation optimization with traffic models.

Sustainable Urban Land Use and Transportation Planning: A Cognitive Decision Support System for the Naples Metropolitan Area

ABSTRACT Increasing needs for higher mobility are often met by design and implementation of new infrastructure provisions. The challenging question is whether this choice increases the general political objective of sustainable development. In this context, also the land-use and transportation interfaces have to be envisaged. The article aims to offer a methodological/operational contribution to sustainable mobility policy in the Naples metropolitan area (in the Campania Region1, Italy). Scenario analysis is used to design combined land-use/transportation plans to be assessed from a sustainability perspective. Long-range choice options are evaluated using inter alia a sophisticated multicriteria analysis (i.e., hierarchical Regime method). Sensitivity analyses will test the robustness of policy rank order solutions found by the above multicriteria analysis. 1The case study is part of a test project of the land use/transport laboratory of the Regional Centre of Competence funded by the Campania Region in the Operative Regional Program 2000–2006 (measure 3.13) and coordinated by Prof. Vincenzo Torrieri.

How Parameters of Microscopic Traffic Flow Models Relate to Traffic Dynamics in Simulation: Implications for Model Calibration

Several methodological issues in setting up a calibration process for traffic microsimulation models are still unresolved. The influence of individual parameters of microscopic models on simulated traffic dynamics is also far from clear. To address those issues, the paper sets up a methodology based on the sensitivity analysis of traffic flow models (the one used here is AIMSUN). Sensitivity analysis was performed by means of a series of 30 analyses of variance. These were designed to evaluate the effect of parameters on the variance of the simulated outputs and to draw a general inference about (a) the proper interval for the aggregation of measurements, (b) the proper measure of performance (e.g., traffic counts versus speeds), (c) the proper traffic measurement locations, and (d) the subset of parameters to calibrate. The analysis allowed quantification of the effect of the single parameters on different traffic phases. For example, it was possible to quantify the extent of the influence of the parameter reaction time on simulated outputs in locations in which free flow, rather than congested conditions, occurs. The great differences between parameters in affecting the different traffic phases suggested that parameters are likely to be calibrated independently, that is, using data from different locations. The first evidence of the possibility of breaking the calibration problem into two subproblems is given. This entails great benefits in regard to computational time, given the exponential computational complexity of the calibration problem.

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.