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Simplified Model of Local Transit Services

UCD-ITS-RP-14-51

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Suggested Citation:
Circella, Giovanni, John D. Hunt, Kevin John Stefan, Alan Thomas Brownlee, Michael McCoy (2014) Simplified Model of Local Transit Services. European Journal of Transport and Infrastructure Research 14 (2), 122 - 142

This paper discusses the development of a simplified model to efficiently represent local public transportation services in a large-scale travel demand model. The California Statewide Travel Demand Model (CSTDM) is a comprehensive model system designed and developed for use in transportation policy analysis and travel demand forecasting, including representation of both long and short distance transportation covering the entire state of California. A novel hybrid system is used to represent the full range of rail and bus transit services that are available. Rail services – including all long-distance rail, commuter rail and light rail services – are represented in the standard manner, using explicit node and link networks; the relevant in-vehicle and out-of-vehicle service characteristics for journeys are determined as standard skims of these networks. On-street bus services are not represented using explicit networks; rather, the relevant in-vehicle and out-of-vehicle service characteristics are determined using functions of other transportation network variables, land use descriptors and relevant policy indicators. These functions are simplified econometric models estimated using observations of transit service obtained from Google Transit Data Feeds. The network and simplified components are integrated in order to allow transit paths with both rail and on-street bus components to be considered by the various travel choice models included in the modelling framework. This hybrid system provides a suitable representation of transit for an area of such size. This facilitates consideration of transit service policies, while obviating the need for extensive transit coding, a daunting task for a large area.

Keywords: public transportation; travel time; travel demand modelling; urban density; Google transit data; statewide modelling