Bikeability in Metropolitan Areas

There have been several techniques for measuring bikeability; however, limited comprehensive research has been conducted focusing on travel distance as an important barrier for cyclists. Furthermore, existing measurements are mainly restricted by the availability of travel behaviour data. In this chapter, a new index for measuring bikeability in metropolitan areas is presented. The Cycling Accessibility Index (CAI) has been developed for computing cycling accessibility within Melbourne metropolitan, Australia. The CAI is defined consistent with gravity-based measures of accessibility. This index measures cycling accessibility levels considering mixed use developments as well as travel distance between origins and destinations. The Victorian Integrated Survey of Travel and Activity (VISTA) dataset was used to assess the proposed index and investigate the association between cycling accessibility levels and number of bicycle trips in local areas. Key findings indicate that there is a significant positive association between bicycle trips and the CAI.

Applying Decision Tree Approaches on Vehicle-Pedestrian Crashes

In the Melbourne metropolitan area in Australia, an average of 34 pedestrians were killed in traffic accidents every year between 2004 and 2013, and vehicle-pedestrian crashes accounted for 24% of all fatal crashes. Mid-block crashes accounted for 46% of the total pedestrian crashes in the Melbourne metropolitan area and 49% of the pedestrian fatalities occurred at mid-blocks. Many studies have examined factors contributing to the frequency and severity of vehicle-pedestrian crashes. While many of the studies have chosen to focus on crashes at intersections, few studies have focussed on vehicle-pedestrian crashes at mid-blocks. Since the factors contributing to vehicle crashes at intersections and mid-blocks are significantly different, more research needs to be done to develop a model for vehicle-pedestrian crashes at mid-blocks. In order to identify factors contributing to the severity of vehicle-pedestrian crashes, three models using different decision trees (DTs) were developed. To improve the accuracy, stability, and robustness of the DTs, bagging and boosting techniques were used in this chapter. The results of this study show that the boosting technique improves the accuracy of individual DT models by 46%. Moreover, the results of boosting DTs (BDTs) show that neighbourhood social characteristics are as important as traffic and infrastructure variables in influencing the severity of pedestrian crashes.

Walkability in Metropolitan Area

Promoting active trips has been considered as a key element towards achieving more sustainable transportation. Walking as a mode of transportation can contribute to more sustainable and healthy travel habits. This chapter presents a new approach for measuring walkability within Melbourne region, Australia. An integrated approach combining transport and land-use planning concepts was employed to construct the walking access index (WAI), which is a location-based measure for accessibility. The WAI along with a common existing walkability index were employed in regression models to examine how the new index performs in transport modelling. Key findings indicate that residents are more likely to have walking trips when living in a more walkable environment. Furthermore, it was found using statistical modelling that the WAI produces better results than one of the common approaches.

Measuring Public Transport Accessibility in Metropolitan Area

Improving access to public transport can be considered an effective way of reducing the negative side-effects of motorised commuting. This chapter used the large dataset of Victorian Integrated Survey of Travel and Activity (VISTA) to introduce a new approach measuring public transport accessibility within the Melbourne region, Australia. A public transport accessibility index (PTAI) is a combined measure of public transport service frequency and population density as an important distributional indicator. Although many studies have measured access levels to public transport stops/stations, there has been limited research on accessibility that integrates population density within geographical areas. Employing geographical information system (GIS), a consistent method is introduced for evaluating public transport accessibility for different levels of analysis, from single elements, including public mode stops, to network analysis. The proposed index is compared with two common existing approaches using regression models. Key findings indicate that the PTAI has a stronger association whilst showing more use of public transport in areas with higher values of the PTAI.

Neighbourhood Influences on Vehicle-Pedestrian Crash Severity

Socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area, 20% of pedestrians, 11% of drivers, and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live. In this chapter, neighbourhood factors associated with road users' residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. The authors found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.

Contributing Factors on Vehicle-Pedestrian Crash Severity of School-Aged Pedestrians

Every year, about 19% of vehicle-pedestrian crashes in Melbourne metropolitan area, Australia, involve pedestrians less than 18 years of age or school-aged pedestrians. This chapter aims to identify contributing factors on vehicle-pedestrian crash severity of this age group. Reasonable walking distance to schools is applied in geographic information systems (GIS) to identify vehicle-pedestrian crashes around schools. Then boosted decision tree (BDT) and cross-validation (CV) technique are applied to explore significant factors. Results show that the distance of pedestrians from school is a significant factor on vehicle-pedestrian crash severity for this age group. This result could assist in identifying a safe distance and safe zone around schools. Furthermore, public health indicators such as income and commuting type from or to school are found as other contributing factors to this crash type.

Spatial and Temporal Distribution of Pedestrian Crashes

In order to develop effective and targeted safety programs, the location and time-specific influences on vehicle-pedestrian crashes must be assessed. Therefore, spatial autocorrelation was applied to the examination of vehicle-pedestrian crashes in geographic information systems (GISs) to identify any dependency between time and location of these crashes. Spider plotting and kernel density estimation (KDE) were then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and gender types. Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, the results of the spatial analysis show that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and gender types. For example, for the age group between 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 pm and 6:00 am, crashes for this age group occur mostly around hotels, clubs, and bars. Therefore, specific safety measures should be implemented during times of high crash risk at different locations for different age groups and gender types, in order to increase the effectiveness of the countermeasures in preventing and reducing the vehicle-pedestrian crashes.