Crash Location Research Articles

Assessing rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure

This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver’s reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U.S.A. It was found that the CPI was generally higher for the following heavy vehicle than the following car due to heavy vehicle’s lower braking capability. This study also validates the CPI using the simulated traffic data which replicate the observed traffic conditions a few minutes before the crash time upstream and downstream of the crash locations. The observed data were obtained from crash records and loop detectors on a section of the Gardiner Expressway in Toronto, Canada. The result shows that the values of CPI were consistently higher during the traffic conditions immediately before the crash time (crash case) than the normal traffic conditions (non-crash case). This demonstrates that the CPI can be used to capture rear-end collision risk during car-following maneuver on freeways. The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors.

Hotspots of road traffic crashes in a redeveloping area of Shanghai

ABSTRACTTo identify high-risk locations (hotspots) of road traffic crashes (RTC) in a redeveloping area of Shanghai, for formulating relevant countermeasures in similar areas. After geocoding the crash locations on electronic map, assessment of spatial clustering of accidents and hotspots spatial densities was conducted following Moran's I method, the kernel density estimation, the Ripley's K-function and the network-based kernel density estimation (NKDE). A total of 21,679 RTC incidents resulting in 24,147 victims were recorded from 2010 to 2012. RTCs tended to occur among male (81.8%) aged 20–49 years old (84.9%) riding buses (60.1%) or electric bikes (16.1%) in working time (15%). The network spatial analysis pinpointed the hotspots of RTC at the street level in the Songjiang new urban area, and more RTCs occurred at road intersections than on road segments. Findings from this research may help the authorities develop efficient programmes to target high-risk locations and specific vulnerable populations.

Effect of Helmet Use on Traumatic Brain Injuries and Other Head Injuries in Alpine Sport

Sport helmet effectiveness in preventing traumatic brain injury (TBI) has been repeatedly questioned. This study assesses the effect of helmet use on risk of TBI and other types of head injury (OTHI) in alpine sports. From 2012 to 2014, data on the injured population were collected by physicians in on-mountain clinics in 30 French ski resorts, and interviews were conducted on the slope to sample a noninjured control population. Two sets of cases (1425 participants with TBI and 1386 with OTHI) were compared with 2 sets of controls (2145 participants without injury and 40,288 with an injury to a body part other than the head). The effect of helmet use on the risk of TBI and OTHI was evaluated with a multivariate logistic regression adjusted for age, sex, sport, skill level, crash type, and crash location. Using participants without injury as control, we found that helmet wearers were less likely to sustain any head injury (odds ratio [OR]TBI = 0.65; OROTHI = 0.42). When considering participants with an injury to another body part as control, the risk of OTHI was lower among helmet wearers (OROTHI: 0.61). However, no significant effect was found for the risk of TBI. Participants with low skill levels, those aged <26 and >50 years, snowboarders, and those involved in collision and in snowpark accidents were at higher risk of head injury. This study confirms the effectiveness of helmets in protecting users from head injuries but questions their effects on TBI, especially concussion.

Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events

Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway (“Attiki Odos”) located in Greater Athens Area in Greece for the 2008–2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways.

Wireless system for flight safety industry

Many parameters can be measured and stored in the black box. The details stored in the black box are essential for the analysis of causes of the accident. In the formal black box, data is stored and it can be retrieved only after the identification of black box after crash. So it is very difficult to identify the exact location of the black box after a crash. Many leading officials and famous personalities died in air crashes and it takes a lot of time to identify the location of crash. It leads to wastage of money and time. Saving human life is the basic nature of humanity. In this article, black box data is transmitted to an earth station. The details are updated to the base station through wireless medium. It is transmitted to selected base stations by calculating the shortest path between them using ad hoc algorithm. Ad hoc algorithm is a multi hop network algorithm which switches to various stations by calculating its distance. Thus black box data is updated to base station wirelessly using ad hoc algorithm.

Helmets Matter: Kentucky Motorcycle Crash Victims Seen at a Tennessee Trauma Center.

Motorcycle helmet laws vary by state, with Kentucky requiring helmets only for younger riders. We hypothesized that motorcyclists injured in Kentucky and seen at a Tennessee trauma center would be more likely to be unhelmeted, have more severe head injuries, and sustain more fatal injuries than those injured in Tennessee or Virginia. A Trauma Registry review of 729 injured motorcyclists from January 2005 through June 2015 examined state location of crash, demographics, helmet use, and clinical outcomes. Multivariate logistic regression analysis evaluated predictors for head injury severity and death. Unhelmeted motorcycle rider status predicted more severe head injuries (relative risk 15.3, P < 0.001) and death (relative risk 4.2, P < 0.001). Motorcyclists injured in the state of Kentucky were more likely to be unhelmeted, require an operative procedure, have more severe head injuries, have longer lengths of stay, and sustain more fatal injuries (all with < 0.001) than motorcyclists injured in Tennessee or Virginia. This study lends support for maintaining and enforcing current universal motorcycle helmet laws for all ages in states where they are in effect and for upgrading helmet laws that apply only to some riders.

Wireless system for flight safety industry

Many parameters can be measured and stored in the black box. The details stored in the black box are essential for the analysis of causes of the accident. In the formal black box, data is stored and it can be retrieved only after the identification of black box after crash. So it is very difficult to identify the exact location of the black box after a crash. Many leading officials and famous personalities died in air crashes and it takes a lot of time to identify the location of crash. It leads to wastage of money and time. Saving human life is the basic nature of humanity. In this article, black box data is transmitted to an earth station. The details are updated to the base station through wireless medium. It is transmitted to selected base stations by calculating the shortest path between them using ad hoc algorithm. Ad hoc algorithm is a multi hop network algorithm which switches to various stations by calculating its distance. Thus black box data is updated to base station wirelessly using ad hoc algorithm.

Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence

ABSTRACTOver the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone bicyclists reside. Therefore, this study aims to identify contributing factors for two subjects: (1) the number of bicycle crashes in the crash location's ZIP code and (2) the number of crash-involved bicyclists in their residence's ZIP. In order to achieve these objectives, a multivariate Bayesian Poisson lognormal CAR (conditional autoregressive) model was developed to identify the contributing factors for each subject. Regarding the model performance, the multivariate model outperformed its univariate counterpart in terms of DIC (deviance information criterion). Subsequently, hot zones for the two target zones were identified based on the modeling results. It is expected that practitioners are able to understand the contributing factors for bicycle crashes and identify hotspots from the results suggested in this study. In addition, they could implement safety countermeasures for the identified problematic locations to effectively reduce bicycle crashes.

Analysis of passenger-car crash injury severity in different work zone configurations

Work zone safety remains a priority to the Federal Highway Administration, State Highway Departments, highway engineers, and the traveling public. Work zones create a hospitable environment for crashes; an issue that gained tremendous share of attention in recent years. Therefore, every effort should be sought out to reduce the injury severity of crashes in work zones. In this paper we attempt to investigate factors contributing to the injury severity of passenger-car crashes in different work zone configurations. Considering the discrete ordinal nature of injury severity categories, a Mixed Generalized Ordered Response Probit (MGORP) modeling framework was developed. The model estimation was undertaken by compiling a database consisting of 10 years of crashes that involved at least one passenger car, and occurred in a work zone. Revealing the underlying factors contributing to injury severity levels for different work zone configurations will allow for distinguishing mitigation methods for higher severity outcomes that best suit each of the depicted work zone layouts. This can be accomplished through the implementation of specific safety measures based on the specific configuration of a work zone as a potential crash location. Elasticity analysis suggests that partial control of access, roadways classified as rural, crashes during evening times, crashes during weekends, and curved roadways are key factors that increase the likelihood of severe outcomes. Also, the effects of several covariates were found to vary across the different work zone configurations.

Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China

Traffic safety of freeways has attracted major concerns, especially for a mountainous freeway affected by adverse terrain conditions, constrained roadway geometry and complicated driving environments. On the basis of a comprehensive dataset collected from a mountainous freeway with a length of 61km but gathering 12 tunnels, this study seeks to examining the interactive effect of mountainous freeway alignment, driving behaviors, vehicle characteristics and environmental factors on crash severity. A classification and regression tree (CART) model is employed as it can deal with high-order interactions between explanatory variables.Results show that the driving behavior is the most important determinant for injury severity of mountainous freeway crashes, followed by the crash time, grade, curve radius and vehicle type. These variables, interacted with the factors of season and crash location, may largely account for the likelihood of high risk events which may result in severe crashes. Events associated with a notably higher probability of severe crashes include coach drivers involved in improper lane changing and other improper actions, drivers involved in speeding during afternoon or evening, drivers involved in speeding along large curve and straight segment during morning, noon or night, and drivers involved in fatigue while passing along the downgrade. Safety interventions to prevent severe crashes at the mountainous freeway include hierarchical supervision in terms of hazardous driving events, enhanced enforcement for speeding and fatigue driving, deployment of advanced driving assistance systems for fatigue driving warning, and cumulative driving time monitoring for long-distance-travel freight vehicles.

Corridor safety evaluation in a developing country using a survey based approach

ABSTRACTRoad traffic crashes are a major cause of concern in Saudi Arabia. Although the general view of the design and quality of Saudi Arabia's road network meets global standards, there is not enough in-depth analysis of crash locations and contributing factors and their relationship with user characteristics or behaviors. This study aims to investigate, analyze, and examine the safety problems on a main corridor in the Eastern Province and then suggest treatments to enhance the safety and explore the crash-risk factors associated with crashes on that corridor. A multiple survey approach was used to collect data from road users and experts. The survey addresses traffic safety issues and user behaviors on the corridor. To achieve the study aims, one-way analysis, two-way contingency table analysis, and structural equation modeling were applied. The results showed a need for adequate shoulders along the corridor, and the markings, signs, and signal timing should be reevaluated. In addition, greater restrictions on the use of mobile phones while driving are needed. Also television and radio programs are recommended for traffic awareness as reported by participants. Lastly, it showed that the multiple survey approach can be very useful when crash and geometric data are not available or incomplete.

Assessing the impact of traffic crashes on near freeway air quality

As interest grows in eco-friendly green transportation systems, transportation management agencies have focused on effectively mitigating vehicle emissions. Extensive research has been conducted to enhance the performance of transportation systems in terms of environmental compatibility. A novel feature of this study includes considering crash occurrences when analyzing near-freeway air quality. This study quantifies the impact of traffic crashes on vehicle emissions and the associated near-freeway air quality. Both crash data and vehicle detection systems (VDS) data, which were obtained in 2012 from a Korean freeway located in a densely populated urban area, were used to determine the impact of traffic crashes on near-freeway air quality. MOVES and CALPUFF were adopted to estimate freeway emissions and air dispersion for crash-involved and crash-free traffic conditions. The contributing factors that affect the severity of emission dispersion were identified by regression analyses. Results show a community near freeways within a 1-km radius of crash locations was affected by traffic-related air pollution exposure. The regression analyses also showed crash characteristics, and weather characteristics affect traffic-related air pollution exposure areas. Finally, this study proposed traffic-related public health strategies to mitigate air pollutants generated from crash occurrences on freeways.

How bicycle level of traffic stress correlate with reported cyclist accidents injury severities: A geospatial and mixed logit analysis

Transportation agencies need efficient methods to determine how to reduce bicycle accidents while promoting cycling activities and prioritizing safety improvement investments. Many studies have used standalone methods, such as level of traffic stress (LTS) and bicycle level of service (BLOS), to better understand bicycle mode share and network connectivity for a region. However, in most cases, other studies rely on crash severity models to explain what variables contribute to the severity of bicycle related crashes. This research uniquely correlates bicycle LTS with reported bicycle crash locations for four cities in New Hampshire through geospatial mapping. LTS measurements and crash locations are compared visually using a GIS framework. Next, a bicycle injury severity model, that incorporates LTS measurements, is created through a mixed logit modeling framework. Results of the visual analysis show some geospatial correlation between higher LTS roads and “Injury” type bicycle crashes. It was determined, statistically, that LTS has an effect on the severity level of bicycle crashes and high LTS can have varying effects on severity outcome. However, it is recommended that further analyses be conducted to better understand the statistical significance and effect of LTS on injury severity. As such, this research will validate the use of LTS as a proxy for safety risk regardless of the recorded bicycle crash history. This research will help identify the clustering patterns of bicycle crashes on high-risk corridors and, therefore, assist with bicycle route planning and policy making. This paper also suggests low-cost countermeasures or treatments that can be implemented to address high-risk areas. Specifically, with the goal of providing safer routes for cyclists, such countermeasures or treatments have the potential to substantially reduce the number of fatalities and severe injuries.

Identifying crash risk factors and high risk locations on an interstate network

Highway safety improvement projects are identified by using either (i) a site-specific or (ii) a systemic approach. In the site-specific approach, locations for improvements are ranked according to different performance measures such as critical crash rate, expected crash rate or equivalent property damage only. Alternatively, in the systemic approach, roadway characteristics such as number of lanes, shoulder width, etc. are flagged as a ‘risk’ (or ‘preventative’) feature that increases (decreases) the risk of negative outcomes. Using the Highway Safety Information System database, we seek to merge the two approaches by, first, identifying roadway factors associated with an increased occurrence of car crashes (features we call ‘risk factors’) and, subsequently, identifying roadway segments with a higher crash risk. Specifically, we model the locations of crashes as a realization from a spatial point process. We then parameterize the associated intensity surface of this spatial point process as the sum of a regression on roadway characteristics and spatially correlated error terms. Thus, through the regression piece, we identify hazardous roadway features and through the spatially correlated error terms, we identify locations of high risk.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity.

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

The location of late night bars and alcohol-related crashes in Houston, Texas

A study in the City of Houston, Texas, related the location of establishments primarily serving alcohol (“bars”) after midnight to late night alcohol-related motor vehicle crashes. There were three data sets for 2007–09: 1) 764bars that were open after midnight; 2) 1660 alcohol-related crashes that occurred within the City of Houston between midnight and 6 am; and 3) 4689 modeling network road segments to which bars and alcohol-related crashes were assigned.Forty-five percent of the late night alcohol-related crashes were within a quarter mile of a late night bar. The bars were highly concentrated in 17 small bar clusters. Using the modeling network, Poisson-Gamma-CAR and Poisson-Lognormal-CAR spatial regression models showed a positive exponential relationship between late night alcohol-related crashes and the number of late nights bars and bar clusters, and a negative exponential relationship to distance to the nearest late night bar controlling for the type of road segment (freeway, principal arterial, minor arterial). A more general model dropped the bar cluster variable.Further, the Poisson-Gamma-CAR model appeared to produce a better representation than the Poisson-Lognormal-CAR model though the errors were different. The general Poisson-Gamma-CAR model showed that each late night bar increased the frequency of alcohol-related crashes on a segment by approximately 190%. For each mile closer a segment was to a late night bar, the likelihood increased by 42%.

Considering built environment and spatial correlation in modeling pedestrian injury severity

ABSTRACTObjective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.

Legal and Engineering Aspects of Deployable Black Boxes with Video Recording Capability

Black box is a compilation of data produced by on-board and ground communications, specifically and inherently in digital nature; therefore it is a digital database and part of a discussion on protection of digital databases. The Existing Black box technology used in Commercial Aircraft is accommodated with Affixed type black boxes. However, if the Deployable black boxes are implemented with fixed black boxes as a dual combined recorder system, the discovery of crash location can be determined with more effectually than the present technology. The lives of the survivors, initiation of aircraft accident and investigation progression obtain safety recommendations to avoid similar happenstances will depend upon the accessibility to the crash location immediately by search and rescue organizations with the reliable information they achieved through satellite communications. Deployable Recorders will provide a backup source of flight data other than the fixed black box data; assist the investigators to carry out their investigations with more trustworthy information while implementing the video recording capability. A new idea has been introduced in this research to implement a mechanism to deploy a parachute with the ejection of the deployable recorder from the aircraft to minimize the impact during its dwindling to the ground. Not only in engineering but also the legal aspects of Deployable black boxes work along with the rights of the human beings. The legal aspect of the Deployable black boxes connect with aircraft accident and an incident investigation process can be discussed in the preview of domestic and international law; for example under the Convention on International Civil Aviation. Annex 13 of the Chicago Convention is endowed with the international requirements for the investigation of aircraft accidents and incidents. The protection over digital databases can be achieved through legal and technological means (Technological Protection Mechanisms-TPMs). The paper investigates the legal and engineering aspects of Black box technology used in Commercial Aircraft. KEYWORDS: Black Box, Deploy, Chicago Convention, Databases, International Air Law

Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances

Differences in hybrid and non-hybrid vehicle design, and potential differences in driver-related behavior among owners of these vehicle types, can potentially have interesting implications for safety-related policies. To study possible differences in hybrid and non-hybrid occupant injury severities in motor vehicle crashes, this paper uses a sample of hybrid-vehicle-involved crashes and estimates a mixed logit model of the resulting injury level of the most severely injured occupant in the crash, while accounting for possible heterogeneity in the means and variances of model parameters. A total of 2015 crashes in Washington State, involving at least one hybrid vehicle in the 5-year period from January 1, 2006 to December 31, 2010 were analyzed. The data included crash information regarding occupants, vehicles, environmental conditions at the time of the crash, hybrid and non-hybrid vehicle attributes, crash-contributing circumstances for both hybrid and non-hybrid involved vehicles, collision type and crash location information relating to intersections, functional class of the highway, and highway curvature. Model estimation results show that a wide range of variables influence the most severely injured occupant, and that the number-of-occupants parameter and the intersection-location indicator parameter are random with significant heterogeneity in both means and variances. Sources of heterogeneity include the ratio of hybrid to non-hybrid vehicle counts in the crash, vehicle weight to horsepower ratio range (maximum difference in ratio) for the crash, number of adult occupants aged 41–64years, functional class, and vehicle type interactions. The results further demonstrate the potential of models that address unobserved heterogeneity to unravel important relationships in the analysis of highway injury severities.

Epidemiology of EMS Responses to Traffic Crashes in the United States for 2014

Background In 2014, there were 6.1 million crashes, resulting in 32,675 fatalities, 2.3 million injured persons, and comprehensive costs of an estimated 836 billion. Reductions in traffic crash fatalities and associated injuries require a comprehensive traffic safety approach that includes emergency medical services (EMS). This study aimed to characterize the EMS response to traffic crashes for the United States in 2014 and examine associations with geographic and socioeconomic factors. Methods Using the 2014 National EMS Database, this study analyzed data from 12 states that had 100% of EMS agencies reporting. Descriptive analyses characterized the EMS response to traffic crashes by time of day, day of week, patient demographics, and urbanicity. Traffic crash incident location zip code was used to link several zip code level socioeconomic variables from the 2010–2014 American Community Survey to each EMS record. Socioeconomic variables were categorized by a quintile distribution. A logistic model was used to determine likelihood estimates between patient demographics and zip-code level socioeconomic variables, with EMS system time, delays, and provider level of service. Results In total there were 288,202 9-1-1 EMS activations dispatched to a traffic crash identified in the 2014 National EMS Database. Activations were concentrated in urban areas (70%) and the majority occurred between the hours of noon and 6 pm (38%). Delays were associated with patient ethnicity and multiple zip code level socioeconomic measures. As the median household income of the zip code crash site decreased, the likelihood of having any EMS delay increased. As the percent of the uninsured population increased, the likelihood of having any EMS delay increased. Patients identified as Hispanic or Latino were two times more likely to have a response and transport delay. American Indians/Alaskan Natives had higher odds of experiencing response delays (OR=1.8), scene delays (OR=1.5), and transport delays (OR =1.9) than any other racial or ethnic group in comparison to Whites. Conclusions Income and racial disparities exist for EMS provider-reported response, scene, and transport delays for traffic crash patients. By identifying disparities, EMS systems should collaborate with public health and traffic safety professionals to develop strategies that decrease gaps in EMS response and service, thereby improving traffic crash mortality and morbidity.