Agent-based model for travel behaviour in multimodal networks - a modelling system for travelsmart initiatives

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Urban sprawl has been observed in many urban areas across North America in recent years. It generally results in areas of medium to low densities and dispersed land use; such characteristics increase auto-dependency and lessen the potential of running an efficient transit system. In the Greater Vancouver Regional District (GVRD), more residents now live in the Green Belt (73%) with such land use characteristics, than in the City of Vancouver (27%) with its compact and dense urban form. With about 34.9% of the GVRD work trips destined to Vancouver, 13.3% of those are originated in the Green Belt with a 25.6% using transit. For travel activities generated from/destined to such areas, passengers transfer between different modes and different networks, forming what is called "multimodal trips". The topic of multimodal trip-making has been the topic of considerable attention in the public and media lately. Therefore, multimodal assignment models are increasingly becoming an essential component of any region-wide planning exercise. Existing research efforts have focused on auto trips and traffic operations, resulting in simplifying (or ignoring) the operations and performance of other modes of travel (such as transit or walking). For multimodal trips, this presents a major drawback since most multimodal trips include transit as the primary mode of travel. In fact, a large portion of multimodal trips do not include auto-usage for any part of the trip (e.g. walk-transit trips). In the GVRD, the percentage of trips using multimodal networks (e.g. walk/transit, bike/transit, auto/transit, etc) destined to Downtown Vancouver is about 50%. The focus of this research is to develop a modelling system to evaluate and assess a broad range of policies that support the integration of multimodal (public) transport networks and target TravelSmart initiatives. TravelSmart initiatives promote travel alternatives that are sustainable, healthy and reduce car use. This research proposes an innovative approach to investigate the modelling of passengers' travel choices in a multimodal network and the development of a multi-agent assignment framework for multimodal trips.