Effects of red light running camera systems installation and then deactivation on intersection safety

IntroductionThe primary objective of this paper is to evaluate the safety impacts of red-light running camera (RLC) system installation and then deactivation at 48 intersections in Houston, Texas. The second objective is to evaluate the spillover effect at nearby non-treated intersections in Houston after the deactivation.MethodsTo accomplish study objectives, an Empirical Bayes (EB) before-after analysis was used.ResultsThe results indicate statistically significant collision reductions on all red-light running (RLR) crash types (37 percent) as well as right-angle RLR crashes (47 percent) at the treated intersections after RLC activation. By way of comparison, the RLC deactivation analysis indicated that crashes increased by 20 percent for all RLR crash types and by 23 percent in right-angle RLR crashes at the formerly treated intersections. After deactivation, all severity RLR crashes increased more than expected at nearby non-treated intersections, which indicates the possibility of an adverse spillover effect. However, fatal/injury crashes associated with rear-end decreased after deactivation at both formerly treated and non-treated intersections, although those rear-end crashes account for smaller proportions when compared to all crash types/right-angle crashes.Practical applicationsOverall, removing RLC treatments results in a negative reaction to the safety benefits that the treatment provides when it is in place and actively working and to the nearby intersections where the treatment has not been implemented. This study helps define the effects that RLCs have on safety at signalized intersections after installation and deactivation.     

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County,Michigan

IntroductionDespite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented.MethodThis study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity.ResultsEstimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents.     

Crash risk of ESC-fitted passenger cars

Objective: We examined both fatal and injury at-fault crashes of a population of passenger cars fitted with electronic stability control (ESC). Crash rates were calculated in relation to both registration years and mileage. Crash rates were also calculated for a non-ESC car population and crash rate ratios were calculated to compare the crash risk between ESC-fitted and non-ESC-fitted passenger cars.

Methods: Passenger car models with and without ESC were identified (ESC-equipped cars: 3,352,813 registration years; non-ESC-equipped: 5,839,946 registration years) and their vehicle information for the period 2009–2013, including mileage (ESC-equipped vehicles: 89.3 billion kilometers; non-ESC-equipped: 72.4 billion kilometers), was drawn from the national Vehicular and Driver Data Register.

The registry of Finnish road accident investigation teams was accessed and all fatal at-fault crashes among the cars in the study populations (ESC 97; non-ESC 377) for the period 2009–2013 were analyzed. The motor insurance database includes at-fault crashes leading to injuries and was utilized for analyses (ESC: N?=?8,827, non-ESC: N?=?21,437).

Crash rates and crash rate ratios were calculated to evaluate crash risk of both ESC-equipped and non-ESC-equipped passenger cars. Poisson regression was used to model crash involvement rate ratios both per registration year and per mileage for vehicles with ESC and without ESC, controlling for age and gender of the vehicle owner and vehicle mass.

Results: Passenger cars fitted with ESC showed lower crash rates than non-ESC-equipped cars in all crash types studied. In general, the difference in crash rates between ESC-equipped and non-ESC-equipped vehicles was greater when the crashes were compared to the mileage rather than registration years. The mileage-proportional crash rate of ESC-equipped cars was 64% (95% confidence interval, 61%; 67%) lower in run-off-road crashes resulting in injury and as much as 82% (65%; 91%) lower in fatal run-off-road crashes when suicides and disease attacks were not taken into account.

Conclusions: Our results show that modern passenger cars provide a significant crash risk reduction, which depends on both ESC and passive safety features introduced. Results also show that exposure evaluation in terms of registration years (or vehicle population) instead of true mileage can provide an overly pessimistic view of the crash risk.     

Rage against the machine? Google's self-driving cars versus human drivers

IntroductionAutomated driving represents both challenges and opportunities in highway safety. Google has been developing self-driving cars and testing them under employee supervision on public roads since 2009. These vehicles have been involved in several crashes, and it is of interest how this testing program compares to human drivers in terms of safety.MethodsGoogle car crashes were coded by type and severity based on narratives released by Google. Crash rates per million vehicle miles traveled (VMT) were computed for crashes deemed severe enough to be reportable to police. These were compared with police-reported crash rates for human drivers. Crash types also were compared.ResultsGoogle cars had a much lower rate of police-reportable crashes per million VMT than human drivers in Mountain View, Calif., during 2009–2015 (2.19 vs 6.06), but the difference was not statistically significant. The most common type of collision involving Google cars was when they got rear-ended by another (human-driven) vehicle. Google cars shared responsibility for only one crash.ConclusionsThese results suggest Google self-driving cars, while a test program, are safer than conventional human-driven passenger vehicles; however, currently there is insufficient information to fully examine the extent to which disengagements affected these results.Practical applicationResults suggest that highly-automated vehicles can perform more safely than human drivers in certain conditions, but will continue to be involved in crashes with conventionally-driven vehicles.     

Safety effectiveness and crash cost benefit of red light cameras in Missouri

Objective: Red light cameras (RLCs) have generated heated discussions over issues of safety effectiveness, revenue generation, and procedural due process. This study focuses on the safety evaluation of RLCs in Missouri, including the economic valuation of safety benefits. The publication of the national Highway Safety Manual (HSM; American Association of State Highway and Transportation Officials) in 2010 produced statistical safety models for intersections and spurred the calibration of these models to local conditions.

Methods: This study adds to existing knowledge by applying the latest statistical methodology presented in the HSM and more current data. Driver behavior constantly changes due in part to driving conditions and the use of technology. The safety and economic benefit evaluation was performed using the empirical Bayes method, which accounts for regression to the mean bias. For the economic benefit evaluation, the KABCO crash severity scale and crash cost estimates were used. A total of 24 4-leg urban intersections were randomly selected from a master list of RLCs in Missouri from 2006 to 2011. Additionally, 35 comparable nontreated intersections were selected for the analysis.

Results and Conclusions: The implementation of RLCs reduced overall angle crashes by 11.6%, whereas rear-end crashes increased by 16.5%. The net economic crash cost benefit of the implementation of RLCs was $35,269 per site per year in 2001 dollars (approximately $47,000 in 2015 dollars). Thus, RLCs produced a sizable net positive safety benefit that is consistent with previous statistical studies.     

Investigation of crashes at pedestrian hybrid beacons: Results of a large-scale study in Arizona

Introduction: The pedestrian hybrid beacon (PHB) is a traffic control device used at pedestrian crossings. A recent Arizona Department of Transportation research effort investigated changes in crashes for different severity levels and crash types (e.g., rear-end crashes) due to the PHB presence, as well as for crashes involving pedestrians and bicycles. Method: Two types of methodologies were used to evaluate the safety of PHBs: (a) an Empirical Bayes (EB) before-after study, and (b) a long-term cross-sectional observational study. For the EB before-after evaluation, the research team considered three reference groups: unsignalized intersections, signalized intersections, and both unsignalized and signalized intersections combined. Results: For the signalized and combined unsignalized and signalized intersection groups, all crash types considered showed statistically significant reductions in crashes (e.g., total crashes, fatal and injury crashes, rear-end crashes, fatal and injury rear-end crashes, angle crashes, fatal and injury angle crashes, pedestrian-related crashes, and fatal and injury pedestrian-related crashes). A cross-sectional study was conducted with a larger number of PHBs (186) to identify relationships between roadway characteristics and crashes at PHBs, especially with respect to the distance to an adjacent traffic control signal. The distance to an adjacent traffic signal was found to be significant only at the α = 0.1 level, and only for rear-end and fatal and injury rear-end crashes. Conclusions: This analysis represents the largest known study to date on the safety impacts of PHBs, along with a focus on how crossing and geometric characteristics affect crash patterns. The study showed the safety benefits of PHBs for both pedestrians and vehicles. Practical Applications: The findings from this study clearly support the installation of PHBs at midblock or intersection crossings, as well as at crossings on higher-speed roads.     

Multi-agent traffic simulations to estimate the impact of automated technologies on safety

Abstract

Objective: The objective of this article was to develop a multi-agent traffic simulation methodology to estimate the potential road safety improvements of automated vehicle technologies.

Methods: We developed a computer program that merges road infrastructure data with a large number of vehicles, drivers, and pedestrians. Human errors are induced by modeling inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation. The program was applied to simulate traffic in a prescribed area in Tsukuba city. First, a 100% manual driving scenario was set to simulate traffic for a total preset vehicle travel distance. The crashes from this simulation were compared with real-world crash data from the prescribed area from 2012 to 2017. Thereafter, 4 additional scenarios of increasing levels of automation penetration (including combinations of automated emergency braking [AEB], lane departure warning [LDW], and SAE Level 4 functions) were implemented to estimate their impact on safety.

Results: Under manual driving, the system simulated a total of 859 crashes including single-car lane departure, car-to-car, and car-to-pedestrian crashes. These crashes tended to occur in locations similar to real-world crashes. The number of crashes predicted decreased to 156 cases with increasing level of automation. All of the technologies considered contributed to the decrease in crashes. Crash reductions attributable to AEB and LDW in the simulations were comparable to those reported in recent field studies. For the highest levels of automation, no assessment data were available and hence the results should be carefully treated. Further, in modeling automated functions, potentially negative aspects such as sensing failure or human overreliance were not incorporated.

Conclusions: We developed a multi-agent traffic simulation methodology to estimate the effect of different automated vehicle technologies on safety. The crash locations resulting from simulations of manual driving within a limited area in Japan were preliminary assessed by comparison with real-world crash data collected in the same area. Increasing penetration levels of AEB and LDW led to a large reduction in both the frequency and severity of rear-end crashes, followed by car-to-car head-on crashes and single-vehicle lane departure crashes. Preliminary estimations of the potential safety improvements that may be achieved with highly automated driving technologies were also obtained.     

Investigating the different characteristics of weekday and weekend crashes

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes.MethodWeekend crashes were defined as crashes occurring between Friday 9 p.m. and Sunday 9 p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed.ResultsIt was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety.Impact on IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.     

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.

Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.

Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.

Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.     

An empirical bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne

Objective: Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures.

Method: A before–after crash study was conducted using the empirical Bayes (EB) method to provide more accurate crash impact estimates by accounting for wider crash trends and regression to the mean effects. Before–after crash data for 29 intersections with tram signal priority and 23 arterials with tram lane priority in Melbourne, Australia, were analyzed to evaluate the road safety impact of tram priority.

Results: The EB before–after analysis results indicated a statistically significant adjusted crash reduction rate of 16.4% after implementation of tram priority measures. Signal priority measures were found to reduce crashes by 13.9% and lane priority by 19.4%. A disaggregate level simple before–after analysis indicated reductions in total and serious crashes as well as vehicle-, pedestrian-, and motorcycle-involved crashes. In addition, reductions in on-path crashes, pedestrian-involved crashes, and collisions among vehicles moving in the same and opposite directions and all other specific crash types were found after tram priority implementation.

Conclusions: Results suggest that streetcar/tram priority measures result in safety benefits for all road users, including vehicles, pedestrians, and cyclists. Policy implications and areas for future research are discussed.     

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.     

Effect of pavement condition and geometrics at signalised intersections on casualty crashes

IntroductionThis study investigated the effects of pavement surface condition and other control factors on casualty crashes at signalized intersections. It involved conducting a before and after study for road surface condition and situational factors. It also included assessing the effects of geometric characteristics on safety performance of signalized intersections post resurfacing to control for the effect of pavement surface condition. Pavement surface condition included roughness, rutting, and skid resistance. The control factors included traffic volume, light and surface moisture condition, and speed limit. The geometric characteristics included approach width, number of lanes, intersection depth, presence of median, presence of shared lane, and presence of bus stop.MethodTo account for the repeated observations of the effect of light and surface moisture conditions in four occasions (day-dry, day-wet, night-dry and night-wet) Generalized Estimating Equation (GEE) with Negative Binomial (NB) and log link function was applied. For each signalized intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of three and maximum of five years before and after treatment years.ResultsThe results show that before treatment, light condition, road surface moisture condition, and skid resistance interaction with traffic volume are the significant contributors to crash occurrence. For after treatment; light condition, road surface moisture condition, their interaction product, and roughness interaction with light condition, surface moisture condition, and traffic volume are the significant contributors. The geometric variables that were found to have significant effects on crash frequency post resurfacing were approach width interactions with presence of shared lane, bus stop, or median.ConclusionsThe findings confirm that resurfacing is significant in reducing crash frequency and severity levels.Practical Applications: The study findings would help for better understanding of how geometric characteristics can be improved to reduce crash occurrence.     

Effects of lane departure warning on police-reported crash rates

ObjectiveTo evaluate the effects of lane departure warning (LDW) on single-vehicle, sideswipe, and head-on crashes.MethodPolice-reported data for the relevant crash types were obtained from 25 U.S. states for the years 2009–2015. Observed counts of crashes with fatalities, injuries, and of all severities for vehicles with LDW were compared with expected counts based on crash involvement rates for the same passenger vehicles without LDW, with exposure by vehicle series, model year, and lighting system standardized between groups. For relevant crashes of all severities and those with injuries, Poisson regression was used to estimate the benefits of LDW while also controlling for demographic variables; fatal crashes were too infrequent to be modeled.ResultsWithout accounting for driver demographics, vehicles with LDW had significantly lower involvement rates in crashes of all severities (18%), in those with injuries (24%), and in those with fatalities (86%). Adding controls for driver demographics in the Poisson regression reduced the estimated benefit of LDW only modestly in crashes of all severities (11%, p < 0.05) and in crashes with injuries (21%, p < 0.07).ConclusionsLane departure warning is preventing the crash types it is designed to address, even after controlling for driver demographics. Results suggest that thousands of lives each year could be saved if every passenger vehicle in the United States were equipped with a lane departure warning system that performed like the study systems.Practical applicationsPurchase of LDW should be encouraged, and, because drivers do not always keep the systems turned on, future efforts should focus on designing systems to encourage greater use and educating consumers about the benefits of using the systems.     

Investigating the influence on safety of retrofitting Italian motorways with barriers meeting a new EU standard

Objective: A new European Union (EU) regulation for safety barriers, which is based on performance, has encouraged road agencies to perform an upgrade of old barriers, with the expectation that there will be safety benefits at the retrofitted sites. The new class of barriers was designed and installed in compliance with the 1998 (European Norm) EN 1317 standards for road restraint systems, which lays down common requirements for the testing and certification of road restraint systems in all countries of the European Committee for Standardization (CEN). Both the older and new barriers are made of steel and are installed in such a way as to avoid vehicle intrusion, but the older ones are thought to be only effective at low speeds and large angles of impact. The new standard seeks to remedy this by providing better protection at higher speeds. This article seeks to quantify the effect on the frequency of fatal and injury crashes of retrofitting motorways with barriers meeting the new standards.

Methods: The estimation of the crash modification was carried out by performing an empirical Bayes before–after analysis based on data from the A18 Messina–Catania motorway in Italy. The methodology has the great advantage to account for the regression to the mean effects. Besides, to account for time trend effects and dispersion of crash data, a modified calibration methodology of safety performance was used.

Results: This study, based on data collected on 76 km of motorway in the period 2000–2012, derived Crash Modification Factor point estimates that indicate reductions of 72% for run-off-road fatal and injury crashes and 38% in total fatal and injury crashes that could be expected by upgrading an old safety barrier by complying with new EU 1317 standards. The estimated benefit-cost ratio of 5.57 for total crashes indicates that the treatment is cost effective.

Conclusions: The magnitude of this benefit indicates that the retrofits are cost-effective even for total crashes and should continue in any European country inasmuch as the estimated Crash Modification Factors are based on treatment sites that are reasonably representative of all European motorways.     

Comparison of Expected Crash and Injury Reduction from Production Forward Collision and Lane Departure Warning Systems

Objectives: The U.S. New Car Assessment Program (NCAP) now tests for forward collision warning (FCW) and lane departure warning (LDW). The design of these warnings differs greatly between vehicles and can result in different real-world field performance in preventing or mitigating the effects of collisions. The objective of this study was to compare the expected number of crashes and injured drivers that could be prevented if all vehicles in the fleet were equipped with the FCW and LDW systems tested under the U.S. NCAP.

Methods: To predict the potential crashes and serious injury that could be prevented, our approach was to computationally model the U.S. crash population. The models simulated all rear-end and single-vehicle road departure collisions that occurred in a nationally representative crash database (NASS-CDS). A sample of 478 single-vehicle crashes from NASS-CDS 2012 was the basis for 24,822 simulations for LDW. A sample of 1,042 rear-end collisions from NASS-CDS years 1997–2013 was the basis for 7,616 simulations for FCW. For each crash, 2 simulations were performed: (1) without the system present and (2) with the system present. Models of each production safety system were based on 54 model year 2010–2014 vehicles that were evaluated under the NCAP confirmation procedure for LDW and/or FCW. NCAP performed 40 LDW and 45 FCW tests of these vehicles.

Results: The design of the FCW systems had a dramatic impact on their potential to prevent crashes and injuries. Between 0 and 67% of crashes and 2 and 69% of moderately to fatally injured drivers in rear-end impacts could have been prevented if all vehicles were equipped with the FCW systems. Earlier warning times resulted in increased benefits. The largest effect on benefits, however, was the lower operating speed threshold of the systems. Systems that only operated at speeds above 20 mph were less than half as effective as those that operated above 5 mph with similar warning times. The production LDW systems could have prevented between 11 and 23% of drift-out-of-lane crashes and 13 and 22% of seriously to fatally injured drivers. A majority of the tested LDW systems delivered warnings near the point when the vehicle first touched the lane line, leading to similar benefits. Minimum operating speed also greatly affected LDW effectiveness.

Conclusions: The results of this study show that the expected field performance of FCW and LDW systems are highly dependent on the design and system limitations. Systems that delivered warnings earlier and operated at lower speeds may prevent far more crashes and injuries than systems that warn late and operate only at high speeds. These results suggest that future FCW and LDW evaluation should prioritize early warnings and full-speed range operation. A limitation of this study is that additional crash avoidance features that may also mitigate collisions—for example, brake assist, automated braking, or lane-keeping assistance—were not evaluated during the NCAP tests or in our benefits models. The potential additional mitigating effects of these systems were not quantified in this study.     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

Crash and injury prevention estimates for intersection driver assistance systems in left turn across path/opposite direction crashes in the United States

Abstract

Objective: The objective of this research study was to estimate the number of left turn across path/opposite direction (LTAP/OD) crashes and injuries that could be prevented in the United States if vehicles were equipped with an intersection advanced driver assistance system (I-ADAS).

Methods: This study reconstructed 501 vehicle-to-vehicle LTAP/OD crashes in the United States that were investigated in the NHTSA National Motor Vehicle Crash Causation Survey (NMVCCS). The performance of 30 different I-ADAS system variations was evaluated for each crash. These variations were the combinations of 5 time-to-collision (TTC) activation thresholds, 3 latency times, and 2 different response types (automated braking and driver warning). In addition, 2 sightline assumptions were modeled for each crash: One where the turning vehicle was visible long before the intersection and one where the turning vehicle was only visible within the intersection. For resimulated crashes that were not avoided by I-ADAS, a new crash delta-V was computed for each vehicle. The probability of Abbreviated Injury Scale 2 or higher injury in any body region (Maximum Abbreviated Injury Scale [MAIS] 2+F) to each front-row occupant was computed.

Results: Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18–84% of all LTAP/OD crashes. Only 0–32% of all LTAP/OD crashes could have been avoided using an I-ADAS system that only warns the driver. An I-ADAS system that applies emergency braking could prevent 47–93% of front-row occupants from receiving MAIS 2?+?F injuries. A system that warns the driver in LTAP/OD crashes was able to prevent 0–37% of front-row occupants from receiving MAIS 2?+?F injuries. The effectiveness of I-ADAS in reducing crashes and number of injured persons was higher when both vehicles were equipped with I-ADAS.

Conclusions: This study presents the simulated effectiveness of a hypothetical intersection active safety system on real crashes that occurred in the United States. This work shows that there is a strong potential to reduce crashes and injuries in the United States.     

The Real-World Safety Potential of Connected Vehicle Technology

Objective: This article estimates the safety potential of a current commercially available connected vehicle technology in real-world crashes.

Method: Data from the Centre for Automotive Safety Research's at-scene in-depth crash investigations in South Australia were used to simulate the circumstances of real-world crashes. A total of 89 crashes were selected for inclusion in the study. The crashes were selected as representative of the most prevalent crash types for injury or fatal crashes and had potential to be mitigated by connected vehicle technology. The trajectory, speeds, braking, and impact configuration of the selected in-depth cases were replicated in a software package and converted to a file format allowing “replay” of the scenario in real time as input to 2 Cohda Wireless MK2 onboard units. The Cohda Wireless onboard units are a mature connected vehicle technology that has been used in both the German simTD field trial and the U.S. Department of Transport's Safety Pilot project and have been tuned for low false alarm rates when used in the real world. The crash replay was achieved by replacing each of the onboard unit Global Positioning System (GPS) inputs with the simulated data of each of the involved vehicles. The time at which the Cohda Wireless threat detection software issued an elevated warning was used to calculate a new impact speed using 3 different reaction scenarios and 2 levels of braking.

Results: It was found that between 37 and 86% of the simulated crashes could be avoided, with highest percentage due a fully autonomous system braking at 0.7 g. The same system also reduced the impact speed relative to the actual crash in all cases. Even when a human reaction time of 1.2 s and moderate braking of 0.4 g was assumed, the impact speed was reduced in 78% of the crashes. Crash types that proved difficult for the threat detection engine were head-on crashes where the approach angle was low and right turn–opposite crashes.

Conclusions: These results indicate that connected vehicle technology can be greatly beneficial in real-world crash scenarios and that this benefit would be maximized by having the vehicle intervene autonomously with heavy braking. The crash types that proved difficult for the connected vehicle technology could be better addressed if controller area network (CAN) information is available, such as steering wheel angle, so that driver intent can be inferred sooner. More accurate positioning in the real world (e.g., combining satellite positioning and accelerometer data) would allow the technology to be more effective for near-collinear head-on and rear-end crashes, because the low approach angles that are common in such crashes are currently ignored in order to minimize false alarms due to positioning uncertainty.