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原文传递 Forecasting Ridership for Commuter Rail in Austin.

题名：	Forecasting Ridership for Commuter Rail in Austin.
作者：	Kilgore, S.; Machemehl, R.
关键词：	Ridership analysis, Commuter rail, Transportation needs, Metropolitan planning models, Forecasting, Decision-making model
摘要：	Growing cities like Austin, Texas continue to see the need to improve commuter rail options to make people’s daily travels in an increasingly congested network easier. Therefore, understanding the way people go about accessing (walking, biking, driving, etc.) boarding stations is fundamentally important to characterizing commuter rail travel. To deal with the growing transportation needs, Capital Metro is proposing the addition of commuter rail services in several corridors where publicly-owned rail right of way is available. Forecasting ridership for such services is problematic due to a lack of experience with rail access modal choices and the potential operational state of the transport system due to rapid growth. The goal of this study was to examine the influence of access modes from a commuter’s decision-making process while understanding the characterization at each boarding station. An onboard survey was deployed on Capital Metro’s MetroRail Red Line, revealing access mode patterns and trip purposes for each train station. Then, a binomial logit model was used to determine whether a rider may choose to access the Red Line by walking or driving to the station. This study illustrates a case involving a 32-mile stretch of rail and nine stations where we model the commuters’ decision-making process and future trips relating preferences in travel. Whether train passengers decided to walk, bike, ride a bus, or drive with the convenience of locating a park-and-ride facility, data collected based on distances and choice of access mode lead to generalizations of an individual’s preference for their trips. With a geographic information system (GIS) perspective of the city, evaluating demographic and socioeconomic data gathered from each commuter helped to depict the area influenced by urban sprawl. After which, boarding locations were identified in accordance with how the rail passengers were willing to access each station. For instance, the Central Business District (CBD) within downtown Austin describes a commuter who prefers walking rather than any of the other identified modes since the individual is in close proximity to entertainment and social activities alike. The research carried out suggests that denser areas see a higher number of people willing to walk to the boarding station. A preference for walking was observed at the Downtown Station and Plaza Saltillo Station for entertainment and social trips. On the other hand, people boarding at stations further from CBD often take advantage of parking available at the stations thus their preferred access mode was typically driving. Travelers boarding at park-and-ride stations and for school trips were also found to prefer driving to the station. The model can be used to understand Red Line riders’ decision-making, and may be used to predict access modes for a given trip to inform long-term metropolitan planning models. Finally, this report offers an initial glimpse into the preferences of commuter rail riders in the Austin, TX area, and how such preferences influence the access modes riders use to get to the station. The model specified in this research could be expanded to include other access modes, such as biking or riding the bus, in addition to walking and driving to the station. More data would need to be collected to have enough information to estimate additional access modes. If more data were collected, individual models could be estimated for access mode decision-making at each station, rather than having one model for all stations with dummy variables for each. The same modeling approach used for access mode in this research could be applied to riders’ egress trip mode choice as well.
报告类型：	科技报告