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On-demand transport (ODT) refers to adaptive transport services that use a fleet of vehicles to provide shared flexible transport to consumers, when and where they need it. This study surveyed 3,985 geographically and demographically representative Australians nationwide, to understand consumer demand and willingness to pay for ODT in Australia. Our analysis finds that the current market for ODT services in Australia is small. For example, for an ODT service that costs roughly the same as UberX's ridesharing service, and offers comparable level-of-service, our analysis predicts that only 17 per cent of the Australian population can be expected to use the service a few times a week or more. However, shared electric autonomous vehicles could significantly change the business case for ODT services, by enabling on-demand door-to-door transport services at a fractional cost of similar existing services. Our analysis finds that while consumers are willing to pay, on average, 0.28$/km more to avoid sharing a vehicle with other passengers, 0.17$/km more for door-to-door service, and 0.10$/km to be able to book the service in real time, cost is the most important determinant of ODT use (of the service attributes included in our analysis). For an ODT service that provides the same level-of-service as UberX, but at a fractional cost of $0.30 per km, 31 per cent of the Australian population can be expected to use the service a few times a week or more. And this figure is likely to be larger once we account for more long-term changes in lifestyles that might accompany the introduction of these services. We find that frequency of ODT use is strongly correlated with age and lifecycle stage: young individuals who are employed full-time are likely to use ODT most frequently; older adults who have retired from the workforce and whose children have left home are likely to be infrequent users. Overall, our analysis indicates that ODT services have the ability to both increase public transport use among existing public transport customers, and to draw new customers to public transport services. [ABSTRACT FROM AUTHOR]

This study develops a hierarchical latent class choice model that captures the concurrent influence of lifestyles on household residential neighbourhood location and individual transport mode use decisions. The model is empirically evaluated using data from the 2010–12 California Household Travel Survey. The model identifies six household-level classes that differ in terms of their preferences for different neighbourhood attributes when deciding where to live and their household characteristics. Coincidentally, the model also identifies six individual-level classes that differ in terms of the travel modes that they consider when deciding how to travel, their sensitivity to different level-of-service attributes, and their individual characteristics. Household preferences for neighbourhood types and individual preferences for travel modes show expected patterns of correlation. In general, households that prefer to live in suburban neighbourhoods are more likely to consist of individuals that are car-dependent, and households that prefer to live in inner-city neighbourhoods are more likely to consist of individuals that are multimodal. However, our analysis also reveals interesting patterns of deviation. For example, high-income migrant households and median-income white households display strong preferences for suburban neighbourhoods, but individuals belonging to these households also have a high likelihood of being multimodal, with a strong preference for bicycling. We discuss how these patterns of correlation can be used to inform transport and land use policy in novel ways. [ABSTRACT FROM AUTHOR]

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods. [ABSTRACT FROM AUTHOR]

This study examines changes in observable patterns of travel mode choice behavior over time, and attempts to explain these changes in terms of possible shifts in latent modal preferences, while controlling for the confounding influence of concurrent changes in the socioeconomic environment and transportation infrastructure. Using repeated cross-sectional travel diary data collected from individuals residing in the San Francisco Bay Area in 2000 and 2012, we develop a latent class choice model of travel mode choice behavior. Estimation results reveal ten segments across the pooled sample populations that differ from one another in terms of their demographic composition, the travel modes that they consider, and the relative importance that they attach to different level-of-service attributes, namely travel times and costs. Findings indicate shifts in latent modal preferences that exceed analogous changes in observable travel mode choice patterns. For example, private (motorized) vehicle mode shares decreased from 85.0% in 2000 to 81.2% in 2012, but the proportion of the population that only considers private vehicle when deciding how to travel is found to decline from 41.7% to 23.5% during the same period. Changes in economic and social factors and changes in the level of service of different travel modes are found to have had a marginal effect. Had modal preferences not changed between 2000 and 2012, over and above changes in the socioeconomic environment and the transportation infrastructure, our framework predicts that private vehicle mode shares would have increased to 88.3% by 2012. Finally, shifts in modal preferences are not found to be limited to any one generation but to have cut across the entire population, reflecting broader cultural shifts that have transcended generational differences. [ABSTRACT FROM AUTHOR]

Highlights: [•] Operationalizes the construct of modality styles, or behavioral predispositions towards different travel modes within the framework used by traditional travel demand models. [•] Synthesizes recent advances in discrete choice analysis in the fields of taste heterogeneity, choice set generation, and simultaneous choice models. [•] Applies the framework to six-week travel diary data from Karlsruhe, Germany. [•] Results indicate the presence of auto-oriented individuals who display a strong bias for using the automobile and multimodal individuals who exhibit variation in their modal preferences. [•] Multimodal behavior is further distinguished by those who appear to be sensitive to travel times and those who appear to be insensitive. [Copyright &y& Elsevier]