When do drivers abort an overtaking maneuver 5 on two-lane rural roads

Overtaking on two-lane roads is a complex driving maneuver. Drivers who desire to overtake a lead vehicle need to evaluate the available gaps in the opposite direction and accept a sufficient gap to successfully complete the overtaking maneuver. However, often drivers realize that the gap they accepted is shorter than what they actually need to complete the maneuver safely, and therefore decide to abort the overtaking maneuver. Previous research studies focused mainly on analyzing drivers’ decisions to accept or reject available gaps in the opposite direction, and their overtaking performance. Very limited research investigated the conditions under which drivers decide to abort or complete an initiated overtaking maneuver. Increased frequency of aborted overtaking maneuvers has negative implications on safety and the operation of two-lane roads. One hundred drivers from both gender and different age groups participated in a driving simulator experiment study. Driving scenarios with different geometric and traffic characteristics were developed. Detailed trajectory data of 670 overtaking maneuvers was collected, in which 554 were successfully completed and 116 were aborted. A logistic regression model was developed which predicts the probability that a driver aborts an overtaking maneuver. The results show that the probability to abort an overtaking maneuver is significantly affected by the size of the accepted gap in the opposite direction, the desired driving speed of the driver, the speed and type of the front lead vehicle, the cumulative 20 waiting time to find an appropriate gap on the opposite direction, the road curvature, and 21 drivers’ age and gender.Transport & PlanningCivil Engineering and Geoscience

State of Art on Infrastructure for Automated Vehicles: Research report summarizing the scientific knowledge, research projects, test sites, initiatives, and knowledge gaps regarding infrastructure for automated vehicles.

This state-of-the-art will review the scientific literature, completed and ongoing projects, testing facilities, and initiatives on the topic of automated vehicles and infrastructure. The purpose is to present the results of studies directly on this topic and studies and projects that are not directly related to this topic but which provide relevant information on the implications on infrastructure. The search was based on web search, scientific journals, conference proceedings and symposia, as well as contact with key figure persons in this field. This led us to identify the gaps in literature and avenues for future research.Transport and Plannin

A multi-faceted approach for assessing the safety of Israeli Arab children in their travel to and from school

Road crashes are considered as one of the main threats to human life around the world. Children pedestrians are most at risk to be seriously injured in road crashes, in particular, those from economically disadvantaged communities. Various factors contribute to their high involvement in road crashes. Some of these factors are related to the characteristics of the children and their parents, while others are related to the physical urban road environment. The share of Arab children in Israel (<15 years old) in pedestrians’ fatalities far exceeds their fraction in their age group. Therefore, effective and immediate measures to increase their safety are required. The present research, a part of a larger ongoing project, proposes a multifaceted approach and applies it to a real case study in the Arab local council of Jadeidi-Makr in Israel. The proposed approach is based on: (1) data collected by means of questionnaires posted to the children and their parents concerning the travel characteristics of the children to school; (2) objective data on the children walking routes collected by GPS-enabled watches; and (3) road safety auditing of the school environment and the main routes to the school. The results of this study found that children’s characteristics, their travel behavioral patterns, their parents' safety perceptions, and the road environment are all significant factors when considering children’s safety as pedestrians. Thus, improvements in the infrastructure, children and parents’ safety awareness, and police enforcement are essential to increase Arab children’s safety. The responsible authorities, decision and policy makers are called to join forces and take immediate actions to realize the suggested improvements in reality

Passing behavior on two-lane roads in a real and in a simulated environment

Passing maneuvers allow faster drivers to continue driving at their own desired speeds without being delayed behind an impeding vehicle. On two lane rural roads, this requires from the passing driver to occupy the opposing lane. This has tremendous implications on safety and operation of two-lane roads. In the literature, several studies investigated the passing behavior of drivers, and some have used driving simulators to analyze drivers’ behavior during following and passing maneuvers. However, the validity of simulators has not been ensured, as their results have rarely been compared with real data. The objective of this study is to compare drivers’ passing behavior as observed in the field with 49 passing behavior in a driving simulator. This may improve both methods to validate the use of simulation instead of observations. For this purpose, data on passing performance and passing gap acceptance decisions is required. This paper carried out a comparative analysis of the most significant variables related to passing behavior. The results showed similarities between passing time and passing distance of completed maneuvers (during the occupation of the opposing lane). However, drivers passed faster in the driving simulator, keeping higher clearances. Gap acceptance decisions were also found to be similar, as the distributions of both accepted and rejected gaps were similar, although critical gaps were found to be lower in the driving simulator. This might be explained by the absence of objective risks. Consequently, the applicability of driving simulation seems reasonable, although some improvements are still possible, in order to account for sight distance limitations, replicate age and gender distributions, and reproduce better the opposing 60 traffic flow.Transport & PlanningCivil Engineering and Geoscience

Speed development at freeway curves based on high frequency floating car data

Road designers need to have insights where deceleration and acceleration are expected related to the position of the curve, and in in which amount so that drivers are able to safely decelerate and accelerate respectively into and out of a freeway curve. For this, empirical speed data is needed. Therefore, Floating Car Data in 153 curves in The Netherlands were collected at a resolution of 1 Hz and were filtered on free-flow periods, to analyse over 800 thousand unique continuous free-flow speed observations on these curves. Regression models were developed to predict speed development, including deceleration and acceleration behaviour upon entering and exiting freeway curves. The models rely on easy to generate geometric design variables, including the start and end position of the horizontal curve, the horizontal radius and the number of lanes. Using these variables, the designer can predict the speed development based on the 85th percentile of speed and acceleration, relative to the position of the curve. The regression models reveal strong goodness-of-fit of the predicted 85th percentiles of speed in a curve, showing acceleration and deceleration inside the curve, and higher predicted 85th percentile speeds than the design speeds. The models also show satisfying results in speed development prediction in sets of consecutive curves with different characteristics, as well as deceleration when entering a first curve and acceleration when exiting a last curve. These insights are valuable in evaluating road design in relation to traffic safety based on its predicted use.Transport and Plannin

Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles

Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.Transport and Plannin

Safety analysis of passing maneuvers using extreme value theory

The increased availability of detailed trajectory data sets from naturalistic, observational and simulation-based studies are a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and the various driving maneuvers. Despite the well-recognized research findings on both crash frequency estimation and traffic conflicts analysis carried out over the last decades, only recently researchers have started to study and model the link between the two. This link is typically made by statistical association between aggregated conflicts and crashes, which still relies on crash data and ignores heterogeneity in the estimation procedure. More recently, an Extreme Value (EV) approach has been used to link the probability of crash occurrence to the frequency of conflicts estimated from observed variability of crash proximity, using a probabilistic framework and without using crash records. In this on-going study the Generalized Extreme Value (GEV) distribution and the Generalized Pareto Distribution (GPD)-based estimation, in the peak over threshold approach, are tested and compared as EV methods using the minimum time-to-collision with the opposing vehicle during passing maneuvers. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment was used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary base. Several two-lane rural highway layouts and traffic conditions were also considered in the design of the simulator environment. Raw data was collected at a resolution of 0.1 s and included the longitudinal and lateral position, speed and acceleration of all vehicles in the scenario. From this raw data, the minimum time-to-collision with the opposing vehicle at the end of the passing, maneuver was calculated. GEV distributions based on the Block Maxima approach and GPD distributions under the POT approach were tested for the estimation of head-on collision probabilities in passing maneuvers with different results. While the GEV approach achieved satisfactory fitting results, the tested POT underestimated the expected number of head-on collisions. Finally, the estimated GEV distributions were validated using a second set of data extracted from an additional driving simulator experiment. The results indicate that this is a promising approach for safety evaluation. On-going work of the authors will attempt to generalize this method to other safety measures related to passing maneuvers, test it for the detailed analysis of the effect of demographic factors on passing maneuvers’ crash probability and for its usefulness in a traffic simulation environmentTransport and PlanningCivil Engineering and Geoscience

Passing behavior on two-lane roads in real and simulated environments

Passing maneuvers allow faster drivers to continue driving at their desired speeds without being delayed behind impeding vehicles. On two-lane rural roads, such maneuvers require the passing driver to occupy the opposing lane; this condition has tremendous implications for the safety and operation of two-lane roads. Several studies have investigated the passing behavior of drivers, and some studies have used driving simulators to analyze drivers' behavior during following and passing maneuvers. However, the validity of simulators has not been ensured, because their results have rarely been compared with real data. The objective of this study was to compare drivers' passing behavior as observed in the field with passing behavior in a driving simulator. For this purpose, data on passing performance and passing gap acceptance decisions were required. The study carried out a comparative analysis of the most significant variables related to passing behavior. The results showed similarities between passing time and passing distance of completed maneuvers (during the occupation of the opposing lane). However, drivers passed faster in the driving simulator and maintained greater clearances. Gap acceptance decisions were found to be similar, as the distributions of accepted and rejected gaps were similar, although critical gaps were found to be lower in the driving simulator. This finding might be explained by the absence of objective risks. The applicability of driving simulation seems reasonable, although some improvements are still possible, to account for sight distance limitations, replicate age and gender distributions, and reproduce the opposing traffic flow.Transport and Plannin

Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pretrain the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the art results, while the training time can be substantially shortened.Transport and Plannin

Next steps in describing possible effects of automated driving on traffic flow

Although at times traffic flow can be complicated to understand and predict, our current understanding is nevertheless pretty good and allows predictions of current and future situations to be made. However, this is about to change with the emergence of (partially) automated vehicles1on roads. This emergence will change the way people drive (speed and acceleration choices, following behaviour, lane change behaviour) and will change the dynamics present in traffic flow and with that the ability of traffic to move effectively. The first generation of partially automated vehicles is already on the road in the form of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA), as the so-called SAE level 1 vehicle automation (SAE 2014). Practical and technical tests are already widespread on higher levels of automation, with their mass-produced introduction expected in advance of 2020 (Merat et al. 2014). According to Shladover (2016) fully automated (SAE level 5) vehicles will not be here until 2075 at the earliest. At this point, no statement has been made on the point if these changes are going to be positive or negative. The reason for this is that there are too many uncertainties in relation to the effects that automated vehicles will have on traffic. Many enthusiasts have made incredible claims that automated vehicles will solve many of our traffic problems and can have an overwhelming positive effect on mobility, traffic flow and safety. However, these claims are rarely substantiated and are often based on questionable assumptions regarding the future. Furthermore, even if these claims are correct, they are applicable to a situation that lies many decades in the future, i.e. 100% penetration rate, and do not consider the route leading towards this future (the so called transition period). The main consensus within the traffic and mobility community is that traffic flow will initially worsen with the introduction of automated vehicles (Le Vine, Zolfaghari, and Polak 2015, SBD and HERE, 2016). This has a lot to do with conservative settings of automated vehicles, unfamiliarity with automated vehicles from both drivers and other road users, initial teething problems with the technology and a limited ability and penetration for cooperation between vehicles (Shladover, Su, and Lu 2012). Furthermore, there are predictions that traffic volume may eventually increase due to a steep uptake in automated vehicles (Wadud, MacKenzie, and Leiby 2016). However, again there is much uncertainty in relation to these statements, even if they are well substantiated Next steps in describing possible effects of automated driving on traffic flow. Available from: https://www.researchgate.net/publication/312377387_Next_steps_in_describing_possible_effects_of_automated_driving_on_traffic_flow?channel=doi&amp;linkId=587c8ba108ae9a860fed444b&amp;showFulltext=true [accessed Jul 18 2018].Transport and Plannin