Stephen H Richards

Using hierarchical tree-based regression model to predict train-vehicle crashes at passive highway-rail grade crossings.

This paper applies a nonparametric statistical method, hierarchical tree-based regression (HTBR), to explore train-vehicle crash prediction and analysis at passive highway-rail grade crossings. Using the Federal Railroad Administration (FRA) database, the research focuses on 27 years of train-vehicle accident history in the United States from 1980 through 2006. A cross-sectional statistical analysis based on HTBR is conducted for public highway-rail grade crossings that were upgraded from crossbuck-only to stop signs without involvement of other traffic-control devices or automatic countermeasures. In this study, HTBR models are developed to predict train-vehicle crash frequencies for passive grade crossings controlled by crossbucks only and crossbucks combined with stop signs respectively, and assess how the crash frequencies change after the stop-sign treatment is applied at the crossbuck-only-controlled crossings. The study results indicate that stop-sign treatment is an effective engineering countermeasure to improve safety at the passive grade crossings. Decision makers and traffic engineers can use the HTBR models to examine train-vehicle crash frequency at passive crossings and assess the potential effectiveness of stop-sign treatment based on specific attributes of the given crossings.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: An exploratory analysis

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies.

Train–Vehicle Crash Risk Comparison Between Before and After Stop Signs Installed at Highway–Rail Grade Crossings

Objective: The safety benefit of stop sign treatment employed at passive highway–rail crossings has been a subject of research for many years. The objectives of this research is to investigate whether and to what degree the crash rate has changed at previously passive grade crossings after stop signs were implemented and examine whether and how the crash characteristics (associated with vehicle type, crossing surrounding, crossing design, crash severity, etc.) changed subsequently. Methods: Federal Railroad Administration grade crossing databases during the 26-year period (1980–2005) were applied in this study. Among the stop-controlled grade crossings, a total of 7394 “target” crossings were identified to be once crossbucks controlled and subsequently upgraded with the installation of stop signs without the implementation of other traffic control devices during the study period. Each target crossing was further divided into two time periods: when it was controlled by crossbucks only (before) and when it was controlled by stop signs (after). Both annual crash rate analysis and crash propensity analysis of before–after stop sign installation are conducted to quantify the safety benefit of stop sign treatment. Results: It was found that during the 26-year period (1980–2005), the annual crash rates when the crossings were controlled by crossbucks-only were consistently higher than the crash rates when the crossings were controlled by stop signs. The further crash propensity analysis indicated that the stop sign treatment was especially effective at crossings with higher annual average daily traffic (AADT), advanced warning signs, sight distance problem, adverse lighting conditions; the motorist-stopped-on-crossing, did-not-stop, and injury crash risks were also significantly reduced after stop signs were applied. Conclusions: The finding of this study suggested that the vehicle volume should be included into the guideline for stop sign use. Therefore, engineers and decision makers are encouraged to routinely check available sight distances at passive crossings controlled by crossbucks only and add stop signs to the crossings with insufficient sight distances. Additionally, it is suggested that advanced warning signs should be jointly used at stop-controlled crossings to maximize the safety effect. However, stop signs were less effective at crossings with higher train speeds or track classifications, where active warning devices may be a better safety solution for grade crossings.

What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors

Crashes at highway-rail grade crossings can result in severe injuries and fatalities to vehicle occupants. Using a crash database from the Federal Railroad Administration (N=15,639 for 2004-2013), this study explores differences in safety outcomes from crashes between passive controls (Crossbucks and STOP signs) and active controls (flashing lights, gates, audible warnings and highway signals). To address missing data, an imputation model is developed, creating a complete dataset for estimation. Path analysis is used to quantify the direct and indirect associations of passive and active controls with pre-crash behaviors and crash outcomes in terms of injury severity. The framework untangles direct and indirect associations of controls by estimating two models, one for pre-crash driving behaviors (e.g., driving around active controls), and another model for injury severity. The results show that while the presence of gates is not directly associated with injury severity, the indirect effect through stopping behavior is statistically significant (95% confidence level) and substantial. Drivers are more likely to stop at gates that also have flashing lights and audible warnings, and stopping at gates is associated with lower injury severity. This indirect association lowers the chances of injury by 16%, compared with crashes at crossings without gates. Similar relationships between other controls and injury severity are explored. Generally, crashes occurring at active controls are less severe than crashes at passive controls. The results of study can be used to modify Crash Modification Factors (CMFs) to account for crash injury severity. The study contributes to enhancing the understanding of safety by incorporating pre-crash behaviors in a broader framework that quantifies correlates of crash injury severity at active and passive crossings.

Evaluation of Effectiveness of Stop-Sign Treatment at Highway–Railroad Grade Crossings

The safety benefit of stop-sign treatment employed at passive highway–rail crossings has been a subject of research for many years. The objective of this study is to assess the effectiveness of the stop-sign treatment on crossing safety. Using the Federal Railroad Administration database, the research focused on 26 years of vehicle–train accident history in the United States from 1980 through 2005. A before-and-after and cross-sectional statistical analysis was conducted for 7,394 public highway–railroad grade crossings that were upgraded from cross buck only to stop signs without involvement in other traffic-control devices (TCDs) or automatic countermeasures. The study found that accident rates based on annual accident frequency per 1,000 crossings were significantly higher during the period when crossings were controlled by cross bucks only than when they were controlled by stop signs. Further, this study developed negative binomial accident prediction models, respectively, for paved and unpaved highway–rail grade-crossings that include effect for stop-sign treatment. Based on specific attributes of the current crossbuck-only-controlled crossings, decision makers and traffic engineers can use the models to examine the accident risks at crossings and assess the potential effectiveness of stop-sign treatment.

Identifying the factors contributing to the severity of truck-involved crashes

To address the dilemma between the need for truck transportation and the costs related to truck-involved crashes, the key is to identify the risk factors that significantly affect truck-involved crashes. The objective of this research is to estimate the effects of the characteristics of traffic, driver, geometry, and environment on severity of truck-involved crashes. Based on four crash severity categories (fatal/incapacitating, non-incapacitating, possible injury, and no injury/property damage only), a multinomial logit model is conducted to identify the risk factors. The investigation of risk ratios indicates that lower traffic volume with higher truck percentage is associated with more serious traffic crash with fatal/incapacitating injury while a non-standard geometric design is the main cause of non-incapacitating crashes. The influences of weather are significant for the possible-injury crashes while driver condition is the principal cause of no-injury/property-damage-only crashes. In addition, the statistical results demonstrate that the influence of the truck percentage is significant. One-unit change in the truck percentage will cause more than one times probability of being in an injury.

Driver Behavior at Highway Rail Grade Crossings with Passive Traffic Controls: A Driving Simulator Study

ABSTRACT Solutions to highway–rail grade crossings require understanding driver responses to traffic controls at crossings. This study examines differences in driver behaviors and safety at several types of passive traffic controls at grade crossings utilizing a high-fidelity driving simulator at the University of Tennessee. This article investigates the use of Stop and Yield signs as viable alternatives to upgrading a passive grade crossing to an active grade crossing. In addition to presenting descriptive statistics, mixed-effects regression models were estimated to handle repeated observations by 64 test participants. Additionally, path analysis provides a more nuanced interpretation of the results. Stop signs at railroad grade crossings were found to be associated with an increased likelihood of drivers looking for an oncoming train while approaching such passive crossings. They were also more inclined to reduce speeds and or stop. There were fewer violations at crossings with Stop signs when a train coming. The behaviors of drivers when faced with Yield signs were very similar to crossbuck signs, with little looking or stopping. The findings imply that Stop signs have the potential to decrease the chance of colliding with a train at passive grade crossings and reduce the crash severity even if a crash occurs.

Influence of curbs on traffic crash frequency on high-speed roadways.

Objective: Curbs are commonly used on roadways for drainage management, access control, and other positive functions. However, curbs may also bring about unfavorable effects on drivers’ behavior and vehicle stability when hitting curbs, especially for high-speed roadways. The objective of this article is to investigate whether the presence of curbs along outside shoulders has produced adverse effects on traffic safety on high-speed roadways and whether increasing speed limits has created any further harmful effects. Methods: In this study, the Illinois Highway Safety Database from 2003 to 2007 was selected to evaluate the effects of curbs over traffic safety on 2-lane and 4-lane non-freeways with speed limits of 45, 50, and 55 mph. Wilcoxon/Kruskal-Wallis nonparametric tests were conducted to compare the road-segment crash rates between 3 types of outside shoulders (curbed shoulder, soft flush shoulder, and hard flush shoulder) and 3 speed limits. In addition, the zero-inflated negative binomial models were developed for all of the roadway segments combined, as well as the curbed outside shoulder–only segments. The models were used to estimate the influences of curbed outside shoulder, speed limit level, as well as other roadway characteristics on crash frequency. Results: It was found that road segments with different types of outside shoulders were from different populations in terms of the distribution of crash rates, so did segments with different speed limits. Further, the crash frequency analysis indicates that the curbed outside shoulders did not induce a higher crash frequency compared to the other 2 types of outside shoulders. In addition, there was no evidence that a decrease in speed limit results in reduction in crash frequencies for road segments with curbed outside shoulders. Conclusions: The findings of this study suggest that the employment of curbed outside shoulders on high-speed roadways would not pose any significantly harmful effect on the occurrence of crashes, and on high-speed roadways with curbed outside shoulders, reducing the speed limit from 55 to 45 mph would not achieve any safety benefit.

Two-Vehicle Injury Severity Models Based on Integration of Pavement Management and Traffic Engineering Factors

Objective: The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. Methods: This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. Results: The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural–urban location, speed limit, peaking hour, and light condition. Conclusions: The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.

Analyzing Influence Factors of Transverse Cracking on LTPP Resurfaced Asphalt Pavements through NB and ZINB Models

The negative binomial (NB) and zero-inflated negative binomial (ZINB) models were employed to simulate the development of pavement transverse cracks on asphalt overlays and to evaluate the influence of different designs of asphalt overlays on crack development. Pavement transverse crack data were collected from 15 long-term pavement performance (LTPP) SPS-5 test sites. Analyzed factors include traffic level, overlay thickness, mixture [using reclaimed asphalt pavement (RAP) or virgin], intensity of surface preparation (mill or no mill) before overlay, total thickness of pavement, and freeze index. Analysis results indicate that the NB and ZINB models were effective in simulating the development of pavement transverse cracks by addressing the overdispersion. In addition, the ZINB model outperformed the NB model by explaining the excess zeros in the cracking count data to capture both the initiation and propagation of cracking. The regression analysis indicates that mill before overlay is effective in retarding the initiation of cracks, but not the propagation of cracks. Thicker overlay appears to reduce transverse cracking. High traffic level or using RAP is likely to increase the number of transverse cracks. Total thickness of pavement and freeze index are not significant for the development of transverse cracks.