scholarly journals An Automatic Calibration Procedure of Driving Behaviour Parameters in the Presence of High Bus Volume

2019 ◽  
Vol 31 (5) ◽  
pp. 491-502 ◽  
Author(s):  
Nima Dadashzadeh ◽  
Murat Ergun ◽  
Sercan Kesten ◽  
Marijan Žura
Keyword(s):  
Calibration Procedure ◽  
Automatic Calibration ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Change Models ◽  
Calibration Process ◽  
Car Following ◽  
Driving Behaviour ◽  
Optimization Stage ◽  
Behaviour Models

Most of the microscopic traffic simulation programs used today incorporate car-following and lane-change models to simulate driving behaviour across a given area. The main goal of this study has been to develop an automatic calibration process for the parameters of driving behaviour models using metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a combination of GA and PSO (i.e. hybrid GAPSO and hybrid PSOGA) were used during the optimization stage. In order to verify our proposed methodology, a suitable study area with high bus volume on-ramp from the O-1 Highway in Istanbul has been modelled in VISSIM. Traffic data have been gathered through detectors. The calibration procedure has been coded using MATLAB and implemented via the VISSIM-MATLAB COM interface. Using the proposed methodology, the results of the calibrated model showed that hybrid GAPSO and hybrid PSOGA techniques outperformed the GA-only and PSO-only techniques during the calibration process. Thus, both are recommended for use in the calibration of microsimulation traffic models, rather than GA-only and PSO-only techniques.

Applying Situated Agents to Microscopic Traffic Modelling

2009 ◽  
pp. 108-123 ◽  
Author(s):  
Paulo A.F. Ferreira ◽  
Edgar F. Esteves ◽  
Rosaldo J.F. Rossetti ◽  
Eugénio C. Oliveira
Keyword(s):  
Cognitive Abilities ◽  
Communication Model ◽  
Microscopic Traffic Simulation ◽  
Change Models ◽  
Agent Model ◽  
Car Following ◽  
Microscopic Models ◽  
Realistic Representation ◽  
Multi Agent

Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors bring this discussion forward and propose a multi-agent model of the traffic domain where integration is ascribed to the way the environment is represented and agents interoperate. While most approaches still deal with drivers and vehicles indistinguishably, in the proposed framework vehicles are merely moveable objects whereas the driving role is played by agents fully endowed with cognitive abilities and situated in the environment. The authors discuss on the role of the environment dynamics in supporting a truly emergent behaviour of the system and present an extension to the traditional car-following and lane-change models based on the concept of situated agents. A physical communication model is proposed to base different interactions and some performance issues are also identified, which allows for more realistic representation of drivers’ behaviour in microscopic models.

scholarly journals Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jinsoo Kim ◽  
Jae Hun Kim ◽  
Gunwoo Lee ◽  
Hyun-ju Shin ◽  
Jahng Hyon Park
Keyword(s):  
Vehicle Emissions ◽  
Traffic Simulation ◽  
Calibration Procedure ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Micro Scale ◽  
Reliable Estimation ◽  
Calibration Measurement ◽  
Emission Models ◽  
Calibrated Simulation

Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these models, but it is not guaranteed that the use of widely used calibration measures can lead more accurate emissions estimates. Accordingly, we propose a systematic guideline for calibration to ensure reliable micro-scale emissions estimates. A calibration procedure is thus established in this paper based on various measure of effect (MOE) compositions (i.e., calibration levels) consisting of aggregated traffic data to identify the level that most reliably estimates micro-scale emissions. Five calibration levels of progressively more detailed measurements are first defined, valid calibration levels are identified, and the reliable calibration level is finally selected based on the available traffic data. The effect of vehicle type (i.e., light vs. heavy vehicles) composition on the estimated emissions is also evaluated for a well-calibrated simulation. We expect that a highly reliable estimation of emissions is possible using this more detailed traffic simulation calibration measurement.

Development of Multiregime Speed–Density Relationships by Cluster Analysis

2005 ◽  
Vol 1934 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Lu Sun ◽  
Jie Zhou
Keyword(s):  
Cluster Analysis ◽  
Traffic Flow ◽  
Ad Hoc ◽  
Data Sets ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Data Segmentation ◽  
Essential Components ◽  
Real Traffic

Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.

scholarly journals Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data

2010 ◽  
Vol 7 (6) ◽  
pp. 9173-9218 ◽  
Author(s):  
N. V. Dung ◽  
B. Merz ◽  
A. Bárdossy ◽  
T. D. Thang ◽  
H. Apel
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Mekong Delta ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrodynamic Models ◽  
Calibration Process ◽  
Multi Objective ◽  
Inundation Maps ◽  
In Situ Data

Abstract. Calibration of hydrodynamic models is – compared to other disciplines like e.g. hydrology – still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on flood extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-D hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi-objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been difficult to detect by a standard manual calibration procedure.

scholarly journals Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data

2011 ◽  
Vol 15 (4) ◽  
pp. 1339-1354 ◽  
Author(s):  
N. V. Dung ◽  
B. Merz ◽  
A. Bárdossy ◽  
T. D. Thang ◽  
H. Apel
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Mekong Delta ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrodynamic Models ◽  
Calibration Process ◽  
Multi Objective ◽  
Inundation Maps ◽  
In Situ Data

Abstract. Automatic and multi-objective calibration of hydrodynamic models is – compared to other disciplines like e.g. hydrology – still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on flood extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-dimensional hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi-objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been difficult to detect by a standard manual calibration procedure.

scholarly journals Human-like driving behaviour emerges from a risk-based driver model

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarvesh Kolekar ◽  
Joost de Winter ◽  
David Abbink
Keyword(s):  
Obstacle Avoidance ◽  
Perceived Risk ◽  
Threshold Level ◽  
Driver Model ◽  
Car Following ◽  
Human In The Loop ◽  
Driving Behaviour ◽  
Underlying Principle ◽  
Model Predictions ◽  
Behaviour Models

Abstract Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Optimization of the Robot Calibration Process

1992 ◽  
Author(s):  
G. Zak ◽  
R. G. Fenton ◽  
B. Benhabib
Keyword(s):  
Optimization Problem ◽  
Kinematic Model ◽  
Optimization Procedure ◽  
Calibration Procedure ◽  
Residual Error ◽  
Industrial Robots ◽  
Multiple Objective Optimization ◽  
Robot Calibration ◽  
Calibration Process ◽  
The Cost

Abstract Most industrial robots cannot be off-line programmed to carry out a task accurately, unless their kinematic model is suitably corrected through a calibration procedure. However, proper calibration is an expensive and time-consuming procedure due to the highly accurate measurement equipment required and due to the significant amount of data that must be collected. To improve the efficiency of robot calibration, an optimization procedure is proposed in this paper. The objective of minimizing the cost of the calibration is combined with the objective of minimizing the residual error after calibration in one multiple-objective optimization. Prediction of the residual error for a given calibration process presents the main difficulty for implementing the optimization. It is proposed that the residual error is expressed as a polynomial function. This function is obtained as a result of fitting a response surface to either experimental or simulated sample estimates of the residual error. The optimization problem is then solved by identifying a reduced set of possible solutions, thus greatly simplifying the decision maker’s choice of an effective calibration procedure. An application example of this method is also included.

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