This paper presents a systematic way of understanding and modelling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet interconnected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with interconsumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modelling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based contextaware stated preference survey. Through model estimation, lower VOTs are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. The obtained results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobility services such as ridehailing services and the emerging MaaS (Mobility as a service).
Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based freight vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stopto-tour assignment algorithm using the location of a "base" as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tourchain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017-2019 in Singapore. We compare the algorithms in an assessment of freight vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across vehicle types and industries.
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