Evaluation of rear-end crash risk at work zone using work zone traffic data

This paper aims to evaluate the rear-end crash risk at work zone activity area and merging area, as well as analyze the impacts of contributing factors by using work zone traffic data. Here, the rear-end crash risk is referred to as the probability that a vehicle is involved in a rear-end crash accident. The deceleration rate to avoid the crash (DRAC) is used in measuring rear-end crash risk. Based on work zone traffic data in Singapore, three rear-end crash risk models are developed to examine the relationship between rear-end crash risk at activity area and its contributing factors. The fourth rear-end crash risk model is developed to examine the effects of merging behavior on crash risk at merging area. The ANOVA results show that the rear-end crash risk at work zone activity area is statistically different from lane positions. Model results indicate that rear-end crash risk at work zone activity area increases with heavy vehicle percentage and lane traffic flow rate. An interesting finding is that the lane closer to work zone is strongly associated with higher rear-end crash risk. A truck has much higher probability involving in a rear-end accident than a car. Further, the expressway work zone activity area is found to have much larger crash risk than arterial work zone activity area. The merging choice has the dominated effect on risk reduction, suggesting that encouraging vehicles to merge early may be the most effective method to reduce rear-end crash risk at work zone merging area.     

Analysis of work zone rear-end crash risk for different vehicle-following patterns

This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car–car, car–truck, truck–car and truck–truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car–truck following pattern has the largest rear-end crash risk, followed by truck–truck, truck–car and car–car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car–car pattern, lane position has less effect on rear-end crash risk in the car–truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck–car pattern.     

In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas

This study investigates the drivers’ merging behavior and the rear-end crash risk in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the merging traffic data from a work zone site in Singapore, a mixed probit model is developed to describe the merging behavior, and two surrogate safety measures including the time to collision (TTC) and deceleration rate to avoid the crash (DRAC) are adopted to compute the rear-end crash risk between the merging vehicle and its neighboring vehicles. Results show that the merging vehicle has a bigger probability of completing a merging maneuver quickly under one of the following situations: (i) the merging vehicle moves relatively fast; (ii) the merging lead vehicle is a heavy vehicle; and (iii) there is a sizable gap in the adjacent through lane. Results indicate that the rear-end crash risk does not monotonically increase as the merging vehicle speed increases. The merging vehicle's rear-end crash risk is also affected by the vehicle type. There is a biggest increment of rear-end crash risk if the merging lead vehicle belongs to a heavy vehicle. Although the reduced remaining distance to work zone could urge the merging vehicle to complete a merging maneuver quickly, it might lead to an increased rear-end crash risk. Interestingly, it is found that the rear-end crash risk could be generally increased over the elapsed time after the merging maneuver being triggered.     

Developing an inverse time-to-collision crash alert timing approach based on drivers' last-second braking and steering judgments

Drivers were asked to execute last-second braking and steering maneuvers while approaching a surrogate target lead vehicle. This surrogate target was designed to allow safely placing naive drivers in controlled, realistic rear-end crash scenarios under test track conditions. Maneuver intensity instructions were varied so that drivers' perceptions of normal and non-normal braking envelopes could be properly identified and modeled for forward collision warning timing purposes. The database modeled includes 3536 last-second braking judgment trials. A promising inverse time-to-collision model was developed, which assumes that the driver deceleration response in response to a crash alert is based on an inverse time-to-collision threshold that decreases linearly with driver speed.     

A crash-prediction model for road tunnels

Considerable research has been carried out into open roads to establish relationships between crashes and traffic flow, geometry of infrastructure and environmental factors, whereas crash-prediction models for road tunnels, have rarely been investigated. In addition different results have been sometimes obtained regarding the effects of traffic and geometry on crashes in road tunnels. However, most research has focused on tunnels where traffic and geometric conditions, as well as driving behaviour, differ from those in Italy. Thus, in this paper crash prediction-models that had not yet been proposed for Italian road tunnels have been developed. For the purpose, a 4-year monitoring period extending from 2006 to 2009 was considered. The tunnels investigated are single-tube ones with unidirectional traffic. The Bivariate Negative Binomial regression model, jointly applied to non-severe crashes (accidents involving material-damage only) and severe crashes (fatal and injury accidents only), was used to model the frequency of accident occurrence. The year effect on severe crashes was also analyzed by the Random Effects Binomial regression model and the Negative Multinomial regression model. Regression parameters were estimated by the Maximum Likelihood Method. The Cumulative Residual Method was used to test the adequacy of the regression model through the range of annual average daily traffic per lane. The candidate set of variables was: tunnel length (L), annual average daily traffic per lane (AADT_L), percentage of trucks (%Tr), number of lanes (N_L), and the presence of a sidewalk. Both for non-severe crashes and severe crashes, prediction-models showed that significant variables are: L, AADT_L, %Tr, and N_L. A significant year effect consisting in a systematic reduction of severe crashes over time was also detected. The analysis developed in this paper appears to be useful for many applications such as the estimation of accident reductions due to improvement in existing tunnels and/or to modifications of traffic control systems, as well as for the prediction of accidents when different tunnel design options are compared.     

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.     

The effect of Center High Mounted Stop Lamp (CHMSL) on rear-end accidents in Israel

This paper examines the effect of the use of Center High Mounted Stop Lamp (CHMSL) on rear-end accidents, as reflected in Israeli police records from calendar years 1991-2002. The basic analysis, similar to that used in previous CHMSL studies, compares the involvement in accidents of passenger cars of model years 1994-1996 that are equipped with CHMSL with passenger cars of model years 1991-1993 that are not equipped with CHMSL. The number of involvements as the struck vehicle in a rear-end accident was used as the relevant measurement and the number of involvements as the striking vehicle in a rear-end accident was used as the reference measurement. The results yielded an odds ratio of 0.93. The explanation that the CHMSL is responsible for the 7% decrease is intuitively appealing and is consistent with previous findings. However, the strength of this evidence is marginal (p = 0.07). Additional analyses evaluated the model year effect in greater detail, in order to determine whether there exists a change point between 1993 and 1994 as would be expected from a CHMSL effect, or whether the effect is spurious. Detailed analyses were performed on the ratio of struck to striking involvements as well as the rates of involvement of both types. These analyses showed that (1) the chosen reference measurement is an appropriate one, but (2) the 0.93 odds ratio is quite possibly due to other reasons unrelated to the CHMSL, thus further limiting the confidence in CHMSL effectiveness.     

Prevalence and factors associated with road traffic crash among taxi drivers in Hanoi,Vietnam

Injury due to road traffic crash is a major cause of ill health and premature deaths in developing countries. Taxis provide a main mode of public transport in Vietnam but there has been little research on the risk of crash for taxi drivers. This retrospective study collected information on taxi crashes for the period 2006–2009 by interviewing drivers from five taxi companies in Hanoi, Vietnam, using a structured questionnaire. Of the total 1214 participants recruited, 276 drivers reported at least one crash, giving an overall crash prevalence of 22.7%. Among the crashed group, 50 drivers (18.1%) were involved in two to four crashes. Logistic regression analysis further identified age of driver, type of driving licence, employment status, perceived sufficiency of income, seat-belt usage, and traffic infringement history to be significantly associated with the crash risk. Further prospective and qualitative studies are recommended to provide detailed crash characteristics as well as behaviour and perception of taxi drivers, so that an effective intervention can be developed to improve road safety and to prevent injury of these commercial drivers.     

Modelling the hierarchical structure of road crash data--application to severity analysis

Road crashes have an unquestionably hierarchical crash-car-occupant structure. Multilevel models are used with correlated data, but their application to crash data can be difficult. The number of sub-clusters per cluster is small, with less than two cars per crash and less than two occupants per car, whereas the number of clusters can be high, with several hundred/thousand crashes. Application of the Monte-Carlo method on observed and simulated French road crash data between 1996 and 2000 allows comparing estimations produced by multilevel logistic models (MLM), Generalized Estimating Equation models (GEE) and logistic models (LM). On the strength of a bias study, MLM is the most efficient model while both GEE and LM underestimate parameters and confidence intervals. MLM is used as a marginal model and not as a random-effect model, i.e. only fixed effects are taken into account. Random effects allow adjusting risks on the hierarchical structure, conferring an interpretative advantage to MLM over GEE. Nevertheless, great care is needed for data coding and quite a high number of crashes are necessary in order to avoid problems and errors with estimates and estimate processes. On balance, MLM must be used when the number of vehicles per crash or the number of occupants per vehicle is high, when the LM results are questionable because they are not in line with the literature or finally when the p-values associated to risk measures are close to 5%. In other cases, LM remains a practical analytical tool for modelling crash data.     

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.