This paper presents a GIS approach based on spatial autocorrelation analysis of pedestrian-vehicle crash data for identification and ranking of unsafe bus stops. Instead of crash counts, severity indices are used for analysis and ranking. Moran's I statistic is employed to examine spatial patterns of pedestrian-vehicle crash data. Getis-Ord Gi* statistic is used to identify the clustering of low and high index values and to generate a pedestrian-vehicle crash hot spots map. As recent studies have shown strong correlations between pedestrian-vehicle crashes and transit access, especially bus stops, bus stops in pedestrian-vehicle crash hot spots are then selected and ranked based on the severity of pedestrian-vehicle crashes in their vicinities. The proposed approach is evaluated using 13 years (1996–2008) of pedestrian-vehicle crash data for the Adelaide metropolitan area. Results show that the approach is efficient and reliable in identifying pedestrian-vehicle crash hot spots and ranking unsafe bus stops.

Urban mobility options have increased in recent years, assisted by the widespread availability of smart device software apps, geo-positioning technology, and convenient electronic financial transactions. Multi-modal shared mobility consists of public transit systems and shared mobilities that support first/last mile travel, denoting the capability of Mobility as a Service (MaaS), and to stimulate additional non-private car travel demand. This paper reviews the supply and demand sides of implementation of multi-modal shared mobility. It found that an abundance of shared modes of car, bike, and e-scooter that are linked to public transport, can improve transport accessibility to meet specific public preferences, reduce social inequality, and minimise dilemmas from the demand side. This study introduces government policy innovations and other supporting system to improve the implementation of multi-modal shared mobility. Government policies play a key role in supporting shared mobility and technology development. However, governments do not have much information about new products such as shared mobility, which creates difficulties in subsidising multi-modal shared mobility services and potentially leads to policy failures around shared mobility schemes. This study suggests that policy entrepreneurship in collaboration with other partners, policy innovation, and the notions of merit goods and second-best policymaking can enable policy initiatives towards multi-modal shared mobility and provide supporting arguments if policies encounter failures. Implementing multi-modal shared mobility requires a collaborative partnership for a paradigm shift: service providers and government must jointly set a merit-based business model, with the support of organisations to achieve improved infrastructure provision, and smart technology applications. The findings will assist the community, business providers and government policymakers to promote multi-modal shared mobility as a pathway towards more efficient, environmentally sustainable, and socially responsive mobility solutions.

Elderly people are less likely to travel long distances or make complex trips and consequently their level of access to services is often less than that of younger people living in the same area. An appropriate residential location may therefore be more critical in old age than at any other time in life. This research uses Geographical Information System (GIS) software to quantify twenty variables that the literature suggests impact elderly residential location decisions. It then examines how well each of these variables explains the current elderly population distribution of Adelaide, South Australia. The results are used to build a conceptual framework of elderly migration that can assist decision makers and stakeholders endeavouring to strategically improve the quality of life of elderly people in urban areas. The research finds that the distribution of people aged 75 years and over is explained better by an area's access to services than by socio-economic, migration or housing related statistics and that the reverse is true for recent retirees. The results enable the importance of specific services to be ranked for four elderly age groups and demonstrate how suburbs with unexpectedly high or low numbers of elderly residents can be identified