Quantifying the impacts of traffic incidents on urban freeway speeds

Planning for traffic related congestion during peak periods continues to be one of the most important challenges facing road managers. Congestion may be thought of as either recurrent or non-recurrent. The latter is caused by factors such as incidents, work zones, weather, and special events. Traffic incidents are reported as the cause of 25 per cent of total delays in the US. However, its effect varies from place to place due to the local conditions. Different types of traffic incidents affect drivers' behaviour and the performance of vehicles. In an incident situation, the average headway between vehicles and the speed variability increases. The resultant impact on road speed profile is the main topic of the paper. The paper describes the methodology of extracting the impacts of traffic incidents including duration and delay on traffic speed for an urban freeway network. The analysis of data from a case study in Brisbane is reported here. A section of an urban freeway has been studied in detail using inductive loop detector data and traffic incidents related variables for a period of 12 months. A variety of probability distribution functions were employed in order to test the best model for the duration and delay frequency distribution for each category of incident. The findings of this research will be used to put forward improved predictive delay models and travel time reliability models for urban freeway conditions

Hazard based models for freeway traffic incident duration

Assessing and prioritising cost-effective strategies to mitigate the impacts of traffic incidents and accidents on non-recurrent congestion on major roads represents a significant challenge for road network managers. This research examines the influence of numerous factors associated with incidents of various types on their duration. It presents a comprehensive traffic incident data mining and analysis by developing an incident duration model based on twelve months of incident data obtained from the Australian freeway network. Parametric accelerated failure time (AFT) survival models of incident duration were developed, including log-logistic, lognormal, and Weibul - considering both fixed and random parameters, as well as a Weibull model with gamma heterogeneity. The Weibull AFT models with random parameters were appropriate for modelling incident duration arising from crashes and hazards. A Weibull model with gamma heterogeneity was most suitable for modelling incident duration of stationary vehicles. Significant variables affecting incident duration include characteristics of the incidents (severity, type, towing requirements, etc.), and location, time of day, and traffic characteristics of the incident. Moreover, the findings reveal no significant effects of infrastructure and weather on incident duration. A significant and unique contribution of this paper is that the durations of each type of incident are uniquely different and respond to different factors. The results of this study are useful for traffic incident management agencies to implement strategies to reduce incident duration, leading to reduced congestion, secondary incidents, and the associated human and economic losses

Using a driving simulator to assess driver compliance at railway level crossings

Railway level crossings have the potential to bring motor vehicles and trains into fatal contact. In Australia there are approximately 9,400 public railway level crossings across the country, protected either passively (64%) or by active/automated systems (28%). Passive crossings provide only a stationary sign warning of the possibility of trains crossing. Their message remains constant over time. Active systems, by contrast, activate automatic warning devices (i.e., flashing lights, bells, barrier, etc.) as a train approaches. Using a driving simulator, this paper compares driver compliance at railway level crossings equipped with either active or passive warning devices including a stop sign, rumble strips, flashing lights/bell and in-vehicle auditory warning. This paper describes the driving simulator data collection and findings and subsequently draws conclusions on driver compliance with respect to different types of warning devices. The results indicate that drivers behave differently and are more compliant at active crossings than at passive crossings

Modeling bus travel time reliability with supply and demand data from automatic vehicle location and smart card systems

Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads

An analysis of traffic incidents on an Australian urban road network

Assessing and prioritising cost-effective strategies to mitigate the impact of traffic incidents on non-recurrent congestion on major roads are currently a major challenge for road network operations. There is a lack of relevant local research in this area. Several incident duration models developed from international research are not considered appropriate for Australian conditions due to different driver behaviour and traffic environment contexts. A comprehensive data mining research project was undertaken to analyse traffic incident data, obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for a one year period ending in November 2010. Various factors that contributed to frequency, type, characteristics, duration and location of traffic incidents were examined and the findings are discussed in this paper. Results indicate that breakdown, multiple vehicle crash and debris were the major sources of incidents. Although incident frequency dropped sharply on weekends, the average incident duration was similar or longer than those of weekdays. Also, rainfall increased the incident duration in all categories. Furthermore, a variety of probability distribution functions were employed in order to test the best model for each category of incident duration frequency distribution. Log-normal distribution was inferred to be appropriate for crash and stationary vehicle incidents and gamma distribution for hazard incidents. Future research directions have been identified, particularly the estimation of the impact (cost) of traffic incidents, to assist in prioritising investment

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts

Analysing freeway traffic incident duration using an Australian data set

This paper investigates incident duration and identifies contributing variables for Australian conditions. The paper presents a new framework for comprehensive traffic-incident data mining and analysis towards an incident delay model and travel-time reliability modelling. Twelve months of data were collected, analysed and the results are presented in this paper. The findings suggest that debris, breakdown and multiple-vehicle crashes are the major sources of incidents on freeways. Furthermore, freeway incident duration varied across the types of incident and time of the day, and whether it was a week day or weekend day. However, there were no significant differences in relation to day, week or month of the year. Significant variables on incident duration were identified using an ANOVA test for each type of incident. In addition, the findings of this study reveal a high variance of incident duration within each incident type. A variety of probability distribution functions were employed to test the best model for the duration frequency distribution for each category of incident. Log-normal distribution was found to be more appropriate for crashes, but log-logistic distribution was more appropriate for hazards and stationaryvehicle incidents

Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics