Modeling Driver Behavior in Road Traffic Simulation

Advances in Intelligent Vehicles, 2014

Since the pioneering theoretical study in human driver behaviors by Gibson and Crooks in 1938 [1], many researchers have contributed to driver behavior characteristics [2e5]. In order to reveal and describe driver behavior and characteristics, many researchers have contributed to driver behavior modeling, including the GaziseHermaneRothery (GHR) model , the Gipps model [7], the linear (Helly) model , and neural network and fuzzy logic models [9,10].

Computing in Civil Engineering (2005), 2005

Microscopic simulation of traffic flow has spawned many different behavioral models, some of them commercially distributed, some developed at academic institutions for scientific purposes. These models are either kept secret (for commercial products) or substantially lacking in terms of usability and visualization. In this paper, we introduce the simulation environment BABSIM that allows for different driving behaviors to be integrated into one common simulation package. An overview of microscopic simulation is given, and several existing models and their implementations are being discussed. The fundamental structure of the BABSIM package, its user interface and the calibration of the model parameters are presented.

2008

This paper presents a novel approach of modeling human driving behavior in a more realistic way that can be effectively utilized in realizing intelligent transportation systems to ensure efficient, safe, secure and human-friendly vehicle control and transportations. A number of supporting systems based on individual driving behavior are identified. The proposed comprehensive driving model approximates complete behavior of individual drivers focusing not only the ideal steady and transient driving styles but also their natural variations. Simulation results and observations from real driving scenario illustrate the significance of the proposed model and its scopes.

IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), 2003

Although much attention has been given to the simulation and modeling of driver behaviour, and comparison and testing of differing algorithms (such as car following) is now performed, there are several assumptions in use regarding

" Design and Implementation of Evidence Collection System from Event Data Recorder " , deals with collection of Evidence for accident analysis. These evidences are in form of graphs. It includes graph of Speed Vs Time, Engine Temperature Vs Time, Distance Vs Time, Brake Pressure Vs Time, Engine Speed (RPM) Vs Time, Impact Vs Time and Tire Pressure Vs Time. Also it provides single graph merged with all these parameters at the time of accident. One more advantage of system is that, it can also provide Driver Behavior Model. By using this, driver's driving performance could be found out. This system is nothing but the GUI constructed using Microsoft Visual Studio. All graphs are plotted using this Graphical User Interface. For Data Analysis purpose, B-tree algorithm is used here. Its performance for secondary storage devices and huge data handling capacity makes it more efficient. Data recorded by Event Data Recorder is used here as input. Event Data Recorder is also called as Black box of vehicles. It records vehicular data like speed, date, time, location (latitude, longitude), alcohol content of driver, airbag deployment etc. This data plays an important role detection and analysis of accident if any persists. These EDR are controlled by processors for very accurate operations.

HAL (Le Centre pour la Communication Scientifique Directe), 2007

Traffic phenomena come on the one hand from supply / demand mechanisms and on the other hand from the interactions between the various actors involved. Simulation models have been developed for several decades by traffic engineers to reproduce the phenomena. Based on the identification of observed traffic, they are unfortunately limited when the study is related to future situations (i.e. non existing, thus non observable, ones). Driver models have also been developed for decades by psychologists, but these models are also often very limited (i.e. they deal with very few and very specific driving tasks) and not operational (i.e. they are conceptual models). The simulation of the impact of a change in the traffic system is nevertheless a key issue, both from the safety and the capacity standpoints. The behaviour of drivers facing a new situation is extremely difficult to forecast, since human beings easily adapt their behaviour in response to infrastructure and equipments. They will not always use them according to designers' expectations (a rational use for collective optimisation) but, on the contrary, they very often follow individual issues, such as minimisation of constraints or economy of manoeuvres. These different standpoints often lead to incoherences between design and uses, which have a negative impact on safety as well as on capacity. Designing tools allowing a systemic approach of changes in the traffic system is the main objective of the INRETS MSIS department. Based on the joint use of a driving simulator and behavioural traffic simulation, the proposed approach (called "integrated approach") consists of a four stage iterative process which jointly uses a driving simulator and a behavioural microscopic traffic simulation model. To carry on studies according to this approach, MSIS team has designed a behavioural traffic simulation model and a driving simulator architecture, both novel.

Traffic phenomena come on the one hand from supply / demand mechanisms and on the other hand from the interactions between the various actors involved. Simulation models have been developed for several decades by traffic engineers to reproduce the phenomena. Based on the identification of observed traffic, they are unfortunately limited when the study is related to future situations (i.e. non existing, thus non observable, ones). Driver models have also been developed for decades by psychologists, but these models are also often very limited (i.e. they deal with very few and very specific driving tasks) and not operational (i.e. they are conceptual models). The simulation of the impact of a change in the traffic system is nevertheless a key issue, both from the safety and the capacity standpoints. The behaviour of drivers facing a new situation is extremely difficult to forecast, since human beings easily adapt their behaviour in response to infrastructure and equipments. They will not always use them according to designers' expectations (a rational use for collective optimisation) but, on the contrary, they very often follow individual issues, such as minimisation of constraints or economy of manoeuvres. These different standpoints often lead to incoherences between design and uses, which have a negative impact on safety as well as on capacity. Designing tools allowing a systemic approach of changes in the traffic system is the main objective of the INRETS MSIS department. Based on the joint use of a driving simulator and behavioural traffic simulation, the proposed approach (called "integrated approach") consists of a four stage iterative process which jointly uses a driving simulator and a behavioural microscopic traffic simulation model. To carry on studies according to this approach, MSIS team has designed a behavioural traffic simulation model and a driving simulator architecture, both novel. In our presentation we will first explain the « integrated » approach. We will then introduce both the traffic simulation model and the driving simulator architecture. We will discuss the validation process of these tools and give an example of use for the assessment of a driver support system. We will conclude with our prospects.

Journal of Advanced Transportation

We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in the long-term perspective from both, individual experience, and widely available social knowledge. The approach is illustrated as practical application to modeling commuter behavior in the city of Winnipeg, Manitoba, Canada. All simulations in the paper are fully reproducible as they have been carried out by utilizing a set of opensource libraries and tools that we have developed for the Julia ...

2007

Traffic phenomena come on the one hand from supply / demand mechanisms and on the other hand from the interactions between the various actors involved. Simulation models have been developed for several decades by traffic engineers to reproduce the phenomena. Based on the identification of observed traffic, they are unfortunately limited when the study is related to future situations (i.e. non existing, thus non observable, ones). Driver models have also been developed for decades by psychologists, but these models are also often very limited (i.e. they deal with very few and very specific driving tasks) and not operational (i.e. they are conceptual models). The simulation of the impact of a change in the traffic system is nevertheless a key issue, both from the safety and the capacity standpoints. The behaviour of drivers facing a new situation is extremely difficult to forecast, since human beings easily adapt their behaviour in response to infrastructure and equipments. They will not always use them according to designers' expectations (a rational use for collective optimisation) but, on the contrary, they very often follow individual issues, such as minimisation of constraints or economy of manoeuvres. These different standpoints often lead to incoherences between design and uses, which have a negative impact on safety as well as on capacity. Designing tools allowing a systemic approach of changes in the traffic system is the main objective of the INRETS MSIS department. Based on the joint use of a driving simulator and behavioural traffic simulation, the proposed approach (called "integrated approach") consists of a four stage iterative process which jointly uses a driving simulator and a behavioural microscopic traffic simulation model. To carry on studies according to this approach, MSIS team has designed a behavioural traffic simulation model and a driving simulator architecture, both novel.

2017 International Multi-topic Conference (INMIC), 2017

Car-following models microscopically express acceleration behavior of an individual driver. There are many car-following models each with its own assumptions. Among these car-following models, Intelligent Driver Model (IDM) has been used and cited extensively by research community. All the models including IDM have been developed with engineering perspective i.e. to reproduce perfect acceleration behavior. This study focuses on development of a humanistic car-following model. We have identified humanistic parameters that have been modeled in IDM from mathematical formulation of the model. In its existing form, parameters of IDM could be assigned arbitrary values from a prescribed range to define different driver profiles. This way, theoretically, infinite driver profiles could be created many of which does not exist in real. Literature of traffic psychology suggests that there are few dominant classes of drivers, which exhibit certain behavioral patterns. These dominant classes are characterized with the help of human personality. In our study, we have modeled a relationship between model of human's personality profile namely Big Five Factors (BFF) and parameters of IDM. The enhanced model let us reproduce individual differences in driving behaviors. The proposed model has been verified using computer simulation to investigate whether proposed humanistic car-following model produce desirable results or not. The proposed car-following model would be able to help in simulating driving behavior of an individual given that personality profile of that individual is known.