Готові списки джерел за темами / Driver behaviour models

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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Driver behaviour models"

1

Li, Yi, Yuren Chen, and Fan Wang. "The Impact of Traffic Environmental Vision Pressure on Driver Behaviour." Journal of Advanced Transportation 2018 (June 5, 2018): 1–12. http://dx.doi.org/10.1155/2018/4941605.

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Car-following (CF) and lane-changing (LC) behaviours are basic components in driving process. Previous models described them as physical processes with vehicle dynamics and physical criteria. However, drivers’ decisions are greatly influenced by their subjective vision information of various traffic environment elements. To solve this problem, we propose a new concept of traffic environmental vision pressure to explain these two behaviours. The pressure source consists of two parts: nearby vehicles and infrastructures. Pressure models were built to quantify the impact of traffic and roadside infrastructures on these two behaviours. 103 field tests (53 LC and 50 CF) carried out by 40 drivers were conducted to test and calibrate the models. Drivers’ psychological data and vehicle data were collected and postprocessed. Results showed positive relationship between drivers’ psychological stress and vision pressure, which verified the assumption that traffic environmental vision information would have certain effect on driver behaviour. Quantitative thresholds of pressure value were also given and explained with test data. It is concluded that the traffic environmental vision pressure in CF and LC behaviours is quite different, and higher pressure has more impact on behaviour change. We believe that these results will be helpful to study the micro driver behaviour.
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2

Chodur, Janusz, and Radosław Bąk. "Study of driver behaviour at turbo-roundabouts." Archives of Transport 38, no. 2 (June 30, 2016): 17–28. http://dx.doi.org/10.5604/08669546.1218790.

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The article presents the results of preliminary research into the behaviour of drivers at turbo-roundabouts. The subject of the research included the frequency of driver behaviour against the traffic rules, and the speed at which vehicles drive through turbo-roundabouts. One of the crucial problems which was analysed was the influence of different kinds of traffic lane division on the behaviour of drivers. The analysis results affirm that the raised lane dividers can visibly improve the propensity of drivers to stay within the designated traffic corridor. However, it does not eliminate the phenomenon of improper lane changing on circulatory roadway. The physical separation of traffic lanes has not been determined to introduce any additional hazard. The speed of vehicles encroaching upon the neighbouring traffic corridor is visibly higher than this of vehicles following traffic rules. Using crash prediction models developed for single- and multi-lane roundabouts, the authors of the research estimated that lane dividers may reduce the number of crashes from about 10% to 17%.
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3

McGordon, A., J. E. W. Poxon, C. Cheng, R. P. Jones, and P. A. Jennings. "Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 225, no. 11 (July 20, 2011): 1518–30. http://dx.doi.org/10.1177/0954407011409116.

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The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.
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4

Manjunath, Abhijna. "Prediction of Crash Risk based on Driving Behaviour." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 453–56. http://dx.doi.org/10.22214/ijraset.2022.45252.

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Abstract: The major interest which is increasing for many kinds of applications includes human driver’s characterisation. There are different promising approaches in order to characterise the drivers by means of control theoretic driver models. The driver state is monitored by applying features of driver model from survey till real road distraction experiment. The dataset for the experiment consists of driving behavior with visuomotor and even few secondary tasks like auditory and even driving reference. The individual estimation of model parameters uses data of driving of nearly eleven drivers for error prediction identification. Few hand gestures and head movements are gentle way of getting distracted by drivers which covers many states like eye closure either short or long term. This paper represents the distraction detection system with the help of attention strategy. By matching the scaled features, the transformation of frontal face of the driver, driver recognition can be made. The severity of accident zone is found in particular area based on dataset. Driver behavior at particular hotspot location is found which is considered as the accident hotspot in order to gain better accuracy. The results help in validation of robustness and effectiveness of the model. Th
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5

Babojelić, Karlo, and Luka Novacko. "Modelling of Driver and Pedestrian Behaviour – A Historical Review." Promet - Traffic&Transportation 32, no. 5 (October 5, 2020): 727–45. http://dx.doi.org/10.7307/ptt.v32i5.3524.

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Анотація:
Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.
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6

Rothkrantz, Leon, Madalina Toma, and Mirela Popa. "AN INTELLIGENT CO-DRIVER SURVEILLANCE SYSTEM." Acta Polytechnica CTU Proceedings 12 (December 15, 2017): 83. http://dx.doi.org/10.14311/app.2017.12.0083.

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Анотація:
In recent years many car manufacturers developed digital co-drivers , which are able to monitor the driving behaviour of a car. Sensors in the car measure if a car passes speed limits, leaves its lane, or violates other traffic rules. A new generation of co-drivers is based on sensors in the car which are able to monitor the driver behaviour. Driving a car is a sequence of actions. In case a driver doesn’t show one of the actions the co-driver generates a warning signal. Experiments in the car simulator TORC were performed to extract the actions of a car driver. These actions were used to develop probabilistic models of the driving behaviour. A prototype of a warning system has been developed and tested in the car simulator. The experiments and test results will be reported in this paper.
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7

Yannes, Craig D., and Nicholas E. Lownes. "Driver behaviour considerations in calibrating microsimulation models for capacity." International Journal of Society Systems Science 2, no. 1 (2010): 84. http://dx.doi.org/10.1504/ijsss.2010.031468.

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8

Arumugam, Subramanian, and R. Bhargavi. "Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning." International Journal of Software Innovation 11, no. 1 (March 2, 2023): 1–29. http://dx.doi.org/10.4018/ijsi.319314.

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Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.
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9

Mirbeygi Moghaddam, Amirreza, Ali Ghaffari, and Alireza Khodayari. "Adaptive comfort-oriented vehicle lateral control with online controller adjustments according to driver behaviour and look-ahead dynamics." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 234, no. 2 (December 23, 2019): 272–87. http://dx.doi.org/10.1177/1464419319895835.

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This paper presents a comfort-oriented adaptive fuzzy-model predictive control strategy to control the lateral motion of a vehicle with the steering angle as the input while preventing sudden changes and unwanted motions. This is reached by utilizing three main contributions: an adaptive fuzzy model based on look-ahead dynamics, limiting the controller to the acceptable range of states to the driver and introducing an adjustment factor to the controller's cost function. Using adaptive-fuzzy models to describe the vehicle lateral dynamics and driver behaviour for the purpose of the control-oriented model enables this method of control to advantage from the low computational effort of the models while maintaining the accuracy and adaptive properties which are crucial to the performance and robustness of the system during the manoeuvres. Also, the characteristics of each driver's behaviour modify the controller towards a more comfort-oriented drive with means of the cost function and constraints. The comparative simulation indicates that the developed method leads to a comfort-oriented drive, in that the vehicle's states stay within the limits of the driver behaviour range, the fluctuations are insignificant and the control strategy is more accurate than previous methods.
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10

Bevrani, Kaveh, Edward Chung, and Pauline Teo. "The Space-Based Car-Following Model: Development and Application for Managed Motorway System Safety Evaluation." Future Transportation 1, no. 3 (September 24, 2021): 443–65. http://dx.doi.org/10.3390/futuretransp1030024.

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Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.
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Дисертації з теми "Driver behaviour models"

1

Gupta, Vishal Ph D. Massachusetts Institute of Technology. "Data-driven models for uncertainty and behavior." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91301.

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Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
117
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 173-180).
The last decade has seen an explosion in the availability of data. In this thesis, we propose new techniques to leverage these data to tractably model uncertainty and behavior. Specifically, this thesis consists of three parts: In the first part, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using hypothesis testing. The approach is flexible and widely applicable, and robust optimization problems built from our new data driven sets are computationally tractable, both theoretically and practically. Optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee. Computational evidence from classical applications of robust optimization { queuing and portfolio management { confirm that our new data-driven sets significantly outperform traditional robust optimization techniques whenever data is available. In the second part, we examine in detail an application of the above technique to the unit commitment problem. Unit commitment is a large-scale, multistage optimization problem under uncertainty that is critical to power system operations. Using real data from the New England market, we illustrate how our proposed data-driven uncertainty sets can be used to build high-fidelity models of the demand for electricity, and that the resulting large-scale, mixed-integer adaptive optimization problems can be solved efficiently. With respect to this second contribution, we propose new data-driven solution techniques for this class of problems inspired by ideas from machine learning. Extensive historical back-testing confirms that our proposed approach generates high quality solutions that compare with state-of-the-art methods. In the third part, we focus on behavioral modeling. Utility maximization (single agent case) and equilibrium modeling (multi-agent case) are by far the most common behavioral models in operations research. By combining ideas from inverse optimization with the theory of variational inequalities, we develop an efficient, data-driven technique for estimating the primitives of these models. Our approach supports both parametric and nonparametric estimation through kernel learning. We prove that our estimators enjoy a strong generalization guarantee even when the model is misspecified. Finally, we present computational evidence from applications in economics and transportation science illustrating the effectiveness of our approach and its scalability to large-scale instances.
by Vishal Gupta.
Ph. D.
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2

Keen, Steven Dale. "Modeling driver steering behaviour using multiple-model predictive control." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611428.

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3

Almén, Marcus. "Driver Model for Mission-Based Driving Cycles." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140158.

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When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.
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4

Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.

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5

Gadepally, Vijay Narasimha. "Estimation of Driver Behavior for Autonomous Vehicle Applications." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365952195.

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6

McNally, Brenton. "The Development and Validation of the CAPS Model in a Reckless Behaviour Context: Identifying the Predictors of Unsafe Driving Behaviours." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365443.

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Young drivers have markedly higher motor vehicle crash-risk and crash involvement rates than do older drivers, and are severely over-represented in both morbidity and mortality rates related to vehicle crashes. Underpinning these findings is the tendency for young drivers to engage in more risk-taking whilst driving than older drivers. The current research focuses on unsafe driving behaviours. Unsafe driving behaviours satisfy Arnett’s (1992) three criteria of recklessness: they lack mainstream social approval and may even involve violations of the law; they carry strong connotations of negative consequences by placing drivers and/or their passengers at risk of morbidity, mortality, and other negative outcomes; and, by definition, they involve deliberate deviations from safe driving. Examples of these behaviours include speeding, tailgating, driving whilst using a mobile phone, driving whilst under the influence of alcohol and other psychoactive substances, and driving whilst tired or fatigued, all of which compromise both driving performance and driving safety. These are also more common amongst younger than older drivers. The current dissertation describes an application of Mischel and Shoda’s (1995) Cognitive-Affective Personality System (CAPS) to unsafe driving behaviours. CAPS is a meta-theoretical framework that fuses research from cognitive-social theory, as well as research on connectionism and activation. CAPS posits that personality consists of a mental representation comprised of a stable system of processes or dispositions, called Cognitive-Affective Units (CAUs), which mediate the relationship between features of the situation and subsequent behaviours. The CAPS model is distinctive in that it allows researchers to evaluate multiple, relevant predictors within a comprehensive, general framework and allows for the assessment of their inter-relations.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
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7

Turley, Carole. "Calibration Procedure for a Microscopic Traffic Simulation Model." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1747.pdf.

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8

Wu, Zujian. "A generic approach to behaviour-driven biochemical model construction." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7413.

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Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features.
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9

Jomaa, Diala. "A data driven approach for automating vehicle activated signs." Doctoral thesis, Högskolan Dalarna, Datateknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:du-21504.

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Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.
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10

Higgs, Bryan James. "Emotional Impacts on Driver Behavior: An Emo-Psychophysical Car-Following Model." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64901.

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This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions.
Ph. D.
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Книги з теми "Driver behaviour models"

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C, Cacciabue Pietro, ed. Modelling driver behaviour in automotive environments: Critical issues in driver interactions with intelligent transport systems. London: Springer, 2007.

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2

Johannes Petrus Bernardus Nicolaas Derks. Cold fluid driven crack propagation: Thermo-mechanical behaviour of rock caverns. Delft, The Netherlands: Delft University Press, 1997.

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3

Buyal'skiy, Vladimir. Wind turbines with optimal control of electricity generation. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1946200.

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In the monograph, based on the analysis of modern methods of automatic control of wind power installations, a solution is proposed for the correct connection (in theoretical terms) of related problems of dynamic behavior of power units with optimal control of electricity generation. In this direction, principles, structures and algorithms have been obtained to reduce the dynamic loads of the components of modern wind turbines based on timely preparation of the system for external disturbing influences and taking into account the vibration load of the drive under different operating modes of the power unit. It is intended for researchers and specialists in the field of wind energy, automation of technological processes, system analysis, as well as graduate students and students of relevant training areas and specialties of technical universities.
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4

Snelgrove, Peter B. Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect: A test of multiple models. St. Catharines, Ont: Brock University, Dept. of Psychology, 2005.

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5

Cacciabue, P. Carlo. Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems. Springer, 2007.

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6

Sanna, Fabrizio, Patrizia Porcu, and Liana Fattore, eds. Sexual Behavior as a Model for the Study of Motivational Drive and Related Behaviors. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-117-6.

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7

Kadri, Faisal L. Animal Drives in Humans: A Cybernetic Model of "Normal" Human Behavior. Trycode, 1989.

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8

Brumby, Duncan P., Christian P. Janssen, Tuomo Kujala, and Dario D. Salvucci. Computational Models of User Multitasking. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0013.

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When users interact with computers and technology ‘in the wild’, multitasking is a practically ubiquitous part of their interactions. Human-computer interaction (HCI) researchers and practitioners have increasingly used computational models to better understand these multitasking behaviours and to build new interactive technologies that facilitate interaction and/or mitigate the problems that arise from multitasking and distraction. This chapter outlines three approaches for modelling: cognitive architectures, cognitive constraint modelling, and uncertainty modelling. These approaches are some of the most common and powerful approaches to computational models of user multitasking, and have complementary strengths. It draws on examples from several domains for which multitasking is a central component, giving a particular focus to in-car multitasking and driver distraction.
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9

Taylor, Sylvester, Paul R. Yost, Cynthia D. McCauley, and D. Scott Derue. Experience-Driven Leader Development: Models, Tools, Best Practices, and Advice for on-The-Job Development. Wiley & Sons, Incorporated, John, 2013.

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10

Taylor, Sylvester, Paul R. Yost, Cynthia D. McCauley, and D. Scott Derue. Experience-Driven Leader Development: Models, Tools, Best Practices, and Advice for on-The-Job Development. Wiley & Sons, Incorporated, John, 2013.

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Частини книг з теми "Driver behaviour models"

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Jürgensohn, Thomas. "Control Theory Models of the Driver." In Modelling Driver Behaviour in Automotive Environments, 277–92. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_16.

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Cody, Delphine, and Timothy Gordon. "TRB Workshop on Driver Models: A Step Towards a Comprehensive Model of Driving?" In Modelling Driver Behaviour in Automotive Environments, 26–42. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_2.

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Tango, Fabio, Roberto Montanari, and Stefano Marzani. "Present and Future of Simulation Traffic Models." In Modelling Driver Behaviour in Automotive Environments, 400–427. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_21.

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Carsten, Oliver. "From Driver Models to Modelling the Driver: What Do We Really Need to Know About the Driver?" In Modelling Driver Behaviour in Automotive Environments, 105–20. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_6.

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Weir, David H., and Kevin C. Chao. "Review of Control Theory Models for Directional and Speed Control." In Modelling Driver Behaviour in Automotive Environments, 293–311. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_17.

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Shinar, David, and Ilit Oppenheim. "Review of Models of Driver Behaviour and Development of a Unified Driver Behaviour Model for Driving in Safety Critical Situations." In Human Modelling in Assisted Transportation, 215–23. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1821-1_23.

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Hojaji, Fazilat, Adam J. Toth, and Mark J. Campbell. "A Machine Learning Approach for Modeling and Analyzing of Driver Performance in Simulated Racing." In Communications in Computer and Information Science, 95–105. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_8.

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AbstractThe emerging progress of esports lacks the approaches for ensuring high-quality analytics and training in professional and amateur esports teams. In this paper, we demonstrated the application of Artificial Intelligence (AI) and Machine Learning (ML) approach in the esports domain, particularly in simulated racing. To achieve this, we gathered a variety of feature-rich telemetry data from several web sources that was captured through MoTec telemetry software and the ACC simulated racing game. We performed a number of analyses using ML algorithms to classify the laps into the performance levels, evaluating driving behaviors along these performance levels, and finally defined a prediction model highlighting the channels/features that have significant impact on the driver performance. To identify the optimal feature set, three feature selection algorithms, i.e., the Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) have been applied where out of 84 features, a subset of 10 features has been selected as the best feature subset. For the classification, XGBoost outperformed RF and SVM with the highest accuracy score among the other evaluated models. The study highlights the promising use of AI to categorize sim racers according to their technical-tactical behaviour, enhancing sim racing knowledge and know how.
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Donges, Edmund. "Driver Behavior Models." In Handbook of Driver Assistance Systems, 19–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12352-3_2.

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Donges, Edmund. "Driver Behavior Models." In Handbook of Driver Assistance Systems, 1–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09840-1_2-1.

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Rämä, Pirkko, and Hanna Koskinen. "Three Driver and Operator Behaviour Models in the Context of Automated Driving – Identification of Issues from Human Actor Perspective." In Advances in Intelligent Systems and Computing, 1059–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60441-1_100.

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Тези доповідей конференцій з теми "Driver behaviour models"

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Walker, Guy, and Malcolm Calvert. "Driver Behaviour at Roadworks." In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100629.

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Road networks around the world are reaching a critical stage in their lifecycle. Typically constructed in the 1960’s and 70’s, many of the structures, now over forty years old, require increasingly significant levels of maintenance in order to ensure their continued integrity and performance. Many national transport authorities while planning ahead for this use traffic microsimulation models to help them predict the likely effects of associated roadwork on traffic flow. The challenge faced is that these models consistently under-predict traffic flows, and the resultant queue lengths, even though there is nothing fundamentally different from a speed or lane restriction for roadworks compared to those used in other normal circumstances. The reasons for this over-prediction or under-prediction are that ‘real’ traffic behaves differently from ‘modelled’ traffic. This paper explores these differences with reference to a case study example, reviews the psychological literature for explanatory factors, and uses this to propose new guidelines for how models should be designed and calibrated for improved accuracy. In the case study presented in this paper, approximately a lane’s worth of capacity is being lost due to ‘soft’ driver behaviour factors. This paper helps to explain why this is happening and how it can be recovered.
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Srinivasan Rammanoharan, Sneha, Jose Alguindigue, Apurva Narayan, and Siby Samuel. "SHRP2 Naturalistic Data Analysis of Older Drivers’ Gap-Acceptance Behaviour." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002478.

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Drivers aged 65 and older are very prone to motor vehicle crashes. Intersections appear to be hazardous for drivers of this age group due to the driver’s cognitive, perceptual, and psychomotor challenges. Literature notes that older drivers find it incredibly challenging to safely navigate left turns at signalized intersections. Studies have identified the driver’s physical health, vision, and cognition as factors that impact the ability of older drivers to sufficiently monitor the gaps in oncoming traffic to make a left turn safely. The current paper aims to address the gap in the literature by explicitly examining older drivers’ gap acceptance behaviors during left turns at protected intersections. We utilize the Naturalistic Driving Study Data collected via the Strategic Highway Research Plan (SHRP2) to understand older driver behavior better. SHRP2 makes available a geo-spatially linked, comprehensive database over a multi-year period from over 3400 participants across six sites. SHRP2 databases contain a relatively more significant proportion of younger and older drivers than the national driver population databases. This dataset includes a trip summary, vehicle data, driver questionnaire, and test battery data specifying driving history, physical and psychological conditions, demographics and exit interview data, time-series data of the drivers approaching the intersections or just after the intersections, and forward video data of the drivers approaching the intersections or just after the intersections. Data is analyzed for participants over the age of 65 and participants between the ages of 30-50. Several hundred baseline, near-crash, and crash events are obtained for comparison. The video data is annotated using the DREAM methodology. The Roadway Information Database (RID) also considers additional variables such as crash histories and traffic and weather conditions. The samples of the forward video data provide the start time and end time of each gap accepted or rejected by the turning driver, especially when turning left, during unprotected phases, and help understand the participant’s interactions with other vehicles just before and after the intersections. As the data has been collected over multiple years across multiple sites, the dataset is considered a multivariate time series model. As there is more than a one-time dependent variable, the data was analyzed using Extreme Gradient Boost (XGBoost), Long-Short Term Memory (LSTM), and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models. These models are expected to achieve an accuracy of around 80 percent at four-way intersections and approximately 60 percent in T-intersections. We anticipate that the older drivers will exhibit longer gap acceptance times and a greater frequency of gap rejections than their younger counterparts while turning left across traffic at signalized intersections. The findings of the current study will have implications for older driver safety. Researchers may use the findings to understand gap acceptance behaviors further, while policymakers may utilize the results to design mobility guidelines.
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Arhin, S. A., and A. Eskandarian. "Driver behaviour models for a driving simulator-based intelligent speed adaptation system." In URBAN TRANSPORT 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/ut090181.

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Bakken, Lars E., Tor Bjo̸rge, Tim M. Bradley, and Neal Smith. "Validation of Compressor Transient Behaviour." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30279.

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Major challenges are related to compressor and driver integration during run down. In order to understand these challenges, the pipeline compressor facility at Troll Kollsnes gas treatment plant, Norway, has been subjected to detailed trip testing and dynamic simulation analysis. The plant includes five pipeline compressors and is utilised as a pilot for analysing the transient response of a 40 MW compressor driven by a variable speed electric motor. The compressor control and protection system include an anti-surge and a hot gas bypass system. Vibration records have shown that under power outage the compressors were exposed to violent vibrations. Further investigation revealed that during a short power outage, the compressor enters the surge- and rotating stall area under certain operating scenarios. The rotating stall response resulted in reduced operating range and flexibility for the pipeline compressors. Specific precautions had to be taken to prevent the compressor from running into the low flow operating area of the performance envelope. Dynamic simulations cover important aspects related to the transient scenario analyses performed to reveal the root cause of the compressor problems. The simulation system enables sophisticated plant models to be configured from high quality standard model algorithm building blocks. Verification of the model blocks have been performed against plant records in order to validate the transient predictions. The paper reports experience from testing and verification of compressor and driver integration with reference to transient behaviour during run down. This includes the validation of the dynamic models, which apply both to the design and commissioning phase where actual plant trip tests should be used to verify the design and stability margins.
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Dia, Hussein, and Sakda Panwai. "Intelligent Mobility for Smart Cities: Driver Behaviour Models for Assessment of Sustainable Transport." In 2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud). IEEE, 2014. http://dx.doi.org/10.1109/bdcloud.2014.50.

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Grabarek, Iwona, and Włodzimierz Choromański. "Computer Simulation of the Dynamic Properties of a Complex Man-Railway Vehicle-Environment System." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/de-23256.

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Abstract This paper presents a model of the driver-locomotive-environment system achieved through the integration of separate models simulating the locomotive’s mechanical properties and the driver’s behaviour. The paper is focused on the modelling and simulation of the “driver” subsystem — for this purpose, the theory of fuzzy sets is used to present the simulation results of the system’s behaviour during a specific manoeuvre.
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Ozkan, Mehmet Fatih, and Yao Ma. "Inverse Reinforcement Learning Based Driver Behavior Analysis and Fuel Economy Assessment." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3122.

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Abstract Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver’s preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we proposed a feature-based driver behavior learning model from demonstrated driving data utilizing the Inverse Reinforcement Learning (IRL) approach to analyze various driver behaviors and their impacts on vehicle fuel consumption. The proposed approach models the individual driving style as cost function which is a linear combination of the features and their corresponding weights. The proposed IRL framework is used to find the model parameters that fit the observed driving style best. By using the learned driving behavior model, the most likely trajectories are computed and the optimized feature weights are used to analyze different driver behaviors. The different driver behaviors and their impacts on vehicle fuel consumption are then analyzed in real-world driving scenarios. Results show that the proposed IRL framework can successfully learn individual driver behaviors using vehicle trajectory data demonstrated by different real drivers. The learned driver behaviors promise a significant correlation between driving behavior and fuel consumption.
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Vasconcelos, Luís, Francisco Martins, and Luísa Cruz-Lopes. "Calibration of the IDM car-following model using trajectory data." In 7th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2022. http://dx.doi.org/10.5592/co/cetra.2022.1353.

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Car-following models describe the longitudinal movement of vehicles and are a major component of microscopic simulation packages. As car-following models seek to replicate the behaviour of individual drivers, their mathematical formulation usually includes a large set of adjustable parameters. The calibration of the model is essential to achieve accurate results, but as it may be a complex and expensive task, users often rely on default values or on simple techniques that offer poor transferability. In this paper we describe a calibration technique for the Intelligent Driver Model (IDM) that explicitly accounts for the physical meaning of each parameter. Trajectory data was collected for a sample of Portuguese drivers using an instrumented vehicle and covers the most relevant cases, such as unconstrained acceleration and deceleration manoeuvres and car following in steady-state conditions. A two-step calibration technique was followed: first, subsets of parameters with clear physical meanings were manually adjusted to replicate the velocity profiles of simple driving patterns; second, the results were used to define the bounds of values within an automatic calibration procedure for normal driving conditions. First results show that the calibration procedure allows to accurately replicate the real trajectories. There is still the concern with the transferability of results and further work is required to understand how to reach the best compromise between the model’s descriptive and predictive capacities.
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San Roma´n, Jose´ Luis, Vicente Di´az, Pedro Cobo, Carolina A´lvarez-Caldas, Jose´ Antonio Calvo, Daniel Garci´a-Pozuelo, Antonio Gauchi´a, David Ibarra, Ester Olmeda, and Alejandro Quesada. "Characterization of the Noise Emissions of a Passenger Vehicle." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63392.

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One of the main sources of noise pollution in cities is vehicle traffic. In this paper a characterization of the noise emission of a passenger vehicle has been carried out. With this aim a representative driving route for noise emission has been defined in order to study the influence of the driver typology and vehicle type. Therefore, this investigation has been developed in three phases: Firstly, usual driving in an urban area like Madrid has been characterized with a specific driving route. In addition, several vehicle models with great presence in the existing fleet of cars have been selected. Several drivers have covered the driving route at different times of the day and previous parameters have been measured in each test in order to determine average values of behavior. Secondly, the type of vehicles and drivers influence in noise emissions has been deeply analyzed. To achieve this aim a sample of vehicles has been instrumented to obtain physical measurements of the variables that can influence the noise emission level. Positions, velocities, accelerations (longitudinal and lateral) and time have been analyzed using a GPS sensor. Parameters such as, engine speed, engine load, throttle position and engine temperature have been studied through the vehicle CAN BUS and a set of microphones has measured the emitted noise in several points of the vehicle. In order to study the ecological and safety impact in urban and interurban roads by means of the measurement of noise emissions the analysis of the driver behaviour is of paramount importance. To conclude, the previous data has been analyzed and noise equivalent levels have been identified with different test configurations.
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Patkar, Nitish, Andrei Chis, Nataliia Stulova, and Oscar Nierstrasz. "Interactive Behavior-driven Development: a Low-code Perspective." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00024.

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Звіти організацій з теми "Driver behaviour models"

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Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai, and Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.

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This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the study was to analyse driving behaviour and gaze data at intersections in urban areas. Based on relevant literature, a simulated driving environment was created, in which a sample of drivers passed a variety of intersections. Considering different driver states, driving tasks, and traffic situations, the collected data provide detailed information about human gaze and driving behaviour when approaching and crossing intersections. The collected data was transferred to LKT for the development of the stochastic traffic simulation.
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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, September 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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Pulugurtha, Srinivas S., and Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.1944.

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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.
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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Mohamed Nadar Ramadan, and Günther Prokop. Report on layout of the traffic simulation and trial design of the evaluation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.244.

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Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying driver behavior model.
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Barberis, Nicholas, and Lawrence Jin. Model-free and Model-based Learning as Joint Drivers of Investor Behavior. Cambridge, MA: National Bureau of Economic Research, March 2023. http://dx.doi.org/10.3386/w31081.

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Nakatsuka, Fuyuki, Shuji Watanabe, Taro Sekine, Michiharu Okano, Youji Shimizu, Yuji Takada, and Osamu Shimoyama. Event-Driven Model on Driving Behavior in the Left Turn. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0621.

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod, and Günther Prokop. Report on design of modules for the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.245.

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As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).
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Roland-Holst, David, Kamalbek Karymshakov, Burulcha Sulaimanova, and Kadyrbek Sultakeev. ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic. Asian Development Bank Institute, December 2022. http://dx.doi.org/10.56506/fepw3647.

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Infrastructure has always been a fundamental driver of long-term economic growth, but in recent decades information and communication technology (ICT) has supported and accelerated the growth of the global economy in ways beyond the imagining of our ancestors. We examine the role of ICT infrastructure in facilitating labor markets' access and remittance flows for workers from the Kyrgyz Republic. Using a combination of traditional high frequency macroeconomic data and real time internet search information from Google Trends, we take a novel approach to explaining the inflow of remittances to a developing country. In the first attempt to model remittance behavior with GTI data in this context, we use a gravity model. We also attempt to account for both origin and destination labor market conditions, using Kyrgyz language search words to identify both push and pull factors affecting migrant decisions.
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Luo, Hao, Ricardo Chahine, Arianna Rambaram, Elizabeth Theresa Rosenzweig, Konstantina Gkritza, and Hua Cai. Assessing the Travel Demand and Mobility Impacts of Transformative Transportation Technologies in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317374.

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The rapid development of transformative transportation technologies, such as bike-sharing, shared e-scooters, and ride-hailing systems, is reshaping the transportation landscape. These transformative transportation technologies have the potential to significantly change travel behavior and travel demand and affect transportation agencies’ planning, operations, and decision-making. The objective of this project is to develop a framework and models to quantify the potential travel demand and mobility impacts of transformative transportation technologies in Indiana cities. This project analyzed historical system usage data and conducted survey studies to evaluate the availability and use of transformative transportation technologies in select Indiana cities. The project also proposed a data-driven model to study the relationship between shared micro-mobility and the existing transit system and developed a simulation model to analyze the potential mode choice change under different future development scenarios. Additionally, based on a comprehensive literature review, a list of operations; environmental, health and safety; and accessibility and equity metrics were identified as the Key Performance Indicators to evaluate transformative transportation technologies. Furthermore, as this study was conducted in the midst of the COVID-19 pandemic, the impacts of the pandemic on both traditional and transformative transportation systems were also examined as documented in the literature and stated in our survey.
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Kim, Joseph J., Samuel Dominguez, and Luis Diaz. Freight Demand Model for Southern California Freeways with Owner–Operator Truck Drivers. Mineta Transportation Institute, October 2020. http://dx.doi.org/10.31979/mti.2020.1931.

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This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and $12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or $18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or $8.94/hr and $0.22/mile or $11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes.
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