Impact of traffic states on freeway crash involvement rates

Freeway traffic accidents are complicated events that are influenced by multiple factors including roadway geometry, drivers’ behavior, traffic conditions and environmental factors. Among the various factors, crash occurrence on freeways is supposed to be strongly influenced by the traffic states representing driving situations that are changed by road geometry and cause the change of drivers’ behavior. This paper proposes a methodology to investigate the relationship between traffic states and crash involvements on the freeway. First, we defined section-based traffic states: free flow (FF), back of queue (BQ), bottleneck front (BN) and congestion (CT) according to their distinctive patterns; and traffic states of each freeway section are determined based on actual measurements of traffic data from upstream and downstream ends of the section. Next, freeway crash data are integrated with the traffic states of a freeway section using upstream and downstream traffic measurements. As an illustrative study to show the applicability, we applied the proposed method on a 32-mile section of I-880 freeway. By integrating freeway crash occurrence and traffic data over a three-year period, we obtained the crash involvement rate for each traffic state. The results show that crash involvement rate in BN, BQ, and CT states are approximately 5 times higher than the one in FF. The proposed method shows promise to be used for various safety performance measurement including hot spot identification and prediction of the number of crash involvements on freeway sections.     

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers’ behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.     

Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes

This study presents multi-level analyses for single- and multi-vehicle crashes on a mountainous freeway. Data from a 15-mile mountainous freeway section on I-70 were investigated. Both aggregate and disaggregate models for the two crash conditions were developed. Five years of crash data were used in the aggregate investigation, while the disaggregate models utilized one year of crash data along with real-time traffic and weather data. For the aggregate analyses, safety performance functions were developed for the purpose of revealing the contributing factors for each crash type. Two methodologies, a Bayesian bivariate Poisson-lognormal model and a Bayesian hierarchical Poisson model with correlated random effects, were estimated to simultaneously analyze the two crash conditions with consideration of possible correlations. Except for the factors related to geometric characteristics, two exposure parameters (annual average daily traffic and segment length) were included. Two different sets of significant explanatory and exposure variables were identified for the single-vehicle (SV) and multi-vehicle (MV) crashes. It was found that the Bayesian bivariate Poisson-lognormal model is superior to the Bayesian hierarchical Poisson model, the former with a substantially lower DIC and more significant variables. In addition to the aggregate analyses, microscopic real-time crash risk evaluation models were developed for the two crash conditions. Multi-level Bayesian logistic regression models were estimated with the random parameters accounting for seasonal variations, crash-unit-level diversity and segment-level random effects capturing unobserved heterogeneity caused by the geometric characteristics. The model results indicate that the effects of the selected variables on crash occurrence vary across seasons and crash units; and that geometric characteristic variables contribute to the segment variations: the more unobserved heterogeneity have been accounted, the better classification ability. Potential applications of the modeling results from both analysis approaches are discussed.     

Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities’ (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers’ characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (I-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models’ results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed.     

A simultaneous equations model of crash frequency by severity level for freeway sections

This paper presents a simultaneous equations model of crash frequencies by severity level for freeway sections using five-year crash severity frequency data for 275 multilane freeway segments in the State of Washington. Crash severity is a subject of much interest in the context of freeway safety due to higher speeds of travel on freeways and the desire of transportation professionals to implement measures that could potentially reduce crash severity on such facilities. This paper applies a joint Poisson regression model with multivariate normal heterogeneities using the method of Maximum Simulated Likelihood Estimation (MSLE). MSLE serves as a computationally viable alternative to the Bayesian approach that has been adopted in the literature for estimating multivariate simultaneous equations models of crash frequencies. The empirical results presented in this paper suggest the presence of statistically significant error correlations across crash frequencies by severity level. The significant error correlations point to the presence of common unobserved factors related to driver behavior and roadway, traffic and environmental characteristics that influence crash frequencies of different severity levels. It is found that the joint Poisson regression model can improve the efficiency of most model coefficient estimators by reducing their standard deviations. In addition, the empirical results show that observed factors generally do not have the same impact on crash frequencies at different levels of severity.     

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition.     

Effects of extraordinary snowfall on traffic safety

Snowfall affects traffic safety by causing changes in roadway surface and visibility that result in crashes, spinouts, and breakdowns. Using data collected at a site that regularly receives nearly 1000 cm of snow during the snow season, this study examines the impact of snowfall quantity, gap between snow events, and weather conditions on crash and incident frequencies. Estimation results from regression analysis show that snowfall severity significantly impacts crashes and incidents but the impact diminishes marginally with each additional centimeter of snow. Gap has a significant fixed effect on crashes but its impact on incidents varies significantly across observations. The effect of the mixed precipitation condition is smaller in comparison to an all-snow condition. These results will help inform policy for snow removal and traffic enforcement in areas of high snowfall.     

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.     

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.     

Real-time assessment of fog-related crashes using airport weather data: A feasibility analysis

The effect of reduction of visibility on crash occurrence has recently been a major concern. Although visibility detection systems can help to mitigate the increased hazard of limited-visibility, such systems are not widely implemented and many locations with no systems are experiencing considerable number of fatal crashes due to reduction in visibility caused by fog and inclement weather. On the other hand, airports’ weather stations continuously monitor all climate parameters in real-time, and the gathered data may be utilized to mitigate the increased risk for the adjacent roadways. This study aims to examine the viability of using airport weather information in real-time road crash risk assessment in locations with recurrent fog problems. Bayesian logistic regression was utilized to link six years (2005–2010) of historical crash data to real-time weather information collected from eight airports in the State of Florida, roadway characteristics and aggregate traffic parameters. The results from this research indicate that real-time weather data collected from adjacent airports are good predictors to assess increased risk on highways.     

Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis

This research explores the injury severity of pedestrians in motor-vehicle crashes. It is hypothesized that the variance of unobserved pedestrian characteristics increases with age. In response, a heteroskedastic generalized extreme value model is used. The analysis links explanatory factors with four injury outcomes: fatal, incapacitating, non-incapacitating, and possible or no injury. Police-reported crash data between 1997 and 2000 from North Carolina, USA, are used. The results show that pedestrian age induces heteroskedasticity which affects the probability of fatal injury. The effect grows more pronounced with increasing age past 65. The heteroskedastic model provides a better fit than the multinomial logit model. Notable factors increasing the probability of fatal pedestrian injury: increasing pedestrian age, male driver, intoxicated driver (2.7 times greater probability of fatality), traffic sign, commercial area, darkness with or without streetlights (2-4 times greater probability of fatality), sport-utility vehicle, truck, freeway, two-way divided roadway, speeding-involved, off roadway, motorist turning or backing, both driver and pedestrian at fault, and pedestrian only at fault. Conversely, the probability of a fatal injury decreased: with increasing driver age, during the PM traffic peak, with traffic signal control, in inclement weather, on a curved roadway, at a crosswalk, and when walking along roadway.     

Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity

This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc.Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists.This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists.     

Impact of traffic oscillations on freeway crash occurrences

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion.     

A tree-structured crash surrogate measure for freeways

In this paper, we propose a novel methodology to define and estimate a surrogate measure. By imposing a hypothetical disturbance to the leading vehicle, the following vehicle’s action is represented as a probabilistic causal model. After that, a tree is built to describe the eight possible conflict types under the model. The surrogate measure, named Aggregated Crash Index (ACI), is thus proposed to measure the crash risk. This index reflects the accommodability of freeway traffic state to a traffic disturbance. We further apply this measure to evaluate the crash risks in a freeway section of Pacific Motorway, Australia. The results show that the proposed indicator outperforms the three traditional crash surrogate measures (i.e., Time to Collision, Proportion of Stopping Distance, and Crash Potential Index) in representing rear-end crash risks. The applications of this measure are also discussed.     

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.     

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.     

Evaluation of the impacts of traffic states on crash risks on freeways

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states.     

Evaluating the safety impacts of the number and arrangement of lanes on freeway exit ramps

The primary objective of this study is to evaluate the impacts of the number and arrangement of lanes on freeway exit ramps on the safety performance of freeway diverge areas. The research team collected crash data at 343 freeway segments in the state of Florida. Four different types of exit ramps were considered in this study. They were defined as type 1, type 2, type 3, and type 4 exit ramps respectively. Cross-sectional comparison was conducted for comparing crash frequency, crash rate and crash severity between different types of freeway exit ramps. Crash prediction models were developed to identify the factors that contribute to the crashes reported at selected freeway segments and to provide quantified information regarding the safety impacts of different freeway exit ramps. It was found that the ramp and freeway AADT, posted speed limit on freeway, deceleration lane length, right shoulder width, and the type of exit ramp significantly affected the safety performance of freeway diverge areas. The study demonstrated the safety benefits of using lane-balanced exit ramps. Based on the crash prediction models, replacing a type 1 exit ramp (lane-balanced) with a type 2 exit ramp (not lane-balanced) will increase crash counts at freeway diverge areas by 68.33%. Replacing a type 3 ramp (lane-balanced) with a type 4 ramp (not lane-balanced) will increase crash counts at freeway diverge areas by 32.20%.