A STUDY ON CAR FOLLOWING MODELS AND THEIR EVALUATION (original) (raw)
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A car following model for traffic flow simulation
2016
The traffic flow microscopic modeling is basically important for the development of specific tools for understanding, simulating and controlling urban transportation systems. Car-following models have been developed to describe the dynamical characteristics of the moving vehicles. In this paper, we present a microscopic car following model based on the consideration of the driving behavior on a single-lane road. With this model, we propose an approach which permits to take into account the phenomenon of anticipation in driver behavior. A comparative study with the optimal velocity model is done. The proposed modeling approach is validated by simulation. The numerical simulation shows that the model can improve the representation of traffic flow.
Evaluation of Different Vehicle Following Models under Mixed Traffic Conditions
Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models. Only fewer studies have been conducted to compare the performance of different car following models under mixed traffic conditions. The present study focuses on the evaluation of different car following models under mixed traffic conditions. Specifically, the following four cars following models were selected: 1. Gipps Model, 2. Intelligent Driver Model (IDM), 3. Krauss Model and 4. Das and Asundi Model. These models were implemented in a microscopic traffic simulation model for a mid block section. Each of these models is then calibrated for three states: non-steady state with constant parameters across classes, steady state parameter and non-steady state with classwise parameters. Then the models are evaluated using the performance measures such as error in hourly stream speeds and classwise speeds, critical parameters and Mean Absolute Percentage Error (MAPE) for speed and density values, obtained at one minute intervals.
Journal of Engineering, 2020
The area of traffic flow modelling and analysis that bridges civil engineering, computer science, and mathematics has gained significant momentum in the urban areas due to increasing vehicular population causing traffic congestion and accidents. Notably, the existence of mixed traffic conditions has been proven to be a significant contributor to road accidents and congestion. The interaction of vehicles takes place in both lateral and longitudinal directions, giving rise to a two-dimensional (2D) traffic behaviour. This behaviour contradicts with the traditional car-following (CF) or one-dimensional (1D) lane-based traffic flow. Existing one-dimensional CF models did the inclusion of lane changing and overtaking behaviour of the mixed traffic stream with specific alterations. However, these parameters cannot describe the continuous lateral manoeuvre of mixed traffic flow. This review focuses on all the significant contributions made by 2D models in evaluating the lateral and longitu...
Review of Microscopic Model for Traffic Flow
2014
Today, the problem of cities urban transportation is becoming something we have to face in our daily life. In Indonesia, traffic congestion is increasingly serious. Several economic and social motivations can be related to the need to minimize the time spent in vehicles for transportation and consequently their related pollution problems. Due to these motivations, the literature on traffic phenomena is already vast and characterized by contributions covering modeling aspects, statement of problems, qualitative analysis, and particularly developed simulation generated by applications. This paper will provides a several literature review of microscopic model based on their utilities, including the critical review about the modeling approaches. Furthermore, some practical issues such as potential for future model improvement using existing and emerging data collection technologies is identified based on Indonesian traffic characteristics and will be presented as a contribution from thi...
Car-following models main characteristics: A review
8TH ENGINEERING AND 2ND INTERNATIONAL CONFERENCE FOR COLLEGE OF ENGINEERING – UNIVERSITY OF BAGHDAD: COEC8-2021 Proceedings
The Car-following model is a significant and essential part of developing any traffic micro-simulation model. This study has focused on the main types of the car-following model up to date with the main features or characteristics of this model: vehicle length, reaction time, buffer spaces, and different types of acceleration. This study attempts to figure out the optimum model and optimum values for the main characteristics of car-following models based on the previous studies. The results indicate that a safety model is the best model to represent the car-following behavior than other types. In this paper, other micro-characteristics have been summarized, too. Recently, a lot of traffic models were developed to characterize the reality of traffic behavior. However, some specific driver behavior parameters are still unspecified [1, 2]. A car-following model mainly represents the cornerstone to model the traffic impacts and different driving behaviors; this is the primary motivation to improve this model [3, 1] continuously. Hence, the developed car-following models have different strengths and weaknesses [3, 1]. Mainly, two main group factors affect the behavior of any car-following model: individual factors and situational factors. The first group includes driver and vehicle characteristics. In contrast, the second group factors are hurry and distraction, impairment resulting from alcohol, fatigue, trip purpose, and length of drive [4, 5, 6]. Therefore, this study tries to discover the significant characteristics of a car-following model to improve and develop new car-following models. Car-Following Models These models are divided into various kinds, and the main models are Gazis-Herman-Rothery (GHR), linear, safety distance, psychophysical, and fuzzy logic-based [5, 7, 8]. The GHR Model This model, also named as General Motors's model, was the most investigated and renowned and dates from the late fifties. The research was mainly conducted by a research group at General Motors Corp and carried by many other independent investigators [1]. The main basic principle that describes car-following models as a vehicle following its leader could be represented by a given stimulus [9] as follows: Response (t) = Sensitivity (t) x Stimulus (t) … ……(1) Having reported the response of the following vehicle to the preceding one, this response could be described by the acceleration (or deceleration) implemented by a driver using the control pedals. The stimulus-response model developed by [10] represented the motion of a car following in a single lane.
Microscopic Evaluation of Extended Car-following Model in Multi-lane Roads
2018
This paper describes a micro-simulation model which combined car following with lane change model. For that, we proposed a new car-following model which is an extended of velocity-separation difference model (VSDM) by introducing a new optimal velocity function, named a modified velocity-separation difference model (MVSDM) which react better in braking case. The problems of collision in urgent braking case existing in the previous models were solved. Furthermore, the simulation results show that (MVSDM) can exactly describe the driver’s behavior under braking case, where no collision occurs.
Development of an Asymmetric Car-Following Model and Simulation Validation
IEEE Transactions on Intelligent Transportation Systems, 2019
Numerous car-following models have been developed since the 1950s. However, there still exist many traffic phenomena that cannot be demonstrated using the existing models. Therefore, this research proposed a new car-following model, the Asymmetric car-following (ACF) model based on the understanding of driver's asymmetric behavior, which can explain complex traffic phenomena. We established the asymmetric car-following (ACF) rule under the vehicle's safety constraints using eight parameters that indicate the driver's characteristics and vehicle's performances. To evaluate the ACF model, we performed the simulation for car-following pairs and conducted a comparison analysis with the existing models: Newell, Gipps, GM, and IDM. As a result, the proposed ACF model showed good fitness with the empirical trajectory and the apparent asymmetric behavior compared to others. For further investigation in the congested traffic stream, we simulated a group of vehicles by adding an error term to represent the driver's unexpected behavior. The simulation showed growth, propagation, and dissipation of the stop-and-go traffic. These results proved that the ACF model has the strength to elaborate on various traffic phenomena, such as traffic hysteresis and stop-and-go traffic.
Comparative Analysis of Car Following Models Based on Driving Strategies Using Simulation Approach
Mobility and Vehicle Mechanics
Transportation and traffic affect all the aspects of everyday life. To better understand traffic dynamics traffic models are developed. On microscopic level, carfollowing models are developed and improved during long period of time. They are used in traffic simulation tools or are the basis for operation in some advanced vehicle systems. Carfollowing models describe traffic dynamics through movement of individual vehicle-driver units. This paper compares Gipps model and Intelligent Driver Model (IDM) as carfollowing models based on driving strategies. These models are derived based on assumptions such as keeping safe distance from the leading vehicle, driving at a desired speed and producing accelerations within a comfortable range. The models are implemented and simulated in MATLAB environment and the results are discussed in terms of the ability to reproduce real driving behaviour in car following scenarios.
Calibration and Validation of Microscopic Traffic Flow Models
Transportation Research Record, 2004
Microscopic simulation models are becoming increasingly important tools in modeling transport systems. There is a large number of models used in many countries. The most difficult stage in the development and use of such models is the calibration and validation of the microscopic sub-models such as the car following and gap acceptance models. This difficulty is due to the lack of suitable methods for adapting models to empirical data. The aim of this paper is to present recent progress in calibrating a number of microscopic traffic flow models. Ten very different models have been tested using data collected via DGPS-equipped cars (Differential Global Positioning System) on a test track in Japan. To calibrate the models, the data of the leading car are fed into the model under consideration and the model is used to compute the headway time series of the following car. The deviations between the measured and the simulated headways are then used to calibrate and validate the models. The calibration results agree with earlier studies as there are errors of 12 % to 17% for all models and no model can be denoted to be the best. The differences between individual drivers are larger than the differences between different models. The validation process gives acceptable errors from 17 % to 22%. But for special data sets with validation errors up to 60% the calibration process has reached what is known as "overfitting": because of the adaptation to a particular situation, the models are not capable of generalizing to other situations.