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Perception response time (PRT), the time required for drivers to respond to roadway events, is a concept that is derived from scientific psychology. The concept is now fundamental to human factors and ergonomics (HFE), to the design of transportation systems, and is often called upon in forensic analyses of traffic crashes. Yet the application of PRT is neither simple nor straightforward, especially when evaluating human performance under difficult conditions, which is typically the situation in forensic analysis of traffic crashes. This article revisits these fundamental limitations by reviewing the scientific history of PRT, highlighting its utility and evolution as well as important constraints on its application. Common violations of these constraints and their ramifications for providing accurate and defensible information in forensic practice are discussed. We conclude that driving behavior is both complex and variable, and no single PRT value, adjusted or not, will ever be universally applicable. [ABSTRACT FROM AUTHOR]

In research on traffic injury prevention, young male drivers willing to take risks are known as "young problem drivers" and have been identified as the greatest road safety challenge. This intersectional study draws on interviews and participant observation, and employs a masculinity perspective to explore how 12 young, male drivers who were injured in severe accidents assess driving after their accidents and ways in which they construct their identities as disabled men. While the participants cited their accidents as a turning point that prompted a change in their attitudes and practices regarding driving, they often continued to engage in deviant driving practices, especially speeding. In addition, the men tended to distance themselves from the social category of disability. We propose that the field of traffic injury prevention would benefit from supplementing the biological approach in the research literature with cultural perspectives on the internal logics and prevailing masculinity discourses that guide the choices of young, male drivers. Also, we argue that the men's negotiation of disability illustrates the stigma still attached to the social category of disability. [ABSTRACT FROM AUTHOR]

The present paper proposes a conceptual framework for the driver’s visual–spatial perceptual processes. Based on a theoretical analysis of driving proposed by Gibson and Crooks [(1938). A theoretical field-analysis of automobile-driving.The American Journal of Psychology,51, 453–471. doi:10.2307/1416145], the developed field of safe travel (FoST) framework suggests that at any moment the driver constructs a “field” by integrating two perceptual entities: (i) the possible available spatial fields for locomotion and (ii) the driver’s mental image of ego-vehicle outer-line and motion dynamics. This framework is used to reinterpret in a unified way a number of disparate research findings reported in the literature concerning specific driving sub-tasks (e.g. lane keeping and car following). It is argued that the FoST framework may be used to predict drivers’ behaviour in various traffic/situation environments based on their prioritisation between the above two perceptual entities. Implications of the proposed framework at a theoretical and practical level, in view of the future of driving with multiple levels of automation, are also discussed. [ABSTRACT FROM AUTHOR]

Dangerous driving behavior detection can be used in video surveillance systems to identify dangerous patterns, such as Abrupt Double Lane Change (ALC), Retrograde Driving (RD), and Illegal U-Turn (IT), for traffic design, traffic management, and law enforcement. The purpose of this study is to develop a detection method of dangerous driving behavior based on video surveillance. First, a novel method named trajectory histogram is proposed. A set of trajectory histograms (e.g., control points histogram and velocity histogram) is constructed to represent vehicle motion. Then, a frequently used feature selection method named Minimum Redundancy and Maximum Relevance (mRMR) is introduced to evaluate the most representative trajectory histograms for dangerous driving behavior detection. In addition, a hybrid algorithm, Particle Swarm Optimization-Support Vector Machine (PSO_SVM), is then developed to identify dangerous driving behavior. To validate the performance and effectiveness of the proposed method, several experiments are conducted. The results show that mRMR is better than other representative methods, namely Conditional Mutual Information Maximization (CMIM), Mutual Information Maximization (MIM), and ReliefF, for evaluating the most representative trajectory histograms. Based on the most representative trajectory histograms, the detection accuracy rate of dangerous driving behavior using PSO_SVM is superior to those of the most frequently used classifiers—Naïve Bayesian Classifier (NBC), k-Nearest Neighbor (kNN), and C4.5 decision tree. In addition, we find that the proposed method outperforms the two common approaches for dangerous driving behavior detection in video surveillance systems. Therefore, the proposed method can be widely applied to detect dangerous driving behavior in video surveillance systems. [ABSTRACT FROM PUBLISHER]

In rapidly developing economies, like urban India, where road traffic injury rates are among the world's highest, the corporate workplace offers a non-traditional venue for road safety interventions. In partnership with a major multinational corporation (MNC) with a large Indian workforce, this study aimed to elicit local employee perspectives on road safety to inform a global corporate health platform. The safety attitudes and behaviours of 75 employees were collected through self-report survey and focus groups in the MNC offices in Bangalore and Pune. Analysis of these data uncovered incongruity between employee knowledge of safety strategies and their enacted safety behaviours and identified local preference for interventions and policy-level actions. The methods modelled by this study offer a straightforward approach for eliciting employee perspective for local road safety interventions that fit within a global strategy to improve employee health. Study findings suggest that MNCs can employ a range of strategies to improve the road traffic safety of their employees in settings like urban India including: implementing corporate traffic safety policy, making local infrastructure changes to improve road and traffic conditions, advocating for road safety with government partners and providing employees with education and access to safety equipment and safe transportation options. [ABSTRACT FROM AUTHOR]

Under the traffic environment of the Internet of Vehicles, especially with the development of vehicle-to-vehicle technologies (i.e., V2V), drivers not only frequently perceive the vehicle information for preceding vehicles but also can actively check the real-time information of following vehicles with the assistance of V2V technologies. However, those traditional traffic flow models where driving behaviors are driven only by the preceding vehicle information are no longer suited to describing this situation. Therefore, a bidirectional cellular automaton model is proposed under active bidirectional looking (ABDL) context, which involves the actively obtained succeeding vehicle information and includes two special cases (i.e., the forward-looking only (FLO) and backward-looking only (BLO)). Meanwhile, a methodology of giving the sensitivity coefficients in the randomization probability of braking is introduced, which helps categorize drivers into three different sensitivity types (i.e., aggressive, normal, and conservative). Subsequently, various kinds of homogeneous and heterogeneous vehicle fleets are generated depending on the combinations of stimulation type (i.e., ABDL, FLO, and BLO) and the sensitivity types, whose safety performance can be evaluated by the average danger coefficients. Finally, through numerical experiments, it is concluded that considering both the preceding and succeeding vehicle information under V2V while driving is conducive to improving the safety performance, and the vehicular flow state, stimulation type, sensitivity type, and orders between different stimulation or sensitivity types have an important effect on the safety performance. [ABSTRACT FROM AUTHOR]

Large numbers of traffic accidents were experienced on the road networks of Arab Gulf Countries including United Arab Emirates (UAE). This had resulted in enormous loss of lives and economy. This article through using Abu Dhabi city, UAE capital as a case study is aiming to understand the reasons behind such safety problem through analysing a large accidents data-set extending over the period from 2008 to 2013. The traffic accidents data-set was obtained from Abu Dhabi traffic police department records and covers wide range of accident's attributes. A wide spectrum of accident attributes was analysed in the paper including but not limited to the time of accident, accident location, type of accident, reasons behind the accident, driver characteristics, road conditions, and many other accident attributes. A specific reasoning for each of these attributes was given by authors. Furthermore, recommendations to enhance the safety levels were introduced. [ABSTRACT FROM AUTHOR]

This study aim to determine the proportion of traffic rule infractions in Cali, Colombia, in places where a road traffic injury (RTI) occurred. Description of videotaping of sites where a person was injured in an RTI in 2009. Counts of road users and infractions were established for each road user group and were compared using a Z-test. They were found 13,491 users, distributed as follow: 8.9% were pedestrians, 4.6% cyclists, 24.6% motorcyclists, and 61.8% were automobile drivers. The most frequent traffic violation among motorcyclists was transiting on lines designated for other vehicles (55.2%). Among cyclists, the most frequent violations were transiting without a helmet (99.2%) and not wearing the designated vest (100%). Among pedestrians, crossing streets at prohibited places (77.3%), even at sites where a pedestrian bridge was present (72.7%), represented two common violations. Vulnerable road users committed more traffic infractions than automobile drivers (p < 0.001). High rates of traffic rule infractions among vulnerable road users were observed. Studies to better understand the occurrence of these behaviours and the promotion of effective interventions are warranted. [ABSTRACT FROM AUTHOR]

Problem, research strategy, and findings: Planners commonly recommend compact development in part as a way of getting people to drive less, with the idea that less driving will lead to more sustainable communities. Planners base their recommendations on a substantial body of research that examines the impact of compact development on driving. Different studies, however, have found different outcomes: Some studies find that compact development causes people to drive less, while other studies do not. I use meta-regression analysis to a) explain why different studies on driving and compact development yield different results, and b) combine different findings from many studies into reliable statistics that can better inform planning practice. I address the following questions: Does compact development make people drive less, and if so, how much less? I find that compact development does make people drive less, because most of the compact development features I study have a statistically significant negative influence on driving. The impact, however, is fairly small: Compact development features do not appear to have much influence on driving. My findings are limited to some extent because they are derived from small sample sizes. Takeaway for practice: Planners should not rely on compact development as their only strategy for reducing driving unless their goals for reduced driving are very modest and can be achieved at a low cost. [ABSTRACT FROM PUBLISHER]

This research proposes a fuzzy model built to predict the track paths of a motor vehicle on a two-lane road and two-way traffic. The purpose of this study stems from the awareness that the position of the vehicle within the cross-section hardly coincides with the axis of the lane but deviates from this one because of some conditions generally not provided in the road standards. To solve this problem, the authors prepared an instrumented vehicle, measuring many variables during some trials in a rural road located in Sicily, Italy. The resulting database was treated at first with subtractive clustering techniques to correctly size the membership functions and rules without the subjective help of an analyst. Therefore, the fuzzy model was built only on the basis of the measured data, and the result was the position of the vehicle within the cross-section of the examined road. This research can help the analyst to look for the real cause of user's misbehavior even when the variables involved are fully respectful with the road standard to modify or eliminate disturbing elements in the understanding of the road by the users. [ABSTRACT FROM AUTHOR]

Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle devices by reducing or eliminating the need for conducting driver experiments in the early stage of the development. Further, these models can help improve traffic flow simulation. This article develops a dual-task model of driving with a visual distraction task using the Queuing Network model of driver lateral control and a logistic regression model. The comparison between the model simulation data and the human data from drivers in a driving simulator shows that this computational model can perform driving with a secondary visual task well and its performance is consistent with the driver data. [ABSTRACT FROM AUTHOR]

The first-order continuum traffic model has been extensively studied in the state-of-the-art of traffic flow theory due to its simplicity and capability to represent many real traffic problems, such as a shock-wave formation. Furthermore, recent extension of the first-order model to multiclass traffic dynamics has revealed some interesting non-linear traffic phenomena such as hysteresis and capacity drop. However, most of the existing first-order continuum models do not display the widely scattered flow-density relationship. We argue in this article that the widely scattered flow-density relationship might be caused by the random variations in driving behaviour. It is shown that both of the hysteresis transitions and the wide scattering can be reproduced by a multiclass first-order model with a stochastic setting in the model parameters. The simulation results support our findings and are in good agreement with the real data. [ABSTRACT FROM AUTHOR]

Advanced driving simulators have a potential to be utilized to assess crash risks of driving behaviors, traffic surroundings, and highway designs. This study focused on investigating if drivers' stop/go decisions due to signal change in the simulator's virtual reality can be used to assess rear-end crash risk at the signalized intersection. A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. Using the virtual signalized intersection, a driving simulator experiment was conducted to test driver's stop/go decisions at two approaches of the intersection that respectively showed higher and lower rear-end crash risks in the crash history analysis. The experiment results showed that the variability in drivers' stop/go decisions at the higher crash-risk location is higher than that at the lower crash-risk location. Further, through modeling rear-end conflicts based on driver's stop/go probability as a function of potential time to intersection, it was found that the rear-end crash tendency at the two locations displayed by the driver's stop/go behavior in driving simulator is consistent with the finding from the trend in crash history analysis. This research supported that driver's stop/go behavior in a driving simulator can be utilized to assess rear-end crash risk at signalized intersections to seek effective engineering countermeasures to lower crash rates for the high-risk locations. [ABSTRACT FROM AUTHOR]