A novel approach for analyzing severe crash patterns on multilane highways

This study presents a novel approach for analysis of patterns in severe crashes that occur on mid-block segments of multilane highways with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towards that end. Under this approach crashes serve as units of analysis and it does not require aggregation of crash data over arterial segments of arbitrary lengths. Also, the proposed approach does not use information on non-severe crashes and hence is not affected by under-reporting of the minor crashes. Random samples of time, day of week, and location (i.e., milepost) combinations were collected for multilane arterials in the state of Florida and matched with severe crashes from the corresponding corridor to form matched strata consisting of severe crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on the corresponding milepost locations. Four groups of crashes, severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes, on multilane arterials segments were compared separately to the non-crash cases. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the ADT (Average Daily Traffic). For severe rear-end crashes speed limit, ADT, K-factor, time of day/day of week, median type, pavement condition, and presence of horizontal curvature were significant factors. The proposed approach uses general roadway characteristics as independent variables rather than event-specific information (i.e., crash characteristics such as driver/vehicle details); it has the potential to fit within a safety evaluation framework for arterial segments.     

Investigating the effect of modeling single-vehicle and multi-vehicle crashes separately on confidence intervals of Poisson-gamma models

Crash prediction models still constitute one of the primary tools for estimating traffic safety. These statistical models play a vital role in various types of safety studies. With a few exceptions, they have often been employed to estimate the number of crashes per unit of time for an entire highway segment or intersection, without distinguishing the influence different sub-groups have on crash risk. The two most important sub-groups that have been identified in the literature are single- and multi-vehicle crashes. Recently, some researchers have noted that developing two distinct models for these two categories of crashes provides better predicting performance than developing models combining both crash categories together. Thus, there is a need to determine whether a significant difference exists for the computation of confidence intervals when a single model is applied rather than two distinct models for single- and multi-vehicle crashes. Building confidence intervals have many important applications in highway safety.This paper investigates the effect of modeling single- and multi-vehicle (head-on and rear-end only) crashes separately versus modeling them together on the prediction of confidence intervals of Poisson-gamma models. Confidence intervals were calculated for total (all severities) crash models and fatal and severe injury crash models. The data used for the comparison analysis were collected on Texas multilane undivided highways for the years 1997-2001. This study shows that modeling single- and multi-vehicle crashes separately predicts larger confidence intervals than modeling them together as a single model. This difference is much larger for fatal and injury crash models than for models for all severity levels. Furthermore, it is found that the single- and multi-vehicle crashes are not independent. Thus, a joint (bivariate) model which accounts for correlation between single- and multi-vehicle crashes is developed and it predicts wider confidence intervals than a univariate model for all severities. Finally, the simulation results show that separate models predict values that are closer to the true confidence intervals, and thus this research supports previous studies that recommended modeling single- and multi-vehicle crashes separately for analyzing highway segments.     

How the choice of safety performance function affects the identification of important crash prediction variables

Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics.An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.     

Safety modeling of suburban arterials in Shanghai,China

As urbanization accelerates in Shanghai, land continues to develop along suburban arterials which results in more access points along the roadways and more congested suburban arterials; all these changes have led to deterioration in traffic safety. In-depth safety analysis is needed to understand the relationship between roadway geometric design, access features, traffic characteristics, and safety. This study examined 161 road segments (each between two adjacent signalized intersections) of eight suburban arterials in Shanghai. Information on signal spacing, geometric design, access features, traffic characteristics, and surrounding area types were collected. The effect of these factors on total crash occurrence was investigated. To account for the hierarchical data structure, hierarchical Bayesian models were developed for total crashes. To identify diverse effects on different crash injury severity, the total crashes were separated into minor injury and severe injury crashes. Bivariate hierarchical Bayesian models were developed for minor injury and severe injury to account for the correlation among different severity levels. The modeling results show that the density of signal spacing along arterials has a significant influence on minor injury, severe injury, and total crash frequencies. The non-uniform signal spacing has a significant impact on the occurrence of minor injury crashes. At the segment-level, higher frequencies of minor injury, severe injury, and total crashes tend to occur for the segments with curves, those with a higher density of access points, those with a higher percentage of heavy vehicles, and those in inner suburban areas. This study is useful for applications such as related engineering safety improvements and making access management policy.     

Exploring the transferability of safety performance functions

Safety performance functions (SPFs), by predicting the number of crashes on roadway facilities, have been a vital tool in the highway safety area. The SPFs are typically applied for identifying hot spots in network screening and evaluating the effectiveness of road safety countermeasures. The Highway Safety Manual (HSM) provides a series of SPFs for several crash types by various roadway facilities. The SPFs, provided in the HSM, were developed using data from multiple states. In regions without local jurisdiction based SPFs it is common practice to adopt national SPFs for crash prediction. There has been little research to examine the viability of such national level models for local jurisdictions. Towards understanding the influence of SPF transferability, we examine the rural divided multilane highway models from Florida, Ohio, and California. Traffic, roadway geometry and crash data from the three states are employed to estimate single-state SPFs, two-state SPFs and three-state SPFs. The SPFs are estimated using the negative binomial model formulation for several crash types and severities. To evaluate transferability of models, we estimate a transfer index that allows us to understand which models transfer adequately to other regions. The results indicate that models from Florida and California seem to be more transferable compared to models from Ohio. More importantly, we observe that the transfer index increases when we used pooled data (from two or three states). Finally, to assist in model transferability, we propose a Modified Empirical Bayes (MEB) measure that provides segment specific calibration factors for transferring SPFs to local jurisdictions. The proposed measure is shown to outperform the HSM calibration factor for transferring SPFs.     

The significance of endogeneity problems in crash models: an examination of left-turn lanes in intersection crash models

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system.

The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature.

This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes ‘appear’ to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.     

A study on crashes related to visibility obstruction due to fog and smoke

Research on weather effects has focused on snow- or rain-related crashes. However, there is a lack of understanding of crashes that occur during fog or smoke (FS). This study presents a comprehensive examination of FS-related crashes using crash data from Florida between 2003 and 2007. A two-stage research strategy was implemented (1) to examine FS-related crash characteristics with respect to temporal distribution, influential factors and crash types and (2) to estimate the effects of various factors on injury severity given that a FS-related crash has occurred. The morning hours from December to February are the prevalent times for FS-related crashes. Compared to crashes under clear-visibility conditions, FS-related crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash risk and severity. These crashes were more prevalent on high-speed roads, undivided roads, roads with no sidewalks and two-lane rural roads. Moreover, FS-related crashes were more likely to occur at night without street lighting, leading to more severe injuries.     

Safety effects of the separation distances between driveway exits and downstream U-turn locations

Using U-turns as alternatives to direct left-turns is an important access management treatment which has been widely implemented in the United States to improve safety on multilane highways. The primary objective of this study is to evaluate the safety effects of the separation distances between driveway exits and downstream U-turn locations. To achieve the research objective, crash data reported at 140 street segments in the state of Florida were investigated. The selected sites were divided into three groups based on the separation distances. t-Tests and proportionality tests were performed for comparing crash frequency, crash type, and crash severity between different separation distance groups. Negative-binomial models were developed for examining the factors that contribute to the crashes reported at selected sites. The data analysis results show that the separation distances significantly impact the safety of the street segments between driveways and downstream U-turn locations. A 10% increase in separation distance will result in a 3.3% decrease in total crashes and a 4.5% decrease in the crashes which is related with right-turns followed by U-turns. The models also show that providing U-turns at a signalized intersection will result in more crashes at weaving sections. Thus, if U-turns are to be provided at a signalized intersection, a longer separation distance shall be provided.     

Analysis of safety factors for urban expressways considering the effect of congestion in Shanghai,China

Urban expressways are the key components of the urban traffic network. The traffic safety situation on expressways directly influences the efficiency of the whole network. A total of 48,325 crashes were recorded by Shanghai Expressway Surveillance System in a three-year period. Considering the different crash mechanisms under different congestion levels, models for the total crashes, non-congested-flow crashes and congested-flow crashes were respectively formulated based on the real-time traffic condition corresponding to each crash. Moreover, considering the potential spatial correlation among segments, the adjacent-correlated spatial and distance-correlated spatial models were formulated and compared to the traditional non-spatial-correlated model. A Bayesian approach was employed to estimate the parameters. The results showed that the congestion index, merging ratio, ramp density, and average daily traffic significantly affect the crash frequency. The safety factors in non-congested flow and congested flow are different; diverging behavior is more risky in non-congested flow, more lanes tend to increase the risk of crashes in congested flow, and horizontal curves tend to decrease the crash risk in congested flow but cause high risk in non-congested flow. In addition, the distance-correlated spatial model is found to be the best-fitting model. The results of this study suggested that dedicated safety countermeasures can be designed for different traffic situations on urban expressways.     

Safety performance functions for crash severity on undivided rural roads

The objective of this paper is to explore the effect of the road features of two-lane rural road networks on crash severity. One of the main goals is to calibrate Safety Performance Functions (SPFs) that can predict the frequency per year of injuries and fatalities on homogeneous road segments. It was found that on more than 2000 km of study-road network that annual average daily traffic, lane width, curvature change rate, length, and vertical grade are important variables in explaining the severity of crashes. A crash database covering a 5-year period was examined to achieve the goals (1295 injurious crashes that included 2089 injuries and 235 fatalities). A total of 1000 km were used to calibrate SPFs and the remaining 1000 km reflecting the traffic, geometric, functional features of the preceding one were used to validate their effectiveness. A negative binomial regression model was used. Reflecting the crash configurations of the dataset and maximizing the validation outcomes, four main sets of SPFs were developed as follows: (a) one equation to predict only injury frequency per year for the subset where only non-fatal injuries occurred, (b) two different equations to predict injury frequency and fatality frequency per year per sub-set where at least one fa tality occurred together with one injury, and (c) only one equation to predict the total frequency per year of total casualties correlating accurate percentages to obtain the final expected frequency of injuries and fatalities per year on homogeneous road segments. Residual analysis confirms the effectiveness of the SPFs.     

Bicyclist safety performance functions for a U.S. city

Efforts have intensified to apply a more evidence-based approach to traffic safety. One such effort is the Highway Safety Manual, which provides typical safety performance functions (SPFs) for common road types. SPFs model the mathematical relationship between frequency of crashes and the most significant causal factors. Unfortunately, the manual provides no SPFs for bicyclists, despite disproportionately high fatalities among this group. In this paper, a method for creating city-specific, bicycle SPFs is presented and applied to Boulder, Colorado. This is the first time a bicycle SPF has been created for a U.S. city. Such functions provide a basis for both future investigations into safety treatment efficacy and for prioritizing intersections to better allocate scarce funds for bicycle safety improvements. As expected, the SPFs show that intersections with higher bicyclist traffic and higher motorist traffic have higher motorist-cyclist collisions. The SPFs also demonstrate that intersections with more cyclists have fewer collisions per cyclist, illustrating that cyclists are safer in numbers. Intersections with fewer than 200 entering cyclists have substantially more collisions per cyclist.     

Temporal and spatial analyses of rear-end crashes at signalized intersections

In this study, the generalized estimating equations with the negative binomial link function were used to model rear-end crash frequencies at signalized intersections to account for the temporal or spatial correlation among the data. The longitudinal data for 208 signalized intersections over 3 years and the spatially correlated data for 476 signalized intersections which are located along different corridors were collected in the state of Florida. The modeling results showed that there are high correlations between the longitudinal or spatially correlated rear-end crashes. Some intersection related variables are identified as significantly influencing rear-end crash occurrences at signalized intersections. Intersections with heavy traffic on the major and minor roadways, having more right and left-turn lanes on the major roadway, having a large number of phases per cycle (indicated by the left-turn protection on the minor roadway), with high speed limits on the major roadway, and in high population areas are correlated with high rear-end crash frequencies. On the other hand, intersections with three legs, having channelized or exclusive right-turn lanes on the minor roadway, with protected left-turning on the major roadway, with medians on the minor roadway, and having longer signal spacing have a lower frequency of rear-end crashes.     

Validating a driving simulator using surrogate safety measures

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.     

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips + widening shoulder width) using before–after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)).     

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.     

Quasi-induced exposure: The choice of exposure metrics

The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure.There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others.We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.     

Evaluating safety effectiveness of surface treatment at signalised intersections: a before and after study

In a metropolitan region of Melbourne, Australia, 136 signalised intersections were identified to have been resurfaced with asphalt over the period 2005–2010. In this study, the safety effectiveness of surface treatment was evaluated using Empirical Bayes (EB) approach to account for regression to the mean bias and traffic volume change through using safety performance function (SPF). Safety effects were estimated for total casualty, high severity (fatality and serious injury) and other injury crashes. For conducting EB method a reference group was selected with similar traffic volumes and site characteristics to the treated sites. Negative Binomial regression was applied to develop SPFs that were used to predict the expected number of crashes at the treated sites. The results of EB approach revealed that the treatment effect was found to be significant at 95% confidence level for all crash severity levels. The evaluation results also showed that total casualty crashes were reduced by 21.3% with a standard error of 3.13% and high severity (fatality and serious injury) crashes were reduced by 15.3% with a standard error of 5.56%. Pavement surface treatment was found to reduce other injury crashes by 21.4% with a standard error of 3.75%.     

Predicting crash likelihood and severity on freeways with real-time loop detector data

Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the model's crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.     

Propensity scores-potential outcomes framework to incorporate severity probabilities in the Highway Safety Manual crash prediction algorithm

Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials’ Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that the probability of occurrence of severe injury crashes is higher at lighted intersections compared to unlighted intersections, which contradicts the findings obtained from the propensity scores-potential outcomes framework. This finding underscores the importance of having comparable treated and untreated entities in traffic safety countermeasure evaluations.