Relevant bibliographies by topics / Driving Behavior

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Driving Behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Driving Behavior"

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Zhang, Yanning, Zhongyin Guo, and Zhi Sun. "Driving Simulator Validity of Driving Behavior in Work Zones." Journal of Advanced Transportation 2020 (June 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/4629132.

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Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.
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He, Yi, Shuo Yang, Xiao Zhou, and Xiao-Yun Lu. "An Individual Driving Behavior Portrait Approach for Professional Driver of HDVs with Naturalistic Driving Data." Computational Intelligence and Neuroscience 2022 (January 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/3970571.

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More than 50% major road accidents are caused by risk driving behaviors from professional drivers of Heavy Duty Vehicles (HDVs). The quantitative estimation of driving performance and driving behaviors portrait for professional drivers is helpful to measure the driver’s driving risk and inherent driving style. Previous studies have attempted to evaluate risk driving behavior, but most of them rely on high-demand vehicle and driving data. However, few studies can dig into the causes and correlations behind individual driving behavior and quantify the driving behaviors portrait for professional driver based on long-term naturalistic driving. In this study, the data is from On-Board Unit (OBU) devices mounted in the HDVs in China. Based on the driving behavior pattern diagram and the frequency and ranking of drivers’ typical driving patterns, a driving behavior portrait approach is proposed by comprehensively considering the vehicle safety, driving comfort, and fuel economy indicators. The similarities and differences of different drivers’ driving behaviors are quantitatively analyzed. The high precision and sampling frequency data from vehicles are used to verify the proposed approach. The results demonstrated that the driving behavior portrait approach can digitally describe the individual driving behaviors styles and identify the potential driving behaviors with long-term naturalistic driving data. The development of this approach can help quantitatively evaluate the individual characteristic of risk driving behaviors to prevent road accidents.
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Liu, Wenlong, Hongtao Li, and Hui Zhang. "Dangerous Driving Behavior Recognition Based on Hand Trajectory." Sustainability 14, no. 19 (September 28, 2022): 12355. http://dx.doi.org/10.3390/su141912355.

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Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.
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Ni, Dingan, Fengxiang Guo, Hui Zhang, Mingyuan Li, and Yanning Zhou. "Improving Older Drivers’ Behaviors Using Theory of Planned Behavior." Sustainability 14, no. 8 (April 15, 2022): 4769. http://dx.doi.org/10.3390/su14084769.

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The proportion of older drivers has increased with the aging population. In order to improve the driving behavior and safety of older drivers, we aim to analyze behavior differences between older and younger drivers and then study an improvement strategy based on the older drivers’ behavioral characteristics. Older drivers’ behaviors can be enhanced through training, thereby improving driving safety. Simulated scenarios for behavior analysis and training are constructed for drivers who are recruited from the general driving population. Data on the drivers’ eye movement, physiological and psychological conditions, operation behavior, and vehicle status are collected and analyzed. The theory of planned behavior is adopted to construct a driving behavior enhancement training model for older drivers. Finally, a structural equation model is developed to comprehend the relationship between training level, driver characteristics, and traffic safety. The ability and speed of older drivers to obtain traffic information is worse than those of young and middle-aged drivers, and the vehicle control capability of older drivers has a larger volatility. The driving behavior training model can improve older drivers’ driving stability and safety, as follows: the positive effect of training on driving behavioral improvement is larger than the negative effect of aging; the negative effect of training level on dangerous driving tendency is larger than the positive effect of driver’s aging. The driving behavior of older drivers should be improved for the safety and stability of driving operations through the PNE (perceived-norm-execution) model. The relationship between training level, driving behavior characteristics, and traffic safety is discussed using the structural equation model, and results show that the training can improve the effect of the drivers’ age on the characteristics of driving behavior, and that older drivers tend to decrease dangerous driving tendencies.
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Ma, Chunmei, Xili Dai, Jinqi Zhu, Nianbo Liu, Huazhi Sun, and Ming Liu. "DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration." Mobile Information Systems 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9075653.

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Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.
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Zuraida, Rida, and Nike Septivani. "Risky-driving behavior and it relation with eco-driving behavior based on an adapted Manchester Driving Behavior questionnaire." IOP Conference Series: Earth and Environmental Science 195 (December 14, 2018): 012072. http://dx.doi.org/10.1088/1755-1315/195/1/012072.

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Bejar, M., N. Regaieg, D. Gdoura, J. Aloulou, and O. Amami. "Anxious driving behavior among taxi drivers." European Psychiatry 64, S1 (April 2021): S184—S185. http://dx.doi.org/10.1192/j.eurpsy.2021.488.

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IntroductionThe data suggest that anxious drivers may engage in problem behaviors that expose them and others to an increased risk of negative traffic events.ObjectivesTo study the problematic behavior taxi drivers related to anxiety in three areas exaggerated safety/caution, performance deficits, and hostile/aggressive behaviors and to determine the factors who are associated with them.MethodsThis is a cross-sectional descriptive and analytical study of 58 taxi drivers in the city of Sfax, Tunisia. We used an anonymous questionnaire that included a socio-demographic fact sheet, and a driver behavior rating scale: Driver Behavior Survey (DBS) with 21 items.ResultsThe mean age of the drivers was 40.8 ± 10.2 years. The sex ratio was 0.98. 75.9% were married. 6.9% lived alone. 53.4% were smokers and 25.9% drank alcohol. Coffee and tea consumption were 59% and 33% respectively. 67% had a pathological personal history, including osteoarticular pathologies. 17.2% had a history of serious accidents. The behavior related to anxiety among taxi drivers was 74.66 ± 13.35. The hostile behavior was 18.88 ± 8, the exaggerated safety behavior was 38.31 ± 7.3 and the deficit performance related to anxiety was 17.47 ± 7.1. The problematic behavior in our population was significantly associated with lifestyle alone, coffee consumption and with serious accidents.ConclusionsThe results of our study identified some risk factors that could lead to poorly adaptive driving behaviors among Taxi drivers. These elements reinforce us in the idea that this population requires special care with a meeting with the doctor.
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Tu, Huizhao, Zhenfei Li, Hao Li, Ke Zhang, and Lijun Sun. "Driving Simulator Fidelity and Emergency Driving Behavior." Transportation Research Record: Journal of the Transportation Research Board 2518, no. 1 (January 2015): 113–21. http://dx.doi.org/10.3141/2518-15.

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Yang, Xiao Yu, Dan Li, and Peng Jun Zheng. "Effects of Eco-Driving on Driving Performance." Applied Mechanics and Materials 178-181 (May 2012): 2859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2859.

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This paper investigates the effects of two driving behaviors on driving performance, the driving with eco-driving support and the general driving. Through observing and analyzing these driving behaviors in a variety of situations, driving performance under conditions of with and without eco-driving was evaluated. Based on the measurements on fuel consumption, speed control and gear use, it was found that eco-driving device can guide drivers to take proper driving behavior, such as in which way to drive and how to drive in order to achieve energy saving. The paper revealed the effects of eco-driving and how to drive efficiently.
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Nachmann, Karl, Benjamin Pillot, Petrina Moore, and Eva Wiese. "Driving with Robots: Mind perception and propensity for aggressive driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1965–70. http://dx.doi.org/10.1177/1071181320641473.

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Mind perception, or the tendency to ascribe agency (i.e., the ability to plan and act) and experience (i.e., the ability to sense and feel) to others, is an important design consideration for human-robot inter-action since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently, lead to different emotional reactions and trigger different behaviors of the ob-server as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional, and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations, the robot driver high in agency was perceived as more intentional and elicited more irritation in partici-pants.
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Dissertations / Theses on the topic "Driving Behavior"

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Toledo, Tomer 1969. "Integrating driving behavior modeling." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29285.

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Thesis (Ph. D .)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.
Includes bibliographical references (p. 192-197).
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. An integrated driving behavior model, which utilizes these concepts, is developed. This model captures both lane changing and acceleration behaviors. The driver's short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration behavior to facilitate the lane change using the target gap. Hence, interdependencies between lane changing and acceleration behaviors are captured. The lane changing portion of the model integrates mandatory and discretionary lane changing considerations in a single model. Hence, allowing trade-offs between these considerations to be captured. Moreover, the integrated lane changing model overcomes the difficulty in defining conditions that trigger a mandatory lane changing situation. Model components that describe the choice of target gaps and acceleration behaviors to facilitate lane changing are introduced. The parameters of all components of the driving behavior model are estimated jointly using detailed vehicle trajectory data collected in a freeway in Arlington, VA. The result is a driving behavior model applicable to the behavior of all freeway traffic. Validation results of the proposed model using a microscopic traffic simulator are also presented.
by Tomer Toledo.
Ph.D .
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Koutentakis, Dimitrios. "Modeling human driving behavior." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129895.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 81-84).
The goal of this thesis paper is to explore models that can predict and anticipate driver behaviors on the road and give probabilities on future actions of neighboring vehicles, while being lightweight enough to be formally verifiable. This thesis starts with looking into related work and doing a short literature review on previous work on driver models. We then talk about the available datasets used to perform such work, different models used (from classic regressions to neural networks) and finally present my approach and my results.
by Dimitrios Koutentakis.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Golshani, Nima. "Analysis of aggressive driving behavior| A driving simulation study." Thesis, State University of New York at Buffalo, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1600753.

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Aggressive driving behavior is the cause of a large percentage of accidents and fatalities, and it is growing every year. In several cases some drivers perceive their driving as non-aggressive when in fact they drive aggressively. To investigate factors affecting perceived (self-reported) and observed (based on the data from a driving simulation experiment) aggressive driving behavior, four fixed effect bivariate ordered probit models for three categories of aggressive driving behavior (i.e., observed and perceived non-aggressive, somewhat aggressive and very aggressive driving) are estimated. The models simultaneously account for panel data effects and cross equation error correlation. To further address unobserved heterogeneity, six grouped random parameter bivariate probit models for two outcomes (observed and perceived non-aggressive and aggressive driving) are estimated. Each model type is estimated using different barriers as driving behavior separators (either physical barriers in the distribution, or basic statistical measures). The results show that different socio-demographic characteristics, driving experience and exposure, and behavioral information of the participants affect the observed and the perceived aggressive driving behavior. The proposed approach, as a whole, provides an incremental step towards better understanding the different factors that affect the observed and the perceived aggressive driving behavior.

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Ventaglio, Daniele. "Knowledge management driving customer behavior." Thesis, Pepperdine University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1541786.

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Addressing the needs and wants of the customers increases the intention of the customers to remain loyal to the company that satisfies their wants and needs. Knowledge Management (KM) and Customer Relationship Management (CRM) have both been shown to impact customer behavior. The purpose of this thesis was to explore and understand the impact of KM supported by a CRM on customer behaviors, specifically customer loyalty and customer perceived value. The results indicate that in order for KM and CRM to be effective in affecting positive changes in organizations, certain conditions need to be met. These include having employees perceive the importance of the implementation of KM and CRM approaches / processes through incorporating both KM and CRM in the business culture. All employees of all levels of the company need to have the same objective, scope and roles and responsibilities are clear defined and communicated. Both KM and CRM end-to-end processes need to be supported by one stable, easy to use, and easily accessible system with a high sophisticated search engine. The significance of this study is three-fold: for the academic community, for the companies that aim to attain competitive advantage over others, and for the customers of these companies.

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Neuenburg, Jesko-Philipp. "Market-driving behavior in emerging firms /." Wiesbaden : Gabler, 2010. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=018694613&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Olsson, Magnus. "Behavior Trees for decision-making in Autonomous Driving." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183060.

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This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four steps. The different versions were evaluated using software maintainability metrics (Cyclomatic complexity and Maintainability index) in order to extrapolate and reason about more complex implementations as would be required in a real autonomous vehicle. It is concluded that as the AI requirements scale and grow more complex, the BTs likely become substantially more maintainable than FSMs and hence may prove a viable alternative for autonomous driving.
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Yanamanamanda, Srinivasa Rao. "Study of car-leading behavior in passing maneuvers on freeways /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418078.

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Wei, Junqing. "Autonomous Vehicle Social Behavior for Highway Driving." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/919.

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In recent years, autonomous driving has become an increasingly practical technology. With state-of-the-art computer and sensor engineering, autonomous vehicles may be produced and widely used for travel and logistics in the near future. They have great potential to reduce traffic accidents, improve transportation efficiency, and release people from driving tasks while commuting. Researchers have built autonomous vehicles that can drive on public roads and handle normal surrounding traffic and obstacles. However, in situations like lane changing and merging, the autonomous vehicle faces the challenge of performing smooth interaction with human-driven vehicles. To do this, autonomous vehicle intelligence still needs to be improved so that it can better understand and react to other human drivers on the road. In this thesis, we argue for the importance of implementing ”socially cooperative driving”, which is an integral part of everyday human driving, in autonomous vehicles. An intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicles to perform cooperative social behaviors. We also propose a behavioral planning framework to enable the socially cooperative behaviors with the iPCB algorithm. The new architecture is implemented in an autonomous vehicle and can coordinate the existing Adaptive Cruise Control (ACC) and Lane Centering interface to perform socially cooperative behaviors. The algorithm has been tested in over 500 entrance ramp and lane change scenarios on public roads in multiple cities in the US and over 10; 000 in simulated case and statistical testing. Results show that the proposed algorithm and framework for autonomous vehicle improves the performance of autonomous lane change and entrance ramp handling. Compared with rule-based algorithms that were previously developed on an autonomous vehicle for these scenarios, over 95% of potentially unsafe situations are avoided.
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VanValkenburg, MaryAnn E. "Alloy-Guided Verification of Cooperative Autonomous Driving Behavior." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1354.

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Alloy is a lightweight formal modeling tool that generates instances of a software specification to check properties of the design. This work demonstrates the use of Alloy for the rapid development of autonomous vehicle driving protocols. We contribute two driving protocols: a Normal protocol that represents the unpredictable yet safe driving behavior of typical human drivers, and a Connected protocol that employs connected technology for cooperative autonomous driving. Using five properties that define safe and productive driving actions, we analyze the performance of our protocols in mixed traffic. Lightweight formal modeling is a valuable way to reason about driving protocols early in the development process because it can automate the checking of safety and productivity properties and prevent costly design flaws.
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Backman, Martin. "Driving skill : the role of car control behavior /." Turku : Turun yliopisto, 2001. http://catalogue.bnf.fr/ark:/12148/cb402215287.

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Books on the topic "Driving Behavior"

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Neuenburg, Jesko-Philipp. Market-Driving Behavior in Emerging Firms. Wiesbaden: Gabler, 2010. http://dx.doi.org/10.1007/978-3-8349-8492-0.

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Neuenburg, Jesko-Philipp. Market-Driving Behavior in Emerging Firms: A Study on Market-Driving Behavior, its Moderators and Performance Implications in German Emerging Technology Ventures. Wiesbaden: Gabler Verlag / GWV Fachverlage GmbH, Wiesbaden, 2010.

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Hopper, Diana Lynn. Stop signs: A naturalistic observation of driving behavior of Sudburians. Sudbury, Ont: Laurentian University, Department of Psychology, 1996.

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Boyle, John M. National Survey of Drinking and Driving Attitudes and Behavior, 1993. [Washington, DC]: National Highway Traffic Safety Administration, 1995.

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United States. Congressional Budget Office., ed. Effects of gasoline prices on driving behavior and vehicle markets. [Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2008.

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M, Boyle John. National Survey of Drinking and Driving Attitudes and Behavior, 1993. [Washington, DC]: National Highway Traffic Safety Administration, 1995.

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Modeling driver characteristics: Driver behavior in traffic. Washington, D.C.]: U.S. Dept. of Transportation, Federal Highway Administration, 2010.

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Erich, Goode, ed. Annual editions: Drugs, society, and behavior. 7th ed. Guilford, CT: Dushkin Publishing Group, 1992.

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Royal, Dawn. National survey of speeding and unsafe driving attitudes and behavior, 2002. Washington, D.C: National Highway Traffic Safety Administration, 2004.

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L, Cooper Cary, ed. Employee morale: Driving performance in challenging times. New York: Palgrave Macmillan, 2009.

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Book chapters on the topic "Driving Behavior"

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Grasso, Giorgio, Pietro Perconti, and Alessio Plebe. "Assessing Social Driving Behavior." In Advances in Intelligent Systems and Computing, 111–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11051-2_17.

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Spiegel, Stephan. "Discovery of Driving Behavior Patterns." In Smart Information Systems, 315–43. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14178-7_12.

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Wang, Junxiu. "Research on Risky Driving Behavior." In Research Series on the Chinese Dream and China’s Development Path, 211–32. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2270-9_10.

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Ulbrich, Simon, Fabian Schuldt, Kai Homeier, Michaela Steinhoff, Till Menzel, Jens Krause, and Markus Maurer. "Testing and Validating Tactical Lane Change Behavior Planning for Automated Driving." In Automated Driving, 451–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31895-0_19.

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Kita, Hideyuki. "Design of Driving Environment, Driving Behavior, and Traffic Safety." In Transportation, Traffic Safety and Health — Human Behavior, 229–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57266-1_14.

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Itoh, Toshihiko, Shinya Yamada, Kazumasa Yamamoto, and Kenji Araki. "Prediction of Driving Actions from Driving Signals." In In-Vehicle Corpus and Signal Processing for Driver Behavior, 197–210. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-79582-9_16.

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Shahab, Qonita. "Supporting Behavior Change in Cooperative Driving." In Communications in Computer and Information Science, 323–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31479-7_55.

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Zhang, Yuanjian, and Zhuoran Hou. "Eco-Driving Behavior of Automated Vehicle." In Recent Advancements in Connected Autonomous Vehicle Technologies, 69–80. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5751-2_4.

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Treiber, Martin, and Arne Kesting. "Modeling Human Aspects of Driving Behavior." In Traffic Flow Dynamics, 205–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_12.

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Tal, Gil, Ken Kurani, Alan Jenn, Debapriya Chakraborty, Scott Hardman, and Dahlia Garas. "Electric Cars in California: Policy and Behavior Perspectives." In Who’s Driving Electric Cars, 11–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38382-4_2.

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Conference papers on the topic "Driving Behavior"

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Yong Luo and Xiuchun Guo. "Driving Behavior Analysis Applying Driving Behavior in the AHS." In 2006 IEEE Intelligent Transportation Systems Conference. IEEE, 2006. http://dx.doi.org/10.1109/itsc.2006.1706824.

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Kang, Kyungwoo. "Socioeconomic Characteristics of Speeding Behavior." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2001. http://dx.doi.org/10.17077/drivingassessment.1066.

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Basalamah, Anas, Muhammad Aurangzeb Ahmad, Mohamed Elidrisi, Saleh Basalamah, and Mohamed Mokbel. "Streaming driving behavior data." In the Third ACM SIGSPATIAL International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2442968.2442983.

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Mao, Tiezheng, and Guihua Wen. "Mutiple Driving Behavior Analysis." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.215.

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Gray, Rob, and Russ Branaghan. "Changing Driver Behavior Through Unconscious Stereotype Activation." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1309.

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Bham, Ghulam H., and Rahim F. Benerkohal. "Acceleration Behavior of Drivers in a Platoon." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2001. http://dx.doi.org/10.17077/drivingassessment.1056.

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Leonard, S. David. "Influences of Knowledge on Behavior in Automobiles." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1116.

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Dawson, Jeffrey D., Joshua D. Cosman, Yang Lei, Elizabeth Dastrup, and JonDavid Sparks. "The Relationship Between Driving Behavior and Entropy." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2007. http://dx.doi.org/10.17077/drivingassessment.1226.

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Krajewski, Jarek, David Sommer, Udo Trutschel, Dave Edwards, and Martin Golz. "Steering Wheel Behavior Based Estimation of Fatigue." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1311.

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Allen, R. Wade, Thomas D. Marcotte, Theodore J. Rosenthal, and Bimal L. Aponso. "Driver Assessment with Measures of Continuous Control Behavior." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1157.

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Reports on the topic "Driving Behavior"

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Nakamura, Takashi, Katsuya Matsunaga, Kazunori Shidoji, and Yuji Matsuki. The Measurement of Everyday Driving Behavior. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0566.

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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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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Abstract:
Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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Asao, Takafumi, Takahiro Wada, Shun'chi Doi, and Kazuyoshi Tsukamoto. Analysis of Driving Behavior Under Physical Workloads. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0055.

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Asao, Takafumi, Takahiro Wada, Shun'ichi Doi, and Kazuyoshi Tsukamoto. Influence of Physical Workloads on Driving Behavior. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0626.

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Kurtz, Jennifer M., Samuel Sprik, Genevieve Saur, and Shaun Onorato. Fuel Cell Electric Vehicle Driving and Fueling Behavior. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1501674.

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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Kalinowski, Jesse, Matthew Ross, and Stephen Ross. Endogenous Driving Behavior in Tests of Racial Profiling. Cambridge, MA: National Bureau of Economic Research, May 2021. http://dx.doi.org/10.3386/w28789.

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Wakita, Toshihiro, Koji Ozawa, Chiyomi Miyajima, Kei Igarashi, Katsunobu Ito, Kazuya Takeda, and Fumitada Itakura. Study on Driver Identification Method Using Driving Behavior Signals. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0569.

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Knittel, Christopher, and Shinsuke Tanaka. Driving Behavior and the Price of Gasoline: Evidence from Fueling-Level Micro Data. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26488.

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