1,720,993 research outputs found
NinjaPark
No full textIl software abilita una gestione efficiente della sosta su strada condividendo informazioni sulla sosta all'interno della comunità di utenti
RailBit
No full textIL PROGRAMMA E' UN SOFTWARE DI SIMULAZIONE DEL TRAFFICO FERROVIARIO, FUNZIONANTE SU PC E MAC IN AMBIENTE OPERATIVO WINDOWS, MAC OS E LINU
Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns
No full textThis paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To this aim, a "traffic-informed" methodology is proposed to restore physical and platoon integrity of trajectories in a finite time-space domain, and it is applied to one NGSIM I80 dataset. However, as the actual trajectories are unknown, it is not possible to verify directly whether the reconstructed trajectories are really "nearer" to the actual unknowns than the original measurements. Therefore, a simulation-based validation framework is proposed, that is also able to verify indirectly the efficacy of the reconstruction methodology. The framework exploits the main feature of NGSIM-like data that is the concurrent view of individual driving behaviors and emerging macroscopic traffic patterns. It allows showing that, at the scale of individual models, the accuracy of trajectories affects the distribution and the correlation structure of lane-changing model parameters (i.e. drivers heterogeneity), while it has very little impact on car-following calibration. At the scale of traffic simulation, when models interact in trace-driven simulation of the I80 scenario (multi-lane heterogeneous traffic), their ability to reproduce the observed macroscopic congested patterns is sensibly higher when model parameters from reconstructed trajectories are applied. These results are mainly due to lane changing, and are also the sought indirect validation of the proposed data reconstruction methodology. © 2015 Elsevier Ltd
Speed or spacing? Cumulative variables, and convolution of model errors and time in traffic flow models validation and calibration
No full textThis paper proves that in traffic flow model calibration and validation the cumulative sum of a variable has to be preferred to the variable itself as a measure of performance. As shown through analytical relationships, model residuals dynamics are preserved if discrep- ancy measures of a model against reality are calculated on a cumulative variable, rather than on the variable itself. Keeping memory of model residuals occurrence times is es- sential in traffic flow modelling where the ability of reproducing the dynamics of a phe- nomenon –as a bottleneck evolution or a vehicle deceleration profile –may count as much as the ability of reproducing its order of magnitude. According to the aforesaid finding, in a car-following models context, calibration on travelled space is more robust than calibra- tion on speed or acceleration. Similarly in case of macroscopic traffic flow models valida- tion and calibration, cumulative flows are to be preferred to flows. Actually, the findings above hold for any dynamic model
Making NGSIM data usable for studies on traffic flow theory. Multistep method for vehicle trajectory reconstruction
No full textDespite the importance of NGSIM data for research on traffic flow theory, these data proved to be massively affected by measurement errors in the vehicle's 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 vehicle's 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
Decision-making processes in transportation planning: a taxonomy and four theoretical models.
No full textAnalysis of Drivers’ Compliance to Speed Limits Enforced with an Automated Section Speed Enforcement System
No full textThis study evaluated the effectiveness of speed limits enforced with an automated section speed enforcement system (ASSES) through the analysis of compliance after the system implementation in the Italian Motorway A3 Naples-Salerno.
The speed limits were selected taking into account the average of the following values: (1) minimum design speed; (2) minimum speed consistent with the available stopping sight distance; and (3) operating speeds before the ASSES implementation. The first two criteria take into account safety whereas the third criterion takes into account the mobility needs. The speed limits selected with these criteria were then refined basing on: maximum difference between the section speed limit and the minimum speed evaluated with criteria 1 and 2, evaluation of critical conditions, and consistency between the speed limits and the highway function.
The system was activated on April 21, 2010 and the driving speeds were monitored in the two periods April 21, 2010 - May 11, 2010 and March 30, 2011 - April 20, 2011. Overall, non compliance to the speed limits was equal to 50%. Non compliance was higher for heavy vehicles (60-70%) than for light vehicles (32-65%). Non compliance of light vehicles was much higher in the sections with the most constrained alignments (64% in sections with speed limit equal to 80 kph vs. 34% in sections with speed limit equal to 100 kph). Higher compliance to the speed limits, according to the authors’ belief, might be achieved by a better strategy of communication and information to the road users and a speed limit management strategy synergic between the highway agency and the Police who actually manages the commitments of fines.JRC.F.8 - Sustainable Transpor
Goodness of fit function in the frequency domain for robust calibration of microscopic traffic flow models
No full textIn the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover both the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly estimated within an optimization framework which tries to maximize the fit between real and simulated measures of the traffic system. This is the case, for example, of the calibration of car-following models’ parameters against vehicle trajectory data. Only recently, it has been proven that the capability of the optimization framework to provide the parameters’ values that allow the car-following model reproducing real trajectories at its best is strictly connected to the setting of the optimization framework itself. This, in particular, entails the necessity to carefully choose an appropriate combination of optimization algorithm and measure of goodness of fit (GOF).
In this study, the authors focus attention on this latter issue. Specifically, it is claimed here that the commonly used GOFs are not able to capture the dynamics of the time-series which calibration is performed against. Therefore, a spectral analysis based approach to evaluate the overall performance of the simulation model in the objective function is proposed.
The new measure of goodness of fit is tested in the calibration of the Intelligent Driver Model against synthetic and real trajectory data. Results with synthetic data, in particular, confirm that such a new optimization setting is always able to find the global optimum of the problem.JRC.F.8 - Sustainable Transpor
Do We Really Need to Calibrate All the Parameters? Variance-Based Sensitivity Analysis to Simplify Microscopic Traffic Flow Models
No full textAutomated 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 carfollowing models, i.e., the leader’s 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 leader’s 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.JRC.F.8 - Sustainable Transpor
Global sensitivity analysis techniques to simplify the calibration of traffic simulation models. Methodology and application to the IDM car-following model
No full textAs models are simplifications of reality, the management of the uncertainty arising along the whole modelling process is a crucial and delicate operation, which primarily affects the credibility of model results. In the field of traffic simulation to tackle this issue it is common practice to include the model uncertainty alongside the uncertainty in the parametric inputs. However, reducing the uncertainty in the modelling process through the indirect estimation of the model parameters is far from being simple.
In this picture a key role can be played by model sensitivity analysis. In the present work, in particular, the role of sensitivity analysis in the management of modelling uncertainties is firstly illustrated. Then, one of the most advanced techniques to perform sensitivity analysis is explained and applied to identify, in the specific context of application, which of the input factors of two car-following models can be fixed without appreciably affecting a specific output of interest. Results confirmed the relevance of sensitivity analysis in driving analysts’ activities for models’ comprehension, calibration and validation, namely, for their appropriate use.JRC.F.8 - Sustainable Transpor
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