Modelling passenger waiting time using large-scale automatic fare collection data: an Australian case study

Passenger waiting time at transit stops is an important component of overall travel time and is perceived to be less desirable than in-vehicle travel time or access time. Therefore, an accurate model to estimate waiting time is necessary to better plan for transit and to improve patronage. The majority of previous studies on waiting time have either made very limiting assumptions on the arrival distribution of passengers or lacked a large-scale and high-quality dataset. The smartcard fare collection system in South-East Queensland, Australia, has provided the opportunity of very large-scale and highly accurate data on passenger boarding and alighting times and locations. In this research, all 130,000 daily rail passengers in all 145 stations of a network are considered. First a methodology is developed to match each individual passenger with the most likely rail service he/she boarded. Then, a hazard-based duration modelling approach is adapted to model passenger waiting time as a function of a variety of factors that influence waiting time. Log-logistic accelerated failure time (AFT) models are inferred to be appropriate among the models tested. The results indicate that: (a) the waiting time can be predicted accurately at various confidence levels; (b) the waiting time at all network stations can be predicted with a single model; and (c) a wide range of influencing parameters are statistically significant in the model, which can be categorized to temporal, infrastructure and operation, demographics, and trip characteristics parameters. The results of this study can be used for demand estimation, operational analysis, transit scheduling, and network design through an understanding of the effects of influential variables on waiting time

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

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

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