Accidents involving pedestrians with their backs to traffic or facing traffic: an evaluation of crash characteristics and injuries

This paper examines pedestrian anatomical injuries and crash characteristics in back‐to‐traffic and facing‐traffic crashes. Pedestrian crashes involving pedestrians walking along streets (i.e. with their backs to traffic or facing traffic) have been overlooked in literature. Although this is not the most frequent type of crash, the crash consequence to pedestrians is a safety concern. Combining Taiwan A1A2 police‐reported accident data and data from the National Health Insurance Database from years 2003–2013, this paper examines anatomical injuries and crash characteristics in back‐to‐traffic and facing‐traffic crashes. There were a total of 830 and 2267 pedestrian casualties in back‐to‐traffic and facing‐traffic crashes respectively. The injuries sustained by pedestrians and crash characteristics of these two crash types were compared with those of other crossing types of crashes (nearside crash, nearside dart‐out crash, offside crash, and offside dart‐out crash). Odds of various injuries to body regions were estimated using logistic regressions. Key findings include that the percentage of fatalities in back‐to‐traffic crashes is the highest; logistic models reveal that pedestrians in back‐to‐traffic crashes sustained more head, neck, and spinal injuries than did pedestrians in other crash types, and unlit darkness and non‐built‐up roadways were associated with an increased risk of pedestrian head injuries. Several crash features (e.g. unlit darkness, overtaking manoeuvres, phone use by pedestrians and drivers, and intoxicated drivers) are more frequently evident in back‐to‐traffic crashes than in other types of crashes. The current research suggests that in terms of crash consequence, facing traffic is safer than back to traffic. Copyright © 2016 John Wiley & Sons, Ltd.     

Real‐time identification of traffic conditions prone to injury and non‐injury crashes on freeways using genetic programming

This study applied the genetic programming (GP) model to identify traffic conditions prone to injury and property‐damage‐only (PDO) crashes in different traffic states on freeways. It was found that the traffic conditions prone to injury and PDO crashes can be classified into a high‐speed and a low‐speed traffic state. The random forest (RF) analyses were conducted to identify the contributing factors to injury and PDO crashes in these two traffic states. Four separate GP models were then developed to link the risks of injury and PDO crashes in two traffic states to the variables selected by the RF. An overall GP model was also developed for the combined dataset. It was found that the separate GP models that considered different traffic states and crash severity provided better predictive performance than the overall model, and the traffic flow variables that affected injury and PDO crashes were quite different across different traffic states. The proposed GP models were also compared with the traditional logistic regression models. The results suggested that the GP models outperformed the logistic regression models in terms of the prediction accuracy. More specifically, the GP models increased the prediction accuracy of injury crashes by 10.7% and 8.0% in the low‐speed and high‐speed traffic states. For PDO crashes, the GP models increased the prediction accuracy by 7.4% and 6.0% in the low‐speed and high‐speed traffic states. Copyright © 2016 John Wiley & Sons, Ltd.     

Effectiveness of red light cameras on the right‐angle crash involvement of motorcycles

Red light cameras (RLCs) have been used to reduce right‐angle collisions at signalized intersections. However, the effect of RLCs on motorcycle crashes has not been well investigated. The objective of this study is to evaluate the effectiveness of RLCs on motorcycle safety in Singapore. This is done by comparing their exposure, proneness of at‐fault right‐angle crashes as well as the resulting right‐angle collisions at RLC with those at non‐RLC sites. Estimating the crash vulnerability from not‐at‐fault crash involvements, the study shows that with a RLC, the relative crash vulnerability (RCV) or crash‐involved exposure of motorcycles at right‐angle crashes is reduced. Furthermore, field investigation of motorcycle maneuvers reveal that at non‐RLC arms, motorcyclists usually queue beyond the stop line, facilitating an earlier discharge, and hence become more exposed to the conflicting stream. However at arms with a RLC, motorcyclists are more restrained to avoid activating the RLC and hence become less exposed to conflicting traffic during the initial period of the green. The study also shows that in right‐angle collisions, the proneness of at‐fault crashes of motorcycles is lowest among all vehicle types. Hence motorcycles are more likely to be victims than the responsible parties in right‐angle crashes. RLCs have also been found to be very effective in reducing at‐fault crash involvements of other vehicle types which may implicate exposed motorcycles in the conflicting stream. Taking all these into account, the presence of RLCs should significantly reduce the vulnerability of motorcycles at signalized intersections. Copyright © 2010 John Wiley & Sons, Ltd.     

Rear‐end crash potential estimation in the work zone merging areas

This paper proposed a methodology to estimate rear‐end crash potential of the merging vehicles traveling in the merge lane, on the basis of the traffic data extracting from the available videotapes. First, we developed a binary logit model to identify drivers' merging behavior in the work zone merging area. Subsequently, the occurrence potential of rear‐end crash based on time‐to‐collision was computed between the merging vehicle and its neighboring vehicles. The overall crash potential of the merging vehicle was finally determined. It was found that the crash potential decreases with the remaining distance to work zone. Moreover, there will be a rear‐end crash potential of 4.0% if the merging vehicle fails to complete merging at the end of work zone merging area. If the merging vehicle takes an early merge, there will be a lower rear‐end crash potential (1.2%). These findings suggest that we should encourage merging vehicles to take early merges for improving the traffic safety in the work zone merging areas. Copyright © 2012 John Wiley & Sons, Ltd.     

A novel approach for assessing the LOS for two‐lane intercity highways under heterogeneous traffic conditions

Performance of two‐lane intercity highways has been evaluated in terms of level of service (LOS) by different researchers. Different follower‐related performance measures, namely, the number of followers (NF), percent followers (PF), follower density (FD) and the number of followers as a proportion of capacity (NFPC) are examined in the present study to define LOS. Data are collected from five sites located in different parts of India. While almost all the past studies used 3‐s headway rule to identify followers suggested by US Highway Capacity Manual, a new methodology is proposed in the current study to identify the followers by analysing speed difference (SD) and the gap between two consecutive vehicles. It is observed that vehicles travel in non‐following condition after a critical gap threshold value of 10 s. By using a SD limit of ?4 km/h to +10 km/h and a gap value of 10 s, followers are identified across all the study sites. Thereafter, different critical gap values ranging from 1.9 s to 4.3 s are observed at the study sites beyond which the probability of not following would increase. Variation in two‐way traffic volume is found to be the main contributory factor which affects the critical gap values. Among all of the performance measures, NFPC shows a strong correlation with two‐way traffic volume followed by FD under heterogeneous traffic condition. Finally, different threshold values of LOS ranges for two‐lane intercity highways are provided by carrying out cluster analysis with the help of NFPC and FD. Copyright © 2017 John Wiley & Sons, Ltd.     

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

A multiple correspondence analysis of at‐fault motorcycle‐involved crashes in Alabama

According to the U.S. National Highway Traffic Safety Administration, in 2012, more than 4950 motorcyclists were killed in traffic accidents. Compared to passenger car occupants, mile for mile, motorcyclists are more than 26 times more at risk to dying in crashes. Due to the high fatality rate associated with motorcycle crashes, factors contributing to this type of crash must be identified in order to implement effective safety countermeasures. Given that the available datasets are large and complex, identifying the key factors contributing to crashes is a challenging task. Using multiple correspondence analysis, as an exploratory data analysis technique to determine the dataset structure, we identified the roadway/environmental, motorcycle, and motorcyclist‐related variables influencing at‐fault motorcycle‐involved crashes. This study used the latest available dataset (2009 to 2013) from the Critical Analysis Reporting Environment database to study motorcycle crashes in the state of Alabama. The most significant contributors to the frequency and severity of at‐fault motorcycle‐involved crashes were found to be light conditions, time of day, driver condition, and weather conditions. Copyright © 2017 John Wiley & Sons, Ltd.     

Geographical unit based analysis in the context of transportation safety planning

A wide array of spatial units has been explored in macro-level modeling. With the advancement of Geographic Information System (GIS) analysts are able to analyze crashes for various geographical units. However, a clear guideline on which geographic entity should be chosen is not present. Macro level safety analysis is at the core of transportation safety planning (TSP) which in turn is a key in many aspects of policy and decision making of safety investments. The preference of spatial unit can vary with the dependent variable of the model. Or, for a specific dependent variable, models may be invariant to multiple spatial units by producing a similar goodness-of-fits. In this study three different crash models were investigated for traffic analysis zones (TAZs), block groups (BGs) and census tracts (CTs) of two counties in Florida. The models were developed for the total crashes, severe crashes and pedestrian crashes in this region. The primary objective of the study was to explore and investigate the effect of zonal variation (scale and zoning) on these specific types of crash models. These models were developed based on various roadway characteristics and census variables (e.g., land use, socio-economic, etc.).It was found that the significance of explanatory variables is not consistent among models based on different zoning systems. Although the difference in variable significance across geographic units was found, the results also show that the sign of the coefficients are reasonable and explainable in all models.Key findings of this study are, first, signs of coefficients are consistent if these variables are significant in models with same response variables, even if geographic units are different. Second, the number of significant variables is affected by response variables and also geographic units.Admittedly, TAZs are now the only traffic related zone system, thus TAZs are being widely used by transportation planners and frequently utilized in research related to macroscopic crash analysis. Nevertheless, considering that TAZs are not delineated for traffic crash analysis but they were designed for the long range transportation plans, TAZs might not be the optimal zone system for traffic crash modeling at the macroscopic level. Therefore, it recommended that other zone systems be explored for crash analysis as well.     

Queue length estimation at metered freeway‐to‐freeway connectors

Freeway‐to‐freeway connector metering is a cost‐effective and proven freeway management strategy for relieving recurrent congestion. However, one of the critical challenges in design and operation of freeway‐to‐freeway connector metering is the lack of up‐to‐date queue storage length design guidance. In this study, it was found that ramp queue is dynamically related to the metering rate, on‐ramp demand, and traffic flow arrival pattern. Hence, simply using an average demand cannot provide accurate queue length estimation and is also not suitable for queue storage design where the maximum or a percentile queue length is generally used. A mesoscopic queue length simulation model was developed based on the input–output method for estimating queue lengths under various demand‐to‐capacity ratio scenarios. Simulation results indicate that for under‐saturated situations, the ramp queue may exist temporally due to the random short‐term surge of traffic arrivals, and the exponential function could best capture the relationship between queue length and demand‐to‐capacity ratio. For over‐saturated situations, the ramp queue tends to prolong linearly with the demand‐to‐capacity ratio. Based on the simulation, it was recommended that queue storage length be designed as 4.3% of on‐ramp demand when demand is lower than 1200 vph or 2.3% when demand is between 1200 and 2400 vph. Copyright © 2016 John Wiley & Sons, Ltd.     

A method for relating type of crash to traffic flow characteristics on urban freeways

A method is developed to determine how crash characteristics are related to traffic flow conditions at the time of occurrence. Crashes are described in terms of the type and location of the collision, the number of vehicles involved, movements of these vehicles prior to collision, and severity. Traffic flow is characterized by central tendencies and variations of traffic flow and flow/occupancy for three different lanes at the time and place of the crash. The method involves nonlinear canonical correlation applied together with cluster analyses to identify traffic flow regimes with distinctly different crash taxonomies. A case study using data for more than 1000 crashes in Southern California identified twenty-one traffic flow regimes for three different ambient conditions: dry roads during daylight (eight regimes), dry roads at night (six regimes), and wet conditions (seven regimes). Each of these regimes has a unique profile in terms of the type of crashes that are most likely to occur, and a matching of traffic flow parameters and crash characteristics reveals ways in which congestion affects highway safety.     

A new lost time estimation method for right‐turn traffic in Japan considering signal phasing and sneakers

Precise estimation of the capacity for right‐turn traffic (comparable to left‐turn traffic in the USA) is of great importance to determine signal phasing schemes at signalized intersections in Japan, where the left‐hand driving rule is valid. However, in most signal timing procedures across the world, the lost time of right‐turn traffic is simply determined by the duration of intergreen intervals and thus lacks considerations of various signal phasing and driver behavior. Meanwhile, sneakers per cycle are usually applied to account for the number of drivers completing right turns during the effective red portion of the clearance‐and‐change intervals. As a result, an initial cycle length must be hypothesized in order to assess the total number of sneakers within the analysis period. Consequently, a time‐consuming iterative calculation process often becomes necessary. Therefore, the present study aims to develop a new lost time estimation method for right‐turn traffic to overcome the aforementioned drawbacks. Lost times of right‐turn traffic under three conventional phasing plans are theoretically formulated on the basis of a time–space diagram and shock‐wave theory. The new method is validated using field data, with case studies of its application in the signal timing procedure. Results indicated that the proposed method is capable of offering more accurate estimation than conventional approaches, which leads to shorter cycle length and simplifies signal timing process by eliminating an iterative check to determine the number of sneakers. Copyright © 2012 John Wiley & Sons, Ltd.     

Crash analysis at intersections in the CBD: A survival analysis model

Enhancing the safety level of urban roads especially in CBDs is paramount. Due to a large number of intersections in what is usually a grid road system in the CBDs, we investigate crashes occurring in and around an intersection. The question of interest in this study is: does the nature of crashes at intersections differ from those of the roads at midblock? Stated more precisely, considering the intersection as a reference point, does the distance to the reference point (i.e. midblock locations on the roads) correlate with different types of crashes compared to that of the intersection? A right answer can lead traffic engineers and safety auditors to propose different safety measures at intersections and the midblock locations. As a pilot study, we collected the last 9 years crash data of the CBD of Melbourne, Australia. For the first time, we employ Survival Analysis models -including Exponential, Weibull, and Log-logistic- to investigate a space-dependent phenomenon (i.e. accidents at proximity to the intersection). Of the outcome, highlights are: (i) police presence at busy intersections during busy night outs and weekends highly improves the pedestrian safety (ii) raised crossings at midblock locations lower likelihood of crashes of pedestrians as well as cars, (iii) lighting conditions at intersections must be watched and kept at a high level. (iv) Severity, likelihood, and location have no known association with the level of congestion. In other words, safety is first, always and everywhere. The results can be of interest to traffic authorities and policy makers in reinforcing traffic calming measures in the cities. The codes developed in this study are made available to the research community to be used in further studies.     

Optimal subwork zone operational strategy for short‐term work zone projects in four‐lane two‐way freeways

Roadside trees in Singapore are regularly trimmed for the purpose of traffic safety and roadside tree‐trimming project is one typical type of short‐term work zone projects. To implement such a short‐term work zone project, contractors usually divide an entire work zone into multiple subwork zones with the uniform length. This paper aims to determine an optimal subwork zone strategy for the short‐term work zone projects in four‐lane two‐way freeways with time window and uniform subwork zone length constraints. The deterministic queuing model is employed to estimate total user delay caused by the work zone project by taking into account variable traffic speeds. Based on the user delay estimations, this paper proceeds to build a minimization model subject to time window and uniform length constraints for the optimal subwork zone strategy problem. This paper also presents a variation of the minimization model to examine the impact of unequal subwork zone length constraint. Since these minimization models belong to the mixed‐integer non‐differentiable optimization problems, an iterative algorithm embedding with the genetic simulated annealing method is thus proposed to solve these models. Finally, a numerical example is carried out to investigate the effectiveness of the proposed models. Copyright © 2010 John Wiley & Sons, Ltd.     

Motorway crash duration and its determinants: do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems, which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard‐based models to develop in‐depth insights into how the crash‐specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland, and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, has been compared with random parameter AFT structures in terms of goodness of fit to the duration data, and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway 1 exhibits durations that are on average 19% shorter compared with the durations on motorway 2. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.     

Development of crash prediction models with individual vehicular data

Typical engineering research on traffic safety focuses on identifying either dangerous locations or contributing factors through a post-crash analysis using aggregated traffic flow data and crash records. A recent development of transportation engineering technologies provides ample opportunities to enhance freeway traffic safety using individual vehicular information. However, little research has been conducted regarding methodologies to utilize and link such technologies to traffic safety analysis. Moreover, traffic safety research has not benefited from the use of hurdle-type models that might treat excessive zeros more properly than zero-inflated models.This study developed a new surrogate measure, unsafe following condition (UFC), to estimate traffic crash likelihood by using individual vehicular information and applied it to basic sections of interstate highways in Virginia. Individual vehicular data and crash data were used in the development of statistical crash prediction models including hurdle models. The results showed that an aggregated UFC measure was effective in predicting traffic crash occurrence, and the hurdle Poisson model outperformed other count data models in a certain case.     

Modeling traffic operation at signalized intersections without explicit left‐turn yielding rules with an enhanced cell transmission model

This paper presents an enhanced cell transmission model (CTM) to capture traffic operation at signalized intersections without explicit permissive left‐turn yielding rules (i.e. aggressive permissive left‐turn maneuvers may not necessarily yield to opposing through traffic), which can be widely observed in many developing countries. Different from previous studies that focus on traffic dynamics on approaching links, this study contributes to modeling traffic operations within the intersection. A novel cell transmission framework with various types of virtual cells is proposed to model the dynamics of traffic movements from approach to exit. The unique phenomenon of competitive occupying of the conflict point between the left turn and opposing through movements is modeled. The cell state indicating its blockage is proposed to capture the dynamic queue formulation and dissipation and to evaluate the operational traffic performance at the intersection. Field validation results show that the proposed model can capture the operation of traffic at signalized intersections without explicit permissive left‐turn yielding rules with significantly higher level of accuracy than traditional traffic flow models. Copyright © 2016 John Wiley & Sons, Ltd.     

Factors contributing to the severity of intersection crashes

Road crashes are a leading cause of death and serious injuries both developed and developing countries. Intersections are recognized as being among the most hazardous locations on the roads. Although crashes at intersections form about 35 % of the reported accidents account for about 32% of traffic‐related serious injuries and fatalities in Singapore, there is no known study that examines the factors contributing to the severity of these crashes. In this study, the ordinal probit model was applied to crash data from 1992 to 2002 to investigate the role a variety of factors play in determining the severity of intersection crashes. Our study shows that vehicle type, road type, collision type, driver's characteristics and time of day are important determinants of the severity of crashes at intersections in Singapore.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.