A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

Are speed enforcement cameras more effective than other speed management measures? The impact of speed management schemes on 30 mph roads

This paper presents the results of an evaluation of the impact of various types of speed management schemes on both traffic speeds and accidents. The study controls for general trends in accidents, regression-to-mean effects and migration, separately estimating the accident changes attributable to the impact of the schemes on traffic speed and on traffic volume. It was found that, when judged in absolute terms, all types of speed management scheme have remarkably similar effects on accidents, with an average fall in personal injury accidents of about 1 accident/km/year. In terms of the percentage accident reduction, however, engineering schemes incorporating vertical deflections (such as speed humps or cushions) offer the largest benefits: at 44%, the average reduction in personal injury accidents attributable to such schemes, is twice that at sites where safety cameras were used to control speeds (22%) and they were the only type of scheme to have a significant impact on fatal and serious accidents. Other types of engineering scheme (with a fall of 29% in personal injury accidents) were on average less effective in reducing accidents than schemes with vertical features but more effective than cameras. All types of scheme were generally effective in reducing speeds, with the largest reductions tending to be obtained with vertical deflections and the smallest with other types of engineering schemes.     

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.     

Multiple-vehicle traffic accidents in Hong Kong

‘Multiple-vehicle traffic accident’ refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into “fatal/serious” and “slight”, a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.     

Freeway safety as a function of traffic flow

In this paper, we present evidence of strong relationships between traffic flow conditions and the likelihood of traffic accidents (crashes), by type of crash. Traffic flow variables are measured using standard monitoring devices such as single inductive loop detectors. The key traffic flow elements that affect safety are found to be mean volume and median speed, and temporal variations in volume and speed, where variations need to be distinguished by freeway lane. We demonstrate how these relationships can form the basis for a tool that monitors the real-time safety level of traffic flow on an urban freeway. Such a safety performance monitoring tool can also be used in cost-benefit evaluations of projects aimed at mitigating congestion, by comparing the levels of safety of traffic flows patterns before and after project implementation.     

Modeling traffic accident occurrence and involvement

The Negative Binomial modeling technique was used to model the frequency of accident occurrence and involvement. Accident data over a period of 3 years, accounting for 1,606 accidents on a principal arterial in Central Florida, were used to estimate the model. The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the section's length, on the frequency of accident occurrence. Several Negative Binomial models of the frequency of accident involvement were also developed to account for the demographic characteristics of the driver (age and gender). The results showed that heavy traffic volume, speeding, narrow lane width, larger number of lanes, urban roadway sections, narrow shoulder width and reduced median width increase the likelihood for accident involvement. Subsequent elasticity computations identified the relative importance of the variables included in the models. Female drivers experience more accidents than male drivers in heavy traffic volume, reduced median width, narrow lane width, and larger number of lanes. Male drivers have greater tendency to be involved in traffic accidents while speeding. The models also indicated that young and older drivers experience more accidents than middle aged drivers in heavy traffic volume, and reduced shoulder and median widths. Younger drivers have a greater tendency of being involved in accidents on roadway curves and while speeding.     

Evaluation and optimization of chromium removal from tannery effluent by microemulsion in the Morris extractor

A multidisciplinary Road Accident Analysis Group with the objective of conducting in-depth investigations of specific types of accidents has existed in Denmark for some years. The group has analysed head-on collisions, left-turn accidents, truck accidents and single vehicle accidents. The data collection included police reports, the group's investigation of accident sites and vehicles involved, and interviews with the involved road users and witnesses. The main accident factors in the head-on collisions and in the single vehicle accidents were excessive speed, drunk driving and driving under the influence of illegal drugs. The primary accident factors in left-turn accidents were attention errors or misjudging the amount of time available to complete the left turn. In the truck accidents insufficient searching for visual information as well as speeding were major factors. For all the accident themes the primary injury factor was failure to wear seat- belts. The multidisciplinary approach has provided a rather precise knowledge of the contributing factors leading up to the accident. The method requires a lot of resources, which is a limiting factor for the number of accidents to be analysed in this way. However, the method is suitable for analysis of common occurring or very serious types of accidents.     

Vehicle occupant injury severity on highways: An empirical investigation

Accident severity analysis is important to both researchers and practitioners because of its implications in accident cost estimation, external cost estimation and road safety. Although much research has been done to explore the factors influencing crash-injury severity, few studies have investigated the association between severity and traffic characteristics collected real-time during the time the accident occurred. We apply a random parameters ordered probit model to explore the influence of speed and traffic volume on the injury level sustained by vehicle occupants involved in accidents on the A4–A86 junction in the Paris region. Results indicate that increased traffic volume has a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions.     

Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong

A population-based case-control study was conducted to examine factors affecting the severity of single vehicle traffic accidents in Hong Kong. In particular, single vehicle accident data of three major vehicle types, namely private vehicles, goods vehicles and motorcycles, which contributed to over 80% of all single vehicle accidents during the 2-year-period 1999-2000, were considered. Data were obtained from the newly implemented traffic accident data system (TRADS), which was developed jointly by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. The effect of district, human, vehicle, safety, environmental and site factors on injury severity of an accident was examined. Unique risk factors associated with each of the vehicle types were identified by means of stepwise logistic regression models. For private vehicles, district board, gender of driver, age of vehicle, time of the accident and street light conditions are significant factors determining injury severity. For goods vehicles, seat-belt usage and weekday occurrence are the only two significant factors associated with injury severity. For motorcycles, age of vehicle, weekday and time of the accident were determined to be important factors affecting the injury severity. Identification of potential risk factors pertinent to the particular vehicle type has important implications to relevant official organisations in modifying safety measures in order to reduce the occurrence of severe traffic accidents, which would help to promote a safe road environment.     

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009–2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.     

Factors affecting the probability of bus drivers being at-fault in bus-involved accidents

Previous research has provided little insight into factors that influence the probability of bus drivers being at-fault in bus-involved accidents. In this study, an analysis was conducted on accident data compiled by a bus company that include an assessment on whether the bus driver was deemed by the company to hold primary responsibility for accident occurrence. Using a mixed logit modelling approach, roadway/environmental, vehicle and driver related variables that were identified to be influential were road type, speed limit, traffic/lighting conditions, bus priority, bus age/length and driver's age/gender/experience/historic at-fault accident record. Results were indicative of possible confined road-space issues that bus drivers face along routes with roadside traffic friction and point to the provision of exclusive right of way for buses as a possible way to address this. Results also suggest benefits in assigning routes comprising mainly divided roads as well as newer and shorter buses to less experienced drivers.     

Personality and behavioral predictors of traffic accidents: testing a contextual mediated model

A contextual mediated model was proposed to distinguish the distal (i.e. personality factors) and proximal (i.e. aberrant driving behaviors) factors in predicting traffic accident involvement. Turkish professional drivers (N=295) answered a questionnaire including various measures of personality factors, driver behaviors, and accident history. Results of the latent variable analysis with LISREL indicated that latent variables in the distal context (i.e. psychological symptoms, sensation seeking, and aggression) predicted at least one of the proximal elements (i.e. aberrant behaviors, dysfunctional drinking, and preferred speed) with relatively high path coefficients. While aberrant driver behaviors yielded a direct effect on accident involvement, psychological symptoms yielded an indirect effect mediated by driver behaviors. Further analyses revealed that personality factors had an impact on road accidents via their effects on actual driving-related behaviors although the path coefficients in predicting accidents were relatively weaker than those predicting risky driving behaviors and habits. Results were discussed considering the implications for classifying the accident correlates in a contextual framework and binominal-poisson distribution of self-reported accidents.     

Rough set approach for accident chains exploration

This paper presents a novel non-parametric methodology--rough set theory--for accident occurrence exploration. The rough set theory allows researchers to analyze accidents in multiple dimensions and to model accident occurrence as factor chains. Factor chains are composed of driver characteristics, trip characteristics, driver behavior and environment factors that imply typical accident occurrence. A real-world database (2003 Taiwan single auto-vehicle accidents) is used as an example to demonstrate the proposed approach. The results show that although most accident patterns are unique, some accident patterns are significant and worth noting. Student drivers who are young and less experienced exhibit a relatively high possibility of being involved in off-road accidents on roads with a speed limit between 51 and 79 km/h under normal driving circumstances. Notably, for bump-into-facility accidents, wet surface is a distinctive environmental factor.     

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.     

Multidisciplinary in-depth investigations of head-on and left-turn road collisions

To enhance traffic safety, a multidisciplinary Road Accident Investigation Team was established in Denmark for a 2-year trial period. The objective was to conduct in-depth investigations of specific types of accidents, and to identify effective preventive measures. The team consisted of a road engineer, a vehicle inspector, a police superintendent, a psychologist and a physician. Seventeen serious head-on collisions as well as 17 left-turn collisions were analysed. In collecting data, police reports were supplemented by the team's investigation of accident sites and vehicles involved, and interviews were carried out with the involved road users and witnesses. The drivers, to whom the accident factors were primarily related in the head-on collisions, were characterised by their conscious risk-taking behaviour. They were all males; several of them were under age 40 and had earlier traffic and/or drug convictions. The main accident factors were excessive speed, drunk driving and driving under the influence of illegal drugs. In the left-turn accidents, the most common accident factors were attention errors, and it was also noted that elderly drivers ( > 74) were over-represented. The synergy effect of working as a multidisciplinary team proved fruitful. It resulted in a more precise knowledge of the road accident circumstances and of contributing factors leading up to the accidents. Due to the great demand on resources, only a limited number of accidents could be analysed, but the results provide a basis for further and more targeted research.     

Suggestions for an improved system for statistical data about road accidents and road traffic

Data information systems for road accidents and road traffic must satisfy high standards of relevance and quality. The general outline of an improved system for collecting road accident data is given. The system is characterized by the use of statistical sampling methods. The police, the insurance companies and the hospitals are recommended as sources of information about the total accident population. A statistical sample of all identified accidents is then investigated in more detail by special local investigation groups. A hypothetical numerical example is given to show how the suggested system would work in practise. Road accident data should not be isolated from road traffic data. An improved system for collecting information on road traffic is also discussed. This consists of a basic system (founded on statistical sampling methods) for estimation of the total volume of traffic and a few other essential variables such as the volume divided into speed and vehicle types.     

The effect of roundabout design features on cyclist accident rate

Roundabouts are known to result in fewer traffic accidents than traditional intersections. However, this is to a lesser degree true for bicycles than for vehicles. In this paper, we aimed at establishing statistical relationships through Poisson regression and logistic regression analyses between yearly rate of cyclist accidents on one hand and roundabout geometry, age and traffic volume (vehicles and cyclists) on the other. We related all roundabout cyclist accidents recorded by the hospital emergency department of the town of Odense, Denmark, through the years 1999-2003 (N=171) to various geometric features, age and traffic volume of all roundabouts on the Danish island of Funen (N=88). Cyclist and vehicle volumes turned out to be significant predictors in most of our models-the higher the volumes, the more accidents. Moreover, potential vehicle speed was a significant predictor, and so was age of the roundabout-older roundabouts related to more accidents and higher accident probability. Excluding 48 single cyclist accidents strengthened the relationship between accidents on one hand and vehicle and cyclist volume and potential vehicle speed on the other. This stresses the significance of speed and traffic volume for traffic accidents with more than one partner involved. The 48 single cyclist accidents were significantly related to the traffic volume of cyclists only. Due to our limited number of observations, the models should be regarded as indicative.     

基于支持向量机的高速公路事故实时风险预测

以高速公路事故数据、交通流数据和天气数据为基础,以交通流为事故主要影响因素,建模预测高速公路事故实时风险。将事故记录作为病例组,采用病例对照方法来配对匹配实验样本,通过随机森林算法从众多变量中筛选出对事故风险影响最重要的10 个特征变量,以支持向量机建立模型预测事故实时风险。实验表明,通过随机森林筛选重要的特征变量,再使用支持向量机建模预测事故风险具有可行性,且以高斯核、Sigmoid核作为支持向量机的核函数比线性核函数和多项式核函数时分类准确性更高;其中,高斯核下支持向量机模型对事故风险预判的准确率达73.20%,对正常交通流的分类达91.44%。     

Safety of freeway median high occupancy vehicle lanes: a comparison of aggregate and disaggregate analyses

This paper addresses safety issues associated with High Occupancy Vehicle (HOV) lanes constructed along freeway medians, without physical separation from adjacent traffic. Data associated with operation of such an HOV facility in Southern California are analyzed relative to the pattern of accidents on the facility and the potential role of congestion. Detailed analyses of accident characteristics point out that potentially false conclusions regarding the safety of HOV lanes can be drawn from simple analyses that are based on aggregate measures of accident frequencies and assumed traffic volumes.     

Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction

The concept of crash precursor identification is gaining more practicality due to the recent advancements in Advanced Transportation Management and Information Systems. Investigating the shortcomings of the existing models, this paper proposes a new method to model the real time crash likelihood based on loop data through schematic eigenvectors. Firstly, traffic volume, occupancy and density spatiotemporal schematics in certain duration before an accident occurrence were constructed to describe the traffic flow status. Secondly, eigenvectors and eigenvalues of the spatiotemporal schematics were extracted to represent traffic volume, occupancy and density situation before the crash occurrence. Thirdly, by setting the vectors in crash time as case and those at crash free time as control, a logistic model is constructed to identify the crash precursors. Results show that both the eigenvectors and eigenvalues can significantly impact the accident likelihood compared to the previous study, the proposed model has the advantage of avoiding multicollinearity, better reflection of the overall traffic flow status before the crash, and improving missing data problem of loop detectors.