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2007, Transportation Research Part …
A framework for integrated driving behavior modeling, based on the concepts of short-term goal and short-term plan is proposed. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This behavioral framework captures drivers' planning capabilities and allows decisions to be based on anticipated future conditions.
Transportation Research Part …, 2009
This paper presents the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving behaviors. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these behaviors and represents drivers' planning capabilities. It introduces new models that capture drivers' choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers' behavior to facilitate the completion of a desired lane change using the target gap. The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, VA. The estimation results are presented and discussed in detail. Driving behavior models are fundamental to the understanding of traffic flow phenomena and form the basis for microscopic traffic simulation models. A vast body of literature (see reviews in Gerlough and Huber 1975, Leutzbach 1988, Brackstone and McDonald 1999, Rothery 2001, Hoogendoorn and Bovy 2001, Toledo 2007, among others) deals with the specification and estimation of these models, and in particular acceleration and lane changing models. However, these two behaviors are commonly modeled and implemented independently of each other. Toledo et al. (2007b) demonstrated the potential shortcomings of the independent modeling approach and presented an integrated framework to jointly model acceleration and lane changing behaviors that can represent drivers' planning capabilities. The structure of the integrated model is shown in . It assumes that drivers develop short-term plans to accomplish short-term goals. The short-term goal is defined by a target lane, which is the lane the driver perceives as best to be in among the
Transportation Science, 2014
Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes as a function of driver characteristics and lane-changing scenarios. This study contributes to the development of a comprehensive framework for modeling drivers' lane-changing maneuver on arterials by using driver behavior-related data. Focus group studies and “in-vehicle” driving tests were performed to investigate the effects of driver type under various lane changes on urban arterials and to collect microscopic vehicular data. With these field collected values, a model was developed to estimate the probability of changing lanes under various lane-changing scenarios and to estimate the corresponding gap acceptance characteristics. The lane-changing probability for each scenario was modeled as a function of the factors identified from the focus group discussions, as well as the driver types. In the gap accepta...
… Record: Journal of the …, 2003
The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies.
Journal of Intelligent Transportation Systems, 2014
Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas similar to those describing physical phenomena, such as forces, viscosity, or potential fields. These models have been carefully calibrated to represent the overall flow of traffic and they can also be extended to account for the cognitive limitations of the driver, such as reaction times. However, there are some aspects of driver behavior, such as strategic planning, that are difficult to formulate mathematically. In this article, we describe the YAES-DSIM highway simulator, which integrates virtual physics models with an agent-based model. The virtual physics component models the physical vehicle and the subconscious aspects of the driver behavior, while the agent component is responsible for the strategic and tactical decisions, which are difficult to model using virtual physics. We focus on the lane change decisions of the drivers, with special attention to the optimal lane positioning for a safe exit. We have used the model to simulate the flow of traffic on Highway 408 in Orlando, Florida, and to study the impact of various tactical and strategic decisions on the efficiency and safety of the traffic.
Driving behaviour models capture drivers' tactical manoeuvring decisions in different traffic conditions. These models are essential to microscopic traffic simulation systems. This paper reviews the state-of-the-art in the main areas of driving behaviour research: acceleration, lane changing and gap acceptance. Overall, the main limitation of current models is that in many cases they do not adequately capture the sophistication of human drivers: they do not capture the inter-dependencies among the decisions made by the same drivers over time and across decision dimensions, represent instantaneous decision making, which fails to capture drivers' planning and anticipation capabilities and only capture myopic considerations that do not account for extended driving goals and considerations. Furthermore, most models proposed in the literature were not estimated rigorously. In many cases, this is due to the limited availability of detailed trajectory data, which is required for estimation. Hence, data availability poses a significant obstacle to the advancement of driving behaviour modelling.
Sensors
Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a Contract...
2015 IEEE Intelligent Vehicles Symposium (IV), 2015
The Intelligent Driver Model (IDM) is a microscopic, time continuous car following model for the simulation of freeway and urban traffic. Its popularity is grounded in its simplicity and its capacity to describe both single vehicle velocity profiles as well as collective traffic behavior. Nevertheless, it lacks a series of properties that would be desirable for more realistic agent models. In this paper, as an alternative and improvement to the IDM, we propose the Foresighted Driver Model (FDM), which assumes that a driver acts in a way that balances predictive risk (e.g. due to possible collisions along its route) with utility (e.g. the time required to travel, smoothness of ride, etc.). Based on a risk concept developed for full behavior planning, we introduce driver model equations from the assumption that a driver will mainly try to avoid risk maxima in time and space. We show how such a model can be used to simulate driving behavior similar to full behavior planning models and which generalizes and reaches beyond the IDM modeling scenarios.
This paper summarizes a series of advances in lane changing models, which aim to provide fuller and more integrated representation of drivers' behavior. These advances include integration of mandatory and discretionary lane changes in a single framework, inclusion of explicit target lane choice in the decision process and incorporation of various types of lane changing mechanisms, such as cooperative lane changing and forced merging. In the specifications of these models, heterogeneity in the driver population and correlations among the various decisions a single driver makes across choice dimensions and time are addressed. These model enhancements were implemented in the open source microscopic traffic simulator of MITSIMLab. Their impact is demonstrated in validation case studies that compare their performance to existing models.
13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.
2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011
This paper presents a state-of-the-art behavioral model (BM) that can be used as a tool to simulate driver behavior after the onset of a yellow indication until (s)he reaches the intersection stop line. The paper presents the general framework of the proposed BM, its components, and discusses its ability to track Dilemma Zone (DZ) drivers and update the information available to them every time step until they reach a final decision. The BM framework is ideal for testing dilemma zone mitigation strategies before actual implementation. In addition, the BM framework can be easily implemented in any traffic simulation software. The paper performs system-based and agent-based characterization of the components involved in the BM framework using data collected from a controlled field driving experiment. The BM is validated using Monte Carlo (MC) simulations, and produces high success rates of 72.8% and 87.2% for system-based and agent-based models, respectively.
Journal of Transportation Engineering, 2014
Lane changing has received much attention as it is a significant component of microscopic traffic simulation. Many studies have focused on the details of the lane changing maneuver from external observation-based data which do not consider the type of driver performing the maneuver. The research reported in this paper relates the physical details of freeway lane changing to the type of driver performing the maneuver. Forty-six research participants drove an instrumented vehicle and performed a combined total of 726 freeway lane changes. Each research participant was categorized into one of four groups ranging from conservative to aggressive based on cluster analysis. The data were analyzed to identify any trends between the different driver types and their lane changing characteristics, specifically lane changing duration and gap acceptance characteristics. In general, more conservative drivers have greater lane changing durations than aggressive drivers. The gap acceptance comparison among driver types did not yield any conclusive trend. In addition, distributions were fitted to lane changing duration and gap acceptance histograms. The results suggest that driver type impacts freeway lane changing behavior, and therefore should be taken into account when developing or refining simulation-based lane changing models.
This study investigates the lane changing behaviour of passenger cars and heavy vehicle drivers on freeways and arterial roads. Lane changing has an important influence on traffic flow through its impact on the surrounding drivers. In this study detailed vehicle trajectory data are used as a basis for the analysis of drivers' behaviour. It is found that, on freeways, gaining a speed advantage for passenger car drivers is a motivation for a lane changing execution. Heavy vehicles driver on freeways do not increase their speed after the completion of lane changing manoeuvre. The speed of the vehicle immediately behind the heavy vehicle is found to be higher than the heavy vehicle speed a few second before the lane changing. This indicates that heavy vehicles may sometimes change lanes to allow faster vehicles to pass. On arterial roads, results showed that passenger car drivers do not gain speed advantages after the lane changing. They may execute a mandatory lane changing in accordance with their immediate travel plans. In contrast heavy vehicle drivers change lane when the surrounding vehicles are far from the heavy vehicle so as to place themselves in the best lane for future manoeuvres.
Transportation in Developing Economies, 2016
Most published microscopic driving behavior models, such as car following and lane changing, were developed for homogeneous and lane-based settings. In the emerging and developing world, traffic is characterized by a wide mix of vehicle types (e.g., motorized and non-motorized, two, three and four wheelers) that differ substantially in their dimensions, performance capabilities and driver behavior and by a lack of lane discipline. This paper presents a review of current driving behavior models in the context of mixed traffic, discusses their limitations and the data and modeling challenges that need to be met in order to extend and improve their fidelity. The models discussed include those for longitudinal and lateral movements and gap acceptance. The review points out some of the limitations of current models. A main limitation of current models is that they have not explicitly considered the wider range of situations that drivers in mixed traffic may face compared to drivers in homogeneous lane-based traffic, and the strategies that they may choose in order to tackle these situations. In longitudinal movement, for example, such strategies include not only strict following, but also staggered following, following between two vehicles and squeezing. Furthermore, due to limited availability of trajectory data in mixed traffic, most of the models are not estimated rigorously. The outline of modeling framework for integrated driver behavior was discussed finally.
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019
Lane changing is regarded as one of the most challenging behaviors of drivers. The lane-changing behaviors are divided into mandatory and discretionary. This study proposes an adaptive neuro-fuzzy model of discretionary lane-changing behavior in real traffic flow. Similar to other behaviors of drivers, lane changing is influenced by human factors, including age, gender, level of driving experience, hastiness, cautiousness, and alertness as well as environmental factors such as road and weather conditions. Identifying and measuring the said factors seem to be difficult or, in some cases, impossible. This study sorts out the lane-changing behavior into moments and two time intervals. In these time intervals, distance and relative speed, affected by the said factors, are accounted for in terms of time parameters and fed as inputs to the proposed predictive model. This is the innovative and distinguishing feature of the present study when compared to other researches. Finally, simulatio...
Sustainability, 2019
Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic "conscious" in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.
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.
Transportation Research Part F-traffic Psychology and Behaviour, 2018
In this paper we address drivers' actions prior to mandatory lane changes of long combination vehicles in dense highway traffic. The studied driver actions were: turn indicator activation, speed reduction and lateral intrusion. We categorised and compared the drivers' actions with respect to the surrounding traffic cooperation and the level of urgency. Urgency here was based on the remaining distance to a targeted exit ramp. The results show that when the subject vehicle is close to the exit ramp, drivers used speed reduction significantly more than when the vehicle is further away. No significant difference was found for the use of lateral intrusion considering the distance to the exit ramp. As regards traffic cooperation, significant differences were found for both speed reduction and lateral intrusion. The drivers' speed reduction and lateral intrusion were significantly greater when the surrounding traffic cooperation was low.
This paper summarizes a series of advances in lane changing models aiming at providing a more complete and integrated representation of drivers' behaviors. These advances include the integration of mandatory and discretionary lane changes in a single framework, the inclusion of an explicit target lane choice in the decision process and the incorporation of various types of lane-changing mechanisms, such as cooperative lane changing and forced merging. In the specifications of these models, heterogeneity in the driver population and correlations among the various decisions a single driver makes across choice dimensions and time are addressed. These model enhancements were implemented in the open source microscopic traffic simulator of MITSIMLab, and their impact was demonstrated in validation case studies where their performance was compared to that of existing models. In all cases, a substantial improvement in simulation capability was observed.
The connected environment provides real-time information about surrounding traffic; such information will substantially change how people drive. The connected environment has the potential to enhance mobility by alleviating traffic congestion, improving traffic safety, and reducing negative environmental impact. However, the literature to date is devoid of any conclusive evidence of the connected environment's impact on driving behaviour, and consequently on traffic flow models. This dissertation focusses on these issues, more specifically the lane changing component of these issues. Lane-changing is one of the primary driving tasks that drivers perform frequently on the road. The importance of modelling lane-changing behaviour is widely acknowledged in the literature because of its negative impact on traffic flow efficiency, road safety, environment, etc. Modelling lane-changing behaviour in a traditional environment (i.e., an environment without information assistance system) has been well studied (at least the decision-making component), and numerous models have been developed using various approaches such as rulebased, utility theory-based, cellular automata-based, fuzzy logic-based, game theory-based, and many others.
2004
Driving behavior is significantly affected by the presence of exclusive lanes. Particularly, unlimited access to exclusive lanes result significant amount of special type of lane-changing actions. The objective of this thesis is to develop an improved lanechanging model that has a generalized structure and is flexible enough to capture the lane-changing behavior in all situations including the presence of unlimited access
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