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• Assessed crash frequency across zoning scales, justifying the use of traffic density-based zones rather than planning-based zones. • Integrated dynamic traffic flow variables, notably shockwave velocity and congestion index into the crash frequency model. • Identified notable built environment factors from various dimensions impacting crash occurrence. • Uncovered the heterogeneity effect of dynamic traffic flow characteristics on crash frequency. While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks. [ABSTRACT FROM AUTHOR]

• Bus crashes are investigated by crash dataset of the State of Victoria, Australia. • Data mining used to discover chain of factors that lead to fatality in bus crashes. • Bus crashes with non-motor vehicles and on weekends increase fatality risk. • Old driver, intersection and vulnerable user increase fatality in non-motor crashes. • Fatal crash on weekend is linked to darkness, speed, pedestrian and female driver. Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians' distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas. [ABSTRACT FROM AUTHOR]

Network planning of bus services requires addressing the trade-off between frequency and coverage. Traditional network planning has focused on coverage using the rule of thumb that people will walk four hundred meters to access bus based public transport services. More modern approaches have recognised that encouraging mode shift means improving quality and thus in the presence of budget constraint, a shift of resources is needed to provide better frequency or smaller headways on core corridors. Using a Stated Choice experiment approach, this paper elicits the trade-off between access distance and headways and how this might vary in a number of cities around the world. The key results confirm travellers in all cities forming part of the sample are willing to walk further for a more frequent service: in Australian capital cities between 226 m and 302 m further for a 10-min reduction in bus headways, while in UK and US this is between 370 m and 475 m further for the same improvement in bus headway. The research provides a consistent valuation for the design attributes of service (such as distance to stop, journey time, headway/frequency and crowding) over eleven cities together with an estimation of the role of socio demographics in tempering how far passengers are likely to walk to access a high frequency corridor and potentially achieve mode shift from the private car. [ABSTRACT FROM AUTHOR]

This work builds upon the thought that individuals allocate higher levels of importance to some particular features of the route, so called anchor points. Previous route choice models have either ignored the effects of anchor points (route-based models), or have given an exclusive attention to their effects and ignored the behavioral accuracy and practicality of these models (anchor-based models). In this work we argue that the consideration of both route-level attributes and anchor points would enhance the behavioral aspect of route choice models as well as their estimation and prediction abilities. Global Positioning System traces have been used to investigate the effect of bridges as anchor points for trips between Montreal and its Northern suburb, Laval. A classic Nested Logit and a nested Logit Kernel model have been estimated, in which interdependencies among routes crossing the same bridge are captured through the nested structure and the adopted factor analytic approach, respectively. A Metropolis-Hastings path-sampling algorithm is applied, for the first time, on a large road network with more than 40,000 nodes and 19,000 links to provide the consideration choice set. Estimates are then compared to three alternate models, representing route-based and anchor-based formulations; namely Path-Size Logit, Extended Path-Size Logit, and Independent Availability Logit models. Empirical results showed that the proposed nested structures with MH sampling provide better estimates and also perform better in the validation step with respect to comparative models. Findings underscore the importance of considering anchor points in conjunction with route level attributes in route choice decisions. [ABSTRACT FROM AUTHOR]

The travel time reliability of buses has become increasingly important for public transit companies. In this study, a novel approach is proposed to evaluate and analyse the travel time reliability of bus services provided by TransLink in Queensland, Australia. In view of their stochastic features, the two components of travel time - dwell time and driving time - are represented by discrete distributed and normally distributed random variables respectively. Accordingly, the travel time could be described by Gaussian mixture models. Based on the proposed model, impact analysis shows that bus line reliability would increase by around 15% if onboard top-up for 'go cards' (electronic tickets) was not offered by TransLink. It was found that not providing this top-up method would not significantly harm the benefit of go card users, but it would substantially increase the total social benefit thanks to improved bus line reliability. [ABSTRACT FROM AUTHOR]

Information about bus travel time and its variability is a key indicator of service performance, and it is valued by passengers and operators. Despite the important effect of traffic flow on bus travel time, previous predictive approaches have not fully considered a traffic measure making their predictions unresponsive to the dynamic changes in traffic congestion. In addition, existing methodologies have primarily concerned predicting average travel time given a certain set of input values. However, predicting travel-time variability has not received sufficient attention in previous research. This article proposes an integrated framework to predict bus average travel time and its variability on the basis of a range of input variables including traffic flow data. The framework is applied using GPS-based travel-time data for a bus route in Melbourne, Australia, in conjunction with dynamic traffic flow data collected by the Sydney Coordinated Adaptive Traffic Systems loop detectors and a measure of schedule adherence. Central to the framework are two artificial neural networks that are used to predict the average and variance of travel times for a certain set of input values. The outcomes are then used to construct a prediction interval corresponding to each input value set. The article demonstrates the ability of the proposed framework to provide robust prediction intervals. The article also explores the value that traffic flow data can provide to the accuracy of travel-time predictions compared with when either temporal variables or scheduled travel times are the base for prediction. While the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used. [ABSTRACT FROM AUTHOR]

In this article the author discusses the experiences of a visit to Hope Vale, an Aboriginal community located in Queensland. Commentary on life at Hope Vale is provided. A description of the journey to the community taken by bus is given. Other topics include a fight between residents and visitors to the community.

Migratory birds travel over long distances twice a year throughout the course of their lives. Knowledge on the routes and travel patterns among linked areas is essential for migratory bird conservation and international population monitoring. In this study, we tracked the full migration of three White-throated Needletails (Hirundapus caudacutus) breeding in northern Japan using light-level geolocators. The migration routes for all the individuals were similar and followed a "figure 8" pattern overall. The destination of all the birds was eastern Australia. This migration route between northern Japan and eastern Australia follows the land arrangements in East Asia to Australia to some extent, but long-distance offshore movements were also observed. None of the three individuals had a notably constant location during the period in Australia. The migration routes of two of the birds were similar. This is the first report on the migration of a swift species in the East Asian–Australasian Flyway. [ABSTRACT FROM AUTHOR]