Abstract:Approximately one-fifth of Perth's population is aged 60 or older. Projections suggest that this proportion will continue to increase as a result of the large number of children born after the World War II (1946)(1947)(1948)(1949)(1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958)(1959)(1960)(1961)(1962)(1963)(1964). Access to and accessibility around train stations for the aging population is and will become a more important issue as the elderly population continues to grow. The aim of the paper is to develop and apply a new measure of accessibility to train stations at a fine spatial scale, justified by the special circumstance of the elderly using a case study in Perth, Western Australia. Intercept surveys are used to collect data on factors affecting train station accessibility for patrons aged 60 years or older, at seven highly dispersed train stations. Overall accessibility is measured separately using a composite index based on three travel modes (walk-and-ride, park-and-ride and bus-and-ride). The results illustrate that key variables, such as distance from an origin to a station, walking or driving route directness, land-use diversity, service and facility quality, bus connection to train stations, all affect the accessibility to train stations for the elderly. This implies that improvements to these factors will improve accessibility for this population group.
The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.
A train station catchment area delineates the spatial territory from which the users of a train station are drawn. The size and shape of this catchment can be influenced by a variety of factors, such as the transport network, the location of stations and the service quality they offer, as well as the land use density and diversity in the transport corridor. Although numerous studies have been conducted to understand the size of catchment areas, limited research has focused on determining the spatial boundary (shape) of train station catchments. This paper develops a framework for deriving a spatial boundary of a Park and Ride (PnR) catchment area by incorporating the Huff model and Geographic Information Systems (GIS) technologies. The approach is staged, firstly determining the PnR station choice as a function of the attractiveness of a train station and the cost of access between the origin (such as a suburb) and the destination of a trip (such as the Perth CBD). Linear referencing method is then applied to redefine the origins to train stations based on the derived station choice probability. Finally, the spatial boundary of a catchment area is determined according to the adjusted origins, using GIS technologies. The model outputs were evaluated against licence plate survey of station users, where the Kappa coefficient (0.74) and overall accuracy (0.88) statistic suggested that the model's results are robust. The paper then shows how catchment area data can be used to better manage travel demand and plan design solutions aimed at increased accessibility to train stations.
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