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Autonomous Vehicle (AV) technology is quickly becoming a reality on US roads. Testing on public roads is currently undergoing, with many AV makers located and testing in Silicon Valley, California. The California Department of Motor Vehicles (CA DMV) currently mandates that any vehicle tested on California public roads be retrofitted to account for a back-up human driver, and that data related to disengagements of the AV technology be publicly available. Disengagements data is analyzed in this work, given the safety-critical role of AV disengagements, which require the control of the vehicle to be handed back to the human driver in a safe and timely manner. This study provides a comprehensive overview of the fragmented data obtained from AV manufacturers testing on California public roads from 2014 to 2017. Trends of disengagement reporting, associated frequencies, average mileage driven before failure, and an analysis of triggers and contributory factors are here presented. The analysis of the disengagements data also highlights several shortcomings of the current regulations. The results presented thus constitute an important starting point for improvements on the current drafts of the testing and deployment regulations for autonomous vehicles on public roads. [ABSTRACT FROM AUTHOR]

In this work, moving synchronized flow patterns (MSP) in empirical city traffic at signals have been observed which was predicted by Kerner (2013, 2014). The single vehicle data has been obtained through aerial observations. Based on this microscopic data, we have found trajectories for all vehicles together with vehicle speeds as functions of time and space. This has allowed us to find empirical MSPs in oversaturated city traffic as well as to perform microscopic analysis, that revealed the following empirical spatiotemporal features of MSPs: (i) MSPs emerge upstream of the queue at the signal and then they can propagate through the complete road section, (ii) merging of several MSPs into a single MSP, (iii) dissolving MSPs and (iv) spontaneous emerging of MSPs faraway upstream of the signal, (v) spontaneous emerging of moving queue within an MSP that is far away upstream of the signal. As a base for this work, we used an unmanned aerial vehicle (UAV) to record videos of inner city traffic situations and supervised tracking methods to gather the vehicle trajectories. [ABSTRACT FROM AUTHOR]

Education, enforcement and engineering countermeasures are implemented to make road users comply with the traffic rules. Not all the traffic rule violations can be addressed nor countermeasures be implemented at all unsafe locations, at once, due to limited funds. Therefore, this study aims at ranking the traffic rule violations resulting in crashes based on individual ranks, such as 1) frequency (expressed as a function of the number of drivers violating a traffic rule and involved in crashes), 2) crash severity, 3) total crash cost, and, 4) cost severity index, to assist transportation system managers in prioritizing the allocation of funds and improving safety on roads. Crash data gathered for the state of North Carolina was processed and used in this study. Variations in the ranks of traffic rule violations were observed when individual ranking methods are used. As an example, exceeding authorized speed limit and driving under the influence of alcohol are ranked 1st and 2nd based on crash severity while failure to reduce speed and failure to yield the right-of-way are ranked 1st and 2nd based on frequency. To minimize the variations and capture the merits of individual ranking methods, four different composite ranks were computed by combining selected individual ranks. The computed averages and standard deviations of absolute rank differences between composite ranks is lower than those obtained from individual ranks. The weights to combine the selected individual ranks have a marginal effect on the computed averages and standard deviations of absolute rank differences. Combining frequency and crash severity or cost severity index, using equal weights, is recommended for prioritization and allocation of funds. [ABSTRACT FROM AUTHOR]

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. These insights facilitate the traffic understanding using curves, histograms, heatmaps, and animations. The present work presents a valuable added value for academic researchers and transportation engineers to automate the traffic video analytics process and extract useful insights to optimize traffic performance. TAU is a ready-to-use framework for any Transportation Engineer to better understand and manage daily road traffic (video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI). The source code is available at: https://github.com/bilel-bj/TAU. [ABSTRACT FROM AUTHOR]

In 2010, the left turn flashing yellow arrow (FYA) signal displays were installed at signalized intersections on state routes in the Peoria, Illinois, area. Supplemental traffic signs with text “Left Turn Yield on Flashing Yellow Arrow” were mounted on the mast arm adjacent to the left turn signal at over half of the FYA installations. The purpose of this paper is to present the results of the effectiveness evaluation of the FYA supplemental sign on safety. Analyses are presented on the effects of the FYA supplemental sign for all drivers and a subset of drivers age 65 and older. A crash-based comparison of 164 FYA approaches including 90 approaches with the sign and 74 approaches without the sign showed greater crash reductions when the supplemental FYA sign was present. The results also showed that crashes involving drivers age 65 and older did not experience the same magnitudes of crash reductions as compared to all drivers. The findings of this research indicate that supplemental FYA signs may help in improving safety for left-turning vehicles during the permissive interval. Thus, it is recommended that supplemental signs be used when initially implementing the FYA, and that effort to educate the driving public on new traffic control be made to further improve safety at signalized intersections. [ABSTRACT FROM AUTHOR]

A few studies have shown that road users can express realistic risk judgements, in the sense that they perceive higher risk when their behavior is in fact riskier. Our aim was to examine whether the realism of absolute and comparative judgements about speeding risks could be found among an at-risk driver population made of 539 French traffic regulation offenders taking a driving licence points recovery course. The results showed that course takers’ absolute judgements regarding speeding-ticket risk were realistic: the more course-takers had the intention to drive fast and reported driving-related sensation-seeking (DRSS), the more they evaluated themselves as being at risk of having a speeding-ticket. The realism was also observed when considering comparative judgements regarding speeding-ticket risk, but only among the more than 25 years old participants. Moreover, the realism was not observed when considering course takers’ absolute and comparative judgements regarding speeding-induced crash risk. Hence, it is important to increase communication about the dangerousness of speeding among specific groups such as traffic regulation offenders, above all among young course takers who reported high speeds and DRSS. [ABSTRACT FROM AUTHOR]

Highlights: [•] Four approaches to develop informative priors for the independent variables were proposed. [•] Effects of the informative priors on Bayesian hierarchal models have been examined. [•] Poisson-gamma models are more robust with the informative priors in achieving better model fit and estimation accuracy. [•] Informative priors for the independent variables are more effective than the inverse dispersion parameter. [ABSTRACT FROM AUTHOR]

Highlights: [•] We examine how the accuracy of predictive accident models (PAMs) may decay. [•] We compare different methods and assumptions for fitting PAMs to new data. [•] We recommend use of the variable shaped gamma distribution to model overdispersion. [•] We propose updating strategies for PAMs that account for statistical dependencies. [•] We provide example code for the methods proposed. [ABSTRACT FROM AUTHOR]

Abstract: Analyses of intersection accidents have shown that in most cases drivers overlook or see other road users with the right of way too late that they cannot react in time. Appropriate visual warning signals which support drivers in their attention allocation and driving behavior could be useful to improve drivers’ reaction. In a driving simulator study, two warning strategies varying in their timing: (1) top-down warning while approaching a critical intersection and (2) bottom-up warning directly before the critical incident were investigated in a T-intersection situation. For the bottom-up warning, two warning signal designs were compared. 48 subjects (M =27.3 years, SD=7.4 years) participated in the study. Driving data as well as subjective evaluation of the three warning signals (one early and two late warning signals) were analyzed. The early warning signal which was given while approaching the intersection showed a positive effect. Here, most collisions could be avoided due to drivers’ adaptation of their driving behavior toward safer driving. They waited longer at the intersection before turning and finally turned with a lower velocity. In addition, drivers evaluated the early warning signal as very “useful”. With regard to the late warnings a much smaller effect was found. From these results, requirements can be derived for the design of effective warning strategies when driving at critical intersections. [Copyright &y& Elsevier]

Abstract: Collision diagnosis is the second step in the six-step road safety management process described in the AASHTO Highway Safety Manual (HSM). Diagnosis is designed to identify a dominant or abnormally high proportion of particular collision configurations (e.g., rear end, right angle, etc.) at a target location. The primary diagnosis method suggested in the HSM is descriptive data analysis. This type of analysis relies on, for example, pie charts, histograms, and/or collision diagrams. Using location specific collision data (e.g., collision frequency per collision configuration for a target location), safety engineers identify (the most) frequent collision configurations. Safety countermeasures are then likely to concentrate on preventing the selected collision configurations. Although its real-world application in engineering practice is limited, an additional collision diagnosis method, known as the beta-binomial (BB) test, is also presented as the secondary diagnosis tool in the HSM. The BB test compares the proportion of a particular collision configuration observed at one location with the proportion of the same collision configuration found at other reference locations which are similar to the target location in terms of selected traffic and roadway characteristics (e.g., traffic volume, traffic control, and number of lanes). This study compared the outcomes obtained from descriptive data analysis and the BB test, and investigates two questions: (1) Do descriptive data analysis and the BB tests produce the same results (i.e., do they select the same collision configurations at the same locations)? and (2) If the tests produce different results, which result should be adopted in engineering practice? This study''s analysis was based on a sample of the most recent five years (2005–2009) of collision and roadway configuration data for 143 signalized intersections in the City of Saskatoon, Saskatchewan. The study results show that the BB test''s role in diagnosing safety concerns in road safety engineering projects such as safety review projects for existing roadways may be just as important as the descriptive data analysis method. [Copyright &y& Elsevier]

Abstract: Knowledge of the factors that contribute to pedestrian crashes is fundamental to the design of engineering, enforcement and education measures to decrease the number and severity of these crashes. To identify the factors that contribute to pedestrian crashes on rural highways crossing urban areas in the Federal District, Brazil, an investigatory procedure was developed. The procedure was implemented by the Federal District Road Department (DER–DF) for a period of 6months. This study presents the investigatory method and the results. Data analysis indicated the importance of conducting detailed studies of the factors that contribute to pedestrian crashes. The main contributory factors identified are presented and discussed. [Copyright &y& Elsevier]

Abstract: The misperception of vehicle approach speed is a key contributory factor to road traffic crash involvement. Past research has indicated that individuals use the rate of visual looming to calculate the time to passage (TTP) of a vehicle, and that smaller vehicles loom to a lesser extent than larger vehicles. Despite a disproportionate number of fatal injuries occurring on the road after dark, and a higher than average number of accidents involving automobile drivers violating the right of way of a motorcyclist occurring in low light conditions, there has been very little consideration of the accuracy of TTP for smaller and larger vehicles under low levels of luminance. We investigated drivers’ judgments of motorcycle and car approach speeds across a number of levels of luminance within a virtual city scene, as well as the effectiveness of a tri-headlight formation on motorcycle speed judgments. The accuracy of car approach speed judgments were not affected by changes in lighting conditions, but speed judgments for the solo headlight motorcycle became significantly less accurate as lighting reduced in the early night and night-time conditions. Incorporation of a tri-headlight formation onto the standard motorcycle frame resulted in improved accuracy of approach speed judgments, relative to the solo headlight motorcycle, as ambient light levels reduced. The practical implications of the findings are discussed in terms of road safety and motorcycle design. [Copyright &y& Elsevier]

Abstract: This driving simulator study investigated how mandatory and voluntary ISA might affect a driver''s overtaking decisions on rural roads, by presenting drivers with a variety of overtaking scenarios designed to evaluate both the frequency and safety of the manoeuvres. In half the overtaking scenarios, ISA was active and in the remainder ISA was switched off. A rural road was modelled with a number of 2+1 road sections, thus allowing drivers a protected overtaking opportunity. The results indicate that drivers became less inclined to initiate an overtaking manoeuvre when the mandatory ISA was active and this was particularly so when the overtaking opportunity was short. In addition to this, when ISA was activated drivers were more likely to have to abandon an overtaking, presumably due to running out of road. They also spent more time in the critical hatched area—a potentially unsafe behaviour. The quality of the overtaking manoeuvre was also affected when mandatory ISA was active, with drivers pulling out and cutting back in more sharply. In contrast, when driving with a voluntary ISA, overtaking behaviour remained mostly unchanged: drivers disengaged the function in approximately 70% of overtaking scenarios. The results of this study suggest that mandatory ISA could affect the safety of overtaking manoeuvres unless coupled with an adaptation period or other driver support functions that support safe overtaking. [Copyright &y& Elsevier]