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Modeling Railway Station Access Mode Choice Behaviour and Rail Travel Demand

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Modeling Railway Station Access Mode Choice Behaviour and Rail Travel Demand Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 Modeling Railway Station Access Mode Choice Behaviour and Rail Travel Demand Li MENGa aSchool of Natural & Built Environments, University of South Australia aE-mail:[email protected] Abstract: This paper discusses residents’ preferences regarding train travel with a focus on their mode choice behaviour for the following railway station access modes: car, bus, bicycle and walking. This paper utilises stated preference data to help identify significant factors in train access mode choices within a rail corridor case study. The findings reveal that walking distance, train frequency, bus access, bus waiting time, car access and car park availability are statistically significant. When considering the choice heterogeneity, train frequency and time of day has an impact on people’s choice to access the station by the modes of walking, car or bus. The socio-demographic factors of age, income and gender also influence mode choice and should always be considered in rail service planning. Policy recommendations include providing sheltered walkways and shaded cycling paths and improving feeder bus services to railway stations that will provide a better multimodal transportation network. Keywords: multimodal transportation network, rail travel demand, station access mode choice, transit connection, feeder bus, train frequency 1. INTRODUCTION Transportation makes a substantial contribution to energy consumption. Vehicle emissions are recognised as a major source of air pollution, making up 15 per cent of total household emissions (Lenzen and Dey, 2002) and 15.3 per cent of Australia’s greenhouse gas emissions in 2010 (DCCEE, 2012). Policy tools that focus on the built environment and transit service improvement have become increasingly important in reducing emissions. Examples include transit-oriented development (TOD) in transit corridors (Meng et al., 2016) and the enhancement of the transport network city structure via land use and transport integrated development (LUTI) (Curtis, 2006). In 2009–10, while 634.1 million heavy rail passenger journeys and 184.4 light rail passenger journeys occurred in Australia's urban areas, these two modes made up only 15.7 per cent of total passenger kilometres across all modes. In Adelaide, there were about 11.8 million heavy rail trips and 3 million light rail trips in 2009–10, which only accounted for 1.5 per cent of the passenger kilometres of total motorised passenger tasks (BITRE, 2014). These statistics demonstrate that barriers exist for train use in Adelaide. The distinct barriers to rail use do not impact on travellers individually, but more as a combination of factors (Blainey et al., 2012) which can make it difficult to identify which barriers are most significant. Brons et al. (2009) suggested that satisfaction with the quality and level of accessibility is an important element in explaining rail use. Various other factors have been intensively discussed in previous research work (e.g. Hoogendoorn et al., 2004). These authors investigated how railway station access gates could be configured to assist pedestrian flows. Wen et al. (2012) looked at cost and the time of access: they stressed that rail travellers are cost-sensitive. Applying a different methodology, Polydoropoulou and 574 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 Ben-Akiva’s (2001) analysis used joint revealed preference (RP)1/stated preference (SP) data2 to include attitude factors, such as the feeling of comfort. They identified that the number of multimodal mode transfers was most important, followed by mode of transport and the probability of having a seat while waiting for the train. With their growing impact on planning policy, station access (feeding) mode choice and behaviour are important issues to investigate. Rail station access mode choice is influenced by complex factors (or barriers), including urban form, transport system supply, car park availability, railway station safety, feeder bus system quality, and broader issues across the economy and the environment (Bhat and Guo, 2007; Cervero, 2002; Handy and Niemeier, 1997). In dispersed cities such as those in Australia, with a high car ownership and a relatively low public transport patronage, rail station access mode choice behaviour is more heterogeneous and often not adequately emphasised. This study analyses railway station access mode choices being walking, cycling, (feeder) bus, and car (‘kiss and ride’ and ‘park and ride’) to indicate which barriers deter people from taking a train to work and to suggest how to promote increased train use by satisfying travellers’ needs. This study contributes to the literature by providing a better understanding of the perceptions of travellers’ train station access modes and by informing policy makers of potential strategies to improve railway based public transport network planning. This empirical work embraces the concepts of transit-oriented development (TOD) in a local rail corridor, Adelaide’s Northern Rail Corridor (ANRC). Section 2 of this paper presents a review of recent studies on barriers to railway station access. The paper then describes methodologies for using discrete choice models and stated preference design in Section 3. Section 4 presents observation and survey results, and describes the estimations developed from discrete choice models and the results obtained from the case study area. Section 5 provides policy relevance and discussion, and finally Section 6 summarises and concludes the research. 2. LITERATURE REVIEW Rail or light rail (LR) transport corridors have become increasingly important in transit studies. People who live long distances from railway stations are often deterred from rail travel due to difficulty with access. For some travellers, a multimodal network consisting of rail and connected station access modes makes an attractive service (Zemp et al., 2011), linking rail-line precinct suburbs and beyond. A well-designed network shortens transfer distances and times which will promote non-motorised travel (Breheny, 1995; Dittmar et al., 2004). As part of the transit network, high-speed rail lines could reduce journey times and increase rail’s relative attractiveness (Preston, 2009). The frequency of trains is often determined by travel demand. For a low density population, the average peak frequency is 8 times/ (peak) hour for Melbourne routes, 6 times for Adelaide, and 5 times for Sydney. Some tram routes in Melbourne, such as route 19, 55 and 57 gained an average frequency of 11 times/hour. This is 32 per cent below the average of European systems and 44 per cent below the average of North American systems (Currie and Burke, 2013). Considering trip distance, transport demand, car ownership and competition with other modes in the area served, Hansen (2009) suggested an average frequency of e.g. 6(12) times/(peak) hour is excellent for 1Revealed preference (RP) data are derived from real markets but are selected by decision makers based on their perceptions of the real market. 2 Stated preference (SP) data are collected by asking respondents to make choices from hypothetical choice scenario sets which are composed by the experiment’s design. 575 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 heavily loaded national (regional) passenger railway lines, while 2(4) times/hour is satisfactory for less loaded lines. With the total travel time and waiting time in the rail trip chain included in the calculation, the modes for accessing train stations are considered in computing and adjusting train timetables (Niu et al., 2015; Vansteenwegen and Van Oudheusden, 2007). An individual’s mode choice preference and responsiveness to station accessibility affect her/his travel mode choice for a work trip (Bhat, 2000) which largely relates to transit connection services, socio-demographic backgrounds, personal characteristics and extra travel needs (Loutzenheiser, 1997). The TOD rail (and other transit) corridors are aimed at shortening the distance from home to stations by bringing higher density development to transit nodes to encourage residents to use public transport (Dittmar and Poticha, 2004). Researchers claim that walking paths and cycling routes directly influence people’s travel mode choice and make a transit node area a more desirable residential location (Givoni and Rietveld, 2007; TRB, 2008). They are also ‘the only completely sustainable forms of travel’ (e.g. Mees, 2009). When only a short distance of travel lies between homes and railway stations, walking or cycling are preferred (TRB, 2004).Givoni and Rietveld (2007) used a Dutch railway survey and found that 43 per cent of rail passengers in the Netherlands chose cycling, walking and public transport, while car availability did not affect their mode choice to a railway station. Debrezion et al. (2009) claimed that, with a negative distance effect applying to station access by walking and cycling, offering parking and bicycle stands can have a positive effect for car and bicycle modes. Any improvements to the quality of walkways and bicycle routes would also depend on the preferences of local residents, the culture and the weather conditions. For example, in Adelaide, Australia, the provision of short cuts or shaded walking and cycling routes (with shade provided by trees or other cover) helped travellers commute more comfortably in hot weather (Meng et al., 2016). The provision of car access
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