Application of surrogate safety measures in higher levels of automated vehicles simulation studies: A review of the state of the practice.

OBJECTIVE: Surrogate safety measures (SSMs) are developed and applied as alternatives or complements of safety analyses mainly due to important road crash data availability and reliability limitations. Automated vehicles (AVs) have recently emerged as a prominent solution to mitigate transport externalities and increase road traffic safety. Due to the novelty of the technology and the lack of real-world data, traffic simulation combined with SSMs is the most common approach to quantify their impact. This study aims to provide an overview of the state of the practice and, more specifically, examine the applicability of applied SSMs on higher levels of AVs (HAVs). METHODS: The methodological approach consists of a comprehensive literature search, which aims to provide an overview of the applied SSMs, followed by a critical assessment of the findings. RESULTS: In total, 17 studies and 11 different SSMs were identified and reviewed. Findings suggest that available SSMs are suitable measures to appropriately estimate the relative safety performance of HAVs and indicate their potential implications due to their expected rule-based driving behavior. However, in some cases, it was noticed that they could not efficiently capture the technological capabilities of HAVs, e.g., shorter headways and faster reaction times, which may lead to false alarms. CONCLUSIONS: Despite the available evidence, there are still significant gaps and certain limitations, as no comparisons between different measures exist, or the validity of the applied measures could not be assessed based on historical road crash data. This work aims to help researchers and practitioners choose the most appropriate SSMs to evaluate HAVs' safety performance. Finally, several research gaps are identified, and recommendations for potential future research directions are presented.

Improved traffic safety at work zones through animation-based variable message signs.

Work zones are established to provide a safe environment for all road users and road workers. However, based on the statistics, they can be considered as crash prone zones due to changes in the road alignments and the posted speed limits. In this driving simulator study, we aimed at investigating the safety impacts of a newly proposed system composed of graphical and animation-based variable message signs (VMSs) in the state of Qatar. The proposed VMS condition was compared with a control condition that was designed following the Qatar Work Zone Traffic Management Guide. A total of seventy subjects were invited to participate in the experiment voluntarily. Study results showed that in the VMS condition, drivers reduced their traveling speeds in advanced compared to the control condition. Drivers' traveling speed in the VMS condition was significantly reduced by 6.3 and 11.1 km/h on the leftmost and the second leftmost lanes, respectively. Next, the results uncovered that the proposed system motivated drivers to initiate early lane changing maneuvers, i.e., 150 m earlier than the control condition. Finally, the VMS condition was effective in stimulating drivers to keep larger headways with a merging vehicle. In sum, the proposed VMS system outperformed the control condition in terms of speed reduction, early merging, and higher headways between the through and the merging vehicle.

Do detection-based warning strategies improve vehicle yielding behavior at uncontrolled midblock crosswalks?

Pedestrians being the most vulnerable road users account for a large proportion of injuries and fatalities from road traffic crashes. Pedestrians are involved in around one-third of the whole fatalities coming from the road traffic crashes in the state of Qatar. In areas with uncontrolled midblock crosswalks, it is very crucial to improve drivers' alertness and yielding behavior. The objective of this driving simulator study is to investigate the impact of pedestrian detection strategies and pavement markings on driving behavior at high-speed uncontrolled crosswalks. To this end, an untreated condition (i.e. Control) was compared with three treatment conditions. The three treated conditions included two detection strategies, i.e., advance variable message sign (VMS) and LED lights, and road markings with pedestrian encircled. Each condition was tested with a yield/stop controlled marked crosswalk for two situations, i.e. with vs. without a pedestrian present. The experiment was conducted using the driving simulator at Qatar University. In total, 67 volunteers possessing a valid Qatari driving license participated in the study. Different analyses were conducted on vehicle-pedestrian interactions, driving speed, variations in acceleration/deceleration and lateral position. The results showed that both the LED and VMS conditions were helpful in increasing yielding rates up to 98.4 % and reducing the vehicle-pedestrian conflicts significantly. Furthermore, both treatments were effective in motivating drivers to reduce vehicle speed in advance. Considering the findings of this study, we recommend LED and VMS conditions as potentially effective solutions to improve safety at yield/stop controlled crosswalks.

Estimation of safety performance functions for urban intersections using various functional forms of the negative binomial regression model and a generalized Poisson regression model.

Intersections are established dangerous entities of a highway system due to the challenging and unsafe roadway environment they are characterized for drivers and other road users. In efforts to improve safety, an enormous interest has been shown in developing statistical models for intersection crash prediction and explanation. The selection of an adequate form of the statistical model is of great importance for the accurate estimation of crash frequency and the correct identification of crash contributing factors. Using a six-year crash data, road infrastructure and geometric design data, and traffic flow data of urban intersections, we applied three different functional forms of negative binomial models (i.e., NB-1, NB-2, NB-P) and a generalized Poisson (GP) model to develop safety performance functions (SPF) by crash severity for signalized and unsignalized intersections. This paper presents the relationships found between the explanatory variables and the expected crash frequency. It reports the comparison of different models for total, injury & fatal, and property damage only crashes in order to obtain ones with the maximum estimation accuracy. The comparison of models was based on the goodness of fit and the prediction performance measures. The fitted models showed that the traffic flow and several variables related to road infrastructure and geometric design significantly influence the intersection crash frequency. Further, the goodness of fit and the prediction performance measures revealed that the NB-P model outperformed other models in most crash severity levels for signalized intersections. For the unsignalized intersections, the GP model was the best performing model. When only the NB models were compared, the functional form NB-P performed better than the traditional NB-1 and, more specifically, the NB-2 models. In conclusion, our findings suggest a potential improvement in the estimation accuracy of the SPFs for urban intersections by applying the NB-P and GP models.

Optical pavement treatments and their impact on speed and lateral position at transition zones: A driving simulator study.

Transition zones are a road section where posted speed drops from higher to lower limits. Due to the sudden changes in posted speed limits and road environment, drivers usually do not adapt to the posted speed limits and underestimate their traveling speed. Previous studies have highlighted that crash rates are usually higher in these sections. This study aims at improving the safety at transition zones by introducing perceptual measures that are tested using a driving simulator. The proposed measures are speed limit pavement markings with a gradual increase of brightness and/or size that were placed at transition zones in simulation scenarios replicating the real-world environment of the Doha Expressway in Qatar. These innovative measures aim to produce the impression of increased speed that could stimulate drivers to better adapt speed limits. The driving behavior of 81 drivers possessing a valid Qatari driving license was recorded with a driving simulator interfaced with STISIM Drive® 3. Results showed that pavement markings combining size and brightness manipulations were the most effective treatment, keeping drivers' traveling speed significantly below the traveling speed recorded in the untreated control condition. In this regard, the maximum mean speed reductions of 5.3 km/h and 4.6 km/h were observed for this treatment at the first transition (120 to 100 km/h) and second transition (100 to 80 km/h) zones, respectively. Regarding the variations in drivers' lateral position, the results showed that the proposed pavement markings did not negatively influence drivers' lateral control on the road as the maximum observed standard deviation of lateral position was around 0.065 m. This study shows that the proposed pavement markings are recommended for improving the speed adaptation of drivers in the transition zones.

Standard freeway merge designs support safer driver behaviour compared to taper designs: a driving simulator study.

Road geometric design standards provide various possibilities for merging freeways with a decreasing number of lanes. In this study, an alternative design (i.e. taper design) is investigated and compared with the standard design under three different heavy vehicle compositions to understand driving performance in relation to the flow of traffic. Taper design is not always the first choice in the road geometric design guidelines and the designer has to provide arguments for selecting this design. Taper design and its comparison with other alternatives are also not well explored in literature. In this study, a driving simulator was used to examine and compare the performance of these two designs under different heavy vehicle compositions. Qualitative results showed that the perceived safety was better for the standard design compared to the taper design. Mean speed, acceleration, standard deviation of acceleration/deceleration, and cumulative lane changes were chosen as behavioural parameters to compare these two designs using MANOVA and repeated measures ANOVA. Results revealed that drivers' discomfort in performing merging manoeuvres was greatest in case of a taper design and when the percentage of heavy vehicles was moderate (15%). Overall, the standard design was found to be more favourable. Practitioner summary: Driving behaviour at merging freeways with a decreasing number of lanes is underexplored. We analysed safety in driving behaviour considering heavy vehicles for taper and standard designs provided in Dutch guidelines using a driving simulator. The standard design was found to be safer and the presence of moderate heavy vehicles caused more disturbances in driving behaviour.

Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study.

The change interval, which includes the yellow and all-red times, plays a crucial role in the safety and operation of signalized intersections. During this interval, drivers not only need to decide to stop or go but also have to interact with drivers both in front and behind, trying to avoid conflicting decisions. Red light running and inconsistent stopping behavior may increase the risk for angular and rear-end crashes. This study aims to investigate the effect of different innovative countermeasures on red light running prevention and safe stopping behavior at signalized intersections. Five different conditions were tested inviting sixty-seven volunteers with a valid driving license. The conditions include a default traffic signal setting (control condition), flashing green signal setting (F-green), red LED ground lights integrated with a traffic signal (R-LED), yellow interval countdown variable message sign (C-VMS), and red light running detection camera warning gantry (RW-gantry). Drivers in each condition were exposed to two different situations based on the distance from the stop line. In the first situation, drivers were located in the indecision zone while in the second situation they were located in the likely stopping zone. A series of logistic regression analyses and linear mixed models were conducted to investigate the overall safety effects of the different countermeasures. The probability of red light running (RLR) was significantly reduced for R-LED in both analyses (i.e. in the total sample, and in the sample of crossed vehicles). Moreover, a clearly inconsistent stopping behavior was observed for the flashing green condition. Furthermore, a unit increase in speed (kph) at the onset of yellow interval significantly increases the probability of RLR by 5.3 %. The study showed that R-LED was the most effective solution for improving red light running prevention and encouraging a consistent stopping behavior at the intersection. In conclusion, the R-LED and the RW-gantry treatments are recommended as effective tools to improve safety at signalized intersections.

Drivers' estimation of their travelling speed: a study on an expressway and a local road.

Correctly estimating the travelling speed is essential for safety on the roads to ensure safer interactions with other drivers and to avoid violations of traffic regulations. Therefore, it is important to create understanding of the pattern of speed perception of drivers and the influencing factors. This study invited 40 drivers to participate in a field experiment measuring speed perception. The experiment was conducted using a test car equipped with high fidelity Global Positioning System (GPS). Data were collected for two settings (hidden versus revealed speedometer) and four requested speeds (50, 70, 80 and 100 km/h). An analysis of variance test was conducted along with separate t-tests and cumulative distributions for each speed independently. The results clearly show that there were significant differences in perceived speed between a hidden and revealed speedometer, between the first and second trial and among all the requested speeds. Participants drove significantly faster for all the requested speeds in the hidden speedometer setting. This shows that participants underestimated their speed. In case of complex situations, drivers may not be able to rely on speedometers and may cause a violation of law, unintentionally. Therefore, it is recommended to jurisdictions to consider these results while making decisions regarding speed management.

Impact of perceptual countermeasures on driving behavior at curves using driving simulator.

OBJECTIVE: The probability of crash occurrence on horizontal curves is 1.5 to 4 times higher than that on tangent sections. A majority of these crashes are associated with human errors. Therefore, human behavior in curves needs to be corrected. METHODOLOGY: In this study, 2 different road marking treatments, optical circles and herringbone patterns, were used to influence driver behavior while entering a curve on a 2-lane rural road section. A driving simulator was used to perform the experiment. The simulated road sections are replicas of 2 real road sections in Flanders. RESULTS: Both treatments were found to reduce speed before entering the curve. However, speed reduction was more gradual when optical circles were used. A herringbone pattern had more influence on lateral position than optical circles by forcing drivers to maintain a safe distance from opposing traffic in the adjacent lane. CONCLUSION: The study concluded that among other low-cost speed reduction methods, optical circles are effective tools to reduce speed and increase drivers' attention. Moreover, a herringbone pattern can be used to reduce crashes on curves, mainly for head-on crashes where the main problem is inappropriate lateral position.

Assessing the impacts of enriched information on crash prediction performance.

While high road safety performing countries base their effective strategies on reliable data, in developing countries the unavailability of essential information makes this task challenging. As a result, this drawback has led researchers and planners to face dilemmas of "doing nothing" or "doing ill", therefore restricting models to data availability, often limited to socio-economic and demographic variables. Taking this into account, this study aims to demonstrate the potential improvements in spatial crash prediction model performance by enhancing the explanatory variables and modelling casualties as a function of a more comprehensive dataset, especially with an appropriate exposure variable. This includes experimental work, where models based on available information from São Paulo, Brazil, and Flanders, the Dutch speaking area of Belgium, are developed and compared with each other. Prediction models are developed within the framework of Geographically Weighted Regression with the Poisson distribution of errors. Moreover, casualties and fatalities as the response variables in the models developed for Flanders and São Paulo, respectively, are divided into two sets based on the transport mode, called active (i.e., pedestrians and cyclists) and motorized transport (i.e., motorized vehicle occupants). In order to assess the impacts of the enriched information on model performance, casualties are firstly associated with all available variables for São Paulo and the corresponding ones for Flanders. In the next step, prediction models are developed only for Flanders considering all the available information in the Flemish dataset. Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in AICc and MSPE, respectively. Considering the practical aspects, results could help identify hotspots and relate most influential factors, suggesting sites and data, which should be prioritized in future local investigations. Besides minimizing costs with data collection, it could help policy makers to identify, implement and enforce appropriate countermeasures.

Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data.

OBJECTIVES: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g., collected by loop detectors). The main objective of this article is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways. METHODS: In this study, the potential prediction variables are confined to traffic-related characteristics. Given that the dependent variable (i.e., traffic safety condition) is dichotomous (i.e., "no-crash" or "crash"), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction toward Antwerp. RESULTS: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed, and standard deviation of speed at the upstream loop detector station and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately, and it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate. CONCLUSIONS: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems.

Integrated health impact assessment of travel behaviour: model exploration and application to a fuel price increase.

Transportation policy measures often aim to change travel behaviour towards more efficient transport. While these policy measures do not necessarily target health, these could have an indirect health effect. We evaluate the health impact of a policy resulting in an increase of car fuel prices by 20% on active travel, outdoor air pollution and risk of road traffic injury. An integrated modelling chain is proposed to evaluate the health impact of this policy measure. An activity-based transport model estimated movements of people, providing whereabouts and travelled kilometres. An emission- and dispersion model provided air quality levels (elemental carbon) and a road safety model provided the number of fatal and non-fatal traffic victims. We used kilometres travelled while walking or cycling to estimate the time in active travel. Differences in health effects between the current and fuel price scenario were expressed in Disability Adjusted Life Years (DALY). A 20% fuel price increase leads to an overall gain of 1650 (1010-2330) DALY. Prevented deaths lead to a total of 1450 (890-2040) Years Life Gained (YLG), with better air quality accounting for 530 (180-880) YLG, fewer road traffic injuries for 750 (590-910) YLG and active travel for 170 (120-250) YLG. Concerning morbidity, mostly road safety led to 200 (120-290) fewer Years Lived with Disability (YLD), while air quality improvement only had a minor effect on cardiovascular hospital admissions. Air quality improvement and increased active travel mainly had an impact at older age, while traffic safety mainly affected younger and middle-aged people. This modelling approach illustrates the feasibility of a comprehensive health impact assessment of changes in travel behaviour. Our results suggest that more is needed than a policy rising car fuel prices by 20% to achieve substantial health gains. While the activity-based model gives an answer on what the effect of a proposed policy is, the focus on health may make policy integration more tangible. The model can therefore add to identifying win-win situations for both transport and health.

Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling.

Travel demand management (TDM) consists of a variety of policy measures that affect the transportation system's effectiveness by changing travel behavior. The primary objective to implement such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to evaluate the traffic safety impact of conducting a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) in Flanders, Belgium. Since TDM strategies are usually conducted at an aggregate level, crash prediction models (CPMs) should also be developed at a geographically aggregated level. Therefore zonal crash prediction models (ZCPMs) are considered to present the association between observed crashes in each zone and a set of predictor variables. To this end, an activity-based transportation model framework is applied to produce exposure metrics which will be used in prediction models. This allows us to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models unlike traditional models in which the impact of TDM strategies are assumed. The crash data used in this study consist of fatal and injury crashes observed between 2004 and 2007. The network and socio-demographic variables are also collected from other sources. In this study, different ZCPMs are developed to predict the number of injury crashes (NOCs) (disaggregated by different severity levels and crash types) for both the null and the fuel-cost increase scenario. The results show a considerable traffic safety benefit of conducting the fuel-cost increase scenario apart from its impact on the reduction of the total vehicle kilometers traveled (VKT). A 20% increase in fuel price is predicted to reduce the annual VKT by 5.02 billion (11.57% of the total annual VKT in Flanders), which causes the total NOCs to decline by 2.83%.

Translating road safety into health outcomes using a quantitative impact assessment model.

INTRODUCTION: The majority of traffic safety policies are limited to preventing mortality. However, non-fatal injuries also impose a significant risk of adverse health. Therefore, both mortality and morbidity outcomes should be included in the evaluation of traffic safety policies. The authors propose a method to evaluate different policy options taking into account both fatalities and serious injuries. METHODS: A health impact model was developed and aligned with a transport and road safety model, calculating the health impact of fatalities and seriously injured traffic victims for two transport scenarios in Flanders and Brussels (Belgium): a base scenario and a fuel price increase of 20% as an alternative. Victim counts were expressed as disability adjusted life years, using a combination of police and medical data. Seriously injured victims were assigned an injury, using injury distributions derived from hospital data, to estimate the resulting health impact from each crash. Health impact of fatalities was taken as the remaining life expectancy at the moment of the fatal crash. RESULTS: The fuel price scenario resulted in a decrease of health impact due to fatalities of 5.53%--5.85% and 3.37%--3.88% for severe injuries. This decrease was however not equal among all road users. CONCLUSIONS: With this method, the impact of traffic polices can be evaluated on both mortality and morbidity, while taking into account the variability of different injuries following a road crash. This model however still underestimates the impact due to non-fatal injuries.