Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

Reducing rear-end crashes with cooperative systems

This paper presents an evaluation of the effectiveness of a cooperative Intelligent Transport System (C-ITS) to reduce rear-end crashes. Two complementary simulation techniques are used to demonstrate the benefits of the C-ITS. A traffic (VEINS) and sensor (SiVIC) simulations use realistic data related to traffic/road in Brisbane’s Pacific Motorway, driver’s reaction time and injury severity to evaluate benefits. The results of our simulations show that C-ITS could reduce rear-end crash risk by providing several seconds of additional warning to drivers.

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, have been compared to 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 exhibits durations that are on average 19% shorter compared to the durations on motorway. 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.

The influence of high contrast acuity and normalised low contrast acuity upon self-reported situation avoidance and driving crashes

The aim of this study was to determine the cues used to signal avoidance of difficult driving situations and to test the hypothesis that drivers with relatively poor high contrast visual acuity (HCVA) have fewer crashes than drivers with relatively poor normalised low contrast visual acuity (NLCVA). This is because those with poorer HCVA are well aware of their difficulties and avoid dangerous driving situations while those poorer NLCVA are often unaware of the extent of their problem. Age, self-reported situation avoidance and HCVA were collected during a practice based study of 690 drivers. Screening was also carried out on 7254 drivers at various venues, mainly motorway sites, throughout the UK. Age, self-reported situation avoidance and prior crash involvement were recorded and Titmus vision screeners were used to measure HCVA and NLCVA. Situation avoidance increased in reduced visibility conditions and was influenced by age and HCVA. Only half of the drivers used visual cues to signal situation avoidance and most of these drivers used high rather than low contrast cues. A statistical model designed to remove confounding interrelationships between variables showed, for drivers that did not report situation avoidance, that crash involvement decreased for drivers with below average HCVA and increased for those with below average NLCVA. These relationships accounted for less than 1% of the crash variance, so the hypothesis was not strongly supported. © 2002 The College of Optometrists.

At work or play : a comparison of private property vehicle crashes with those occurring on public roads in north Queensland.

Objective: To define characteristics of vehicle crashes occurring on rural private property in north Queensland with an exploration of associated risk factors. Design: Descriptive analysis of private property crash data collected by the Rural and Remote Road Safety Study. Setting: Rural and remote north Queensland. Participants: A total of 305 vehicle controllers aged 16 years or over hospitalised at Atherton, Cairns, Mount Isa or Townsville for at least 24 hours as a result of a vehicle crash. Main outcome measure: A structured questionnaire completed by participants covering crash details, lifestyle and demographic characteristics, driving history, medical history, alcohol and drug use and attitudes to road use. Results: Overall, 27.9% of interviewees crashed on private property, with the highest proportion of private road crashes occurring in the North West Statistical Division (45%). Risk factors shown to be associated with private property crashes included male sex, riding off-road motorcycle or all-terrain vehicle, first-time driving at that site, lack of licence for vehicle type, recreational use and not wearing a helmet or seatbelt. Conclusions: Considerable trauma results from vehicle crashes on rural private property. These crashes are not included in most crash data sets, which are limited to public road crashes. Legislation and regulations applicable to private property vehicle use are largely focused on workplace health and safety, yet work-related crashes represent a minority of private property crashes in north Queensland.

Fatal motorcycle crashes in north Queensland : characteristics and potential interventions

Aims: The Rural and Remote Road Safety Study (RRRSS) addresses a recognised need for greater research on road trauma in rural and remote Australia, the costs of which are disproportionately high compared with urban areas. The 5-year multi-phase study with whole-of-government support concluded in June 2008. Drawing on RRRSS data, we analysed fatal motorcycle crashes which occurred over 39 months to provide a description of crash characteristics, contributing factors and people involved. The descriptive analysis and discussion may inform development of tailored motorcycle safety interventions. Methods: RRRSS criteria sought vehicle crashes resulting in death or hospitalisation for 24 hours minimum of at least 1 person aged 16 years or over, in the study area defined roughly as the Queensland area north from Bowen in the east and Boulia in the west (excluding Townsville and Cairns urban areas). Fatal motorcycle crashes were selected from the RRRSS dataset. Analysis considered medical data covering injury types and severity, evidence of alcohol, drugs and prior medical conditions, as well as crash descriptions supplied by police to Queensland Transport on contributing circumstances, vehicle types, environmental conditions and people involved. Crash data were plotted in a geographic information system (MapInfo) for spatial analysis. Results: There were 23 deaths from 22 motorcycle crashes on public roads meeting RRRSS criteria. Of these, half were single vehicle crashes and half involved 2 or more vehicles. In contrast to general patterns for driver/rider age distribution in crashes, riders below 25 years of age were represented proportionally within the population. Riders in their thirties comprised 41% of fatalities, with a further 36% accounted for by riders in their fifties. 18 crashes occurred in the Far North Statistical Division (SD), with 2 crashes in both the Northern and North West SDs. Behavioural factors comprised the vast majority of contributing circumstances cited by police, with adverse environmental conditions noted in only 4 cases. Conclusions: Fatal motorcycle crashes were more likely to involve another vehicle and less likely to involve a young rider than non-fatal crashes recorded by the RRRSS. Rider behaviour contributed to the majority of crashes and should be a major focus of research, education and policy development, while other road users’ behaviour and awareness also remains important. With 68% of crashes occurring on major and secondary roads within a 130km radius of Cairns, efforts should focus on this geographic area.

All-terrain vehicle crashes and associated injuries in North Queensland : findings from the Rural and Remote Road Safety Study

Objective: To define characteristics of all-terrain vehicle (ATV) crashes occurring in north Queensland from March 2004 till June 2007 with the exploration of associated risk factors. Design: Descriptive analysis of ATV crash data collected by the Rural and Remote Road Safety Study. Setting: Rural and remote north Queensland. Participants: Forty-two ATV drivers and passengers aged 16 years or over hospitalised at Atherton, Cairns, Mount Isa or Townsville for at least 24 hours as a result of a vehicle crash. Main outcome measures: Demographics of participants, reason for travel, nature of crash, injuries sustained and risk factors associated with ATV crash. Results: The majority of casualties were men aged 16–64. Forty-one per cent of accidents occurred while performing agricultural tasks. Furthermore, 39% of casualties had less than one year’s experience riding ATVs. Over half the casualties were not wearing a helmet at the time of the crash. Common injuries were head and neck and upper limb injuries. Rollovers tended to occur while performing agricultural tasks and most commonly resulted in multiple injuries. Conclusions: Considerable trauma results from ATV crashes in rural and remote north Queensland. These crashes are not included in most general vehicle crash data sets, as they are usually limited to events occurring on public roads. Minimal legislation and regulation currently applies to ATV use in agricultural, recreational and commercial settings. Legislation on safer design of ATVs and mandatory courses for riders is an essential part of addressing the burden of ATV crashes on rural and remote communities.

The on-road difficulties of older drivers and their relationship with self-reported motor vehicle crashes

OBJECTIVES: To quantify the driving difficulties of older adults using a detailed assessment of driving performance and to link this with self-reported retrospective and prospective crashes. DESIGN: Prospective cohort study. SETTING: On-road driving assessment. PARTICIPANTS: Two hundred sixty-seven community-living adults aged 70 to 88 randomly recruited through the electoral roll. MEASUREMENTS: Performance on a standardized measure of driving performance. RESULTS: Lane positioning, approach, and blind spot monitoring were the most common error types, and errors occurred most frequently in situations involving merging and maneuvering. Drivers reporting more retrospective or prospective crashes made significantly more driving errors. Driver instructor interventions during self-navigation (where the instructor had to brake or take control of the steering to avoid an accident) were significantly associated with higher retrospective and prospective crashes; every instructor intervention almost doubled prospective crash risk. CONCLUSION: These findings suggest that on-road driving assessment provides useful information on older driver difficulties, with the self-directed component providing the most valuable information.

Application of text mining in analysing road crashes for road asset management

Traffic safety is a major concern world-wide. It is in both the sociological and economic interests of society that attempts should be made to identify the major and multiple contributory factors to those road crashes. This paper presents a text mining based method to better understand the contextual relationships inherent in road crashes. By examining and analyzing the crash report data in Queensland from year 2004 and year 2005, this paper identifies and reports the major and multiple contributory factors to those crashes. The outcome of this study will support road asset management in reducing road crashes.

An analysis of cyclists crashes to identify ITS-based intervention

Cycling provides a number of health and environmental benefits. However, cyclists are more likely to suffer serious injury or be killed in traffic accidents than car drivers and the estimated cost of crashes in Australia is $1.25AU billion per year. Current interventions to reduce bicycle crashes include compulsory helmet use, media campaigns, and the provision of cycling lanes, as well as road user education and training. It is difficult to assess the effectiveness of current interventions as there is no accurate measure of cyclist exposure in South East Queensland (SEQ). This paper analyses cyclist crash characteristics in Queensland with the view to identifying appropriate Intelligent Transport Systems (ITS) based intervention to reduce cyclist injury and death. The inappropriateness of some ITS interventions to improve cyclist safety is highlighted and a set of ITS interventions are identified, based on Queensland crash data 2002-2006.