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Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.

Given the limitations of traditional methods of data collection and the increased use of smartphones, there is growing attention given to using smartphone apps for activity-travel surveys. Smartphones, through their location-logging capability, allow for the collection of high-quality data on the travel patterns of individuals over multiple days while minimizing the burden on those being monitored. This paper presents the results of an investigation into the potential and limitations of smartphone apps as passenger travel survey instruments. It evaluates the accuracy and performance of various smartphone apps using properly recorded ‘ground truth’ data. Through an open and global invitation to travel survey app and trace processing suite developers, a total of 17 apps were recruited for testing. A controlled experiment was devised, and the accuracy of the apps evaluated based on their ability to reproduce ground truth trip information. Further, the performance of the apps in terms of battery drain was also quantified and evaluated. Results indicate that while accuracy in terms of the trip ends/starts is reasonably high in most cases, mode inference accuracy varied significantly, with a maximum 65–75% accuracy achieved. As such, until significant improvements in mode inference algorithms arise, purely passive location-logging smartphone apps cannot serve as full-fledged automated travel survey instruments. While this may seem problematic, with minor input from respondents regarding regularly visited locations and modes used, as well as specific test case tuning and use of external data such as General Transit Feed Specification, there is an excellent potential to significantly reduce overall response burden and allow for high quality multi-day travel diary data to be collected. Implications of our findings for app design are discussed.

Home location choice is based on both the characteristics of the dwelling (e.g., size, style, number of bedrooms) and the location (e.g., proximity to work, quality of schools, accessibility). Recent years have seen a steep increase in the price of housing in many major cities. In this research, we examine how these price increases are affecting the types of dwelling and locations considered by households. A large sample of real estate listings from 2006 and 2016 from the Greater Toronto Area is used to develop the empirical models. Two recently developed discrete choice models are used in the study: a nested logit model with latent class feedback (LCF) and a semi-compensatory independent availability logit (SCIAL) model. A method of alternative aggregation is proposed to overcome the computational hurdle that often impedes the estimation of choice set models. We find a significant increase in the probability of larger households considering townhouses and apartments over detached single-family dwellings between 2006 and 2016.

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.

Public transit agencies are constantly looking for ways to increase their ridership. While many studies have attempted to identify the factors affecting new customer attraction, the issue of transit user loyalty has been far less researched. In addition to being a good indicator of a transit agency’s performance, customer loyalty provides several added benefits. Loyal customers are more likely to use the transit agency’s services and recommend them to potential new users. Furthermore, attracting users usually involves additional customer acquisition costs (e.g. marketing) not required in order to retain existing loyal users. This study used data provided by a mixed Stated Preference/Revealed Preference survey to identify some of the factors that affect customer loyalty in the context of public transit. Factors examined include service attributes, trip characteristics, as well as socioeconomic and psychological attributes of the individual. The findings suggest that service quality attributes play a critical role in transit user loyalty, while initiatives such as the provision of real-time information panels or making park and ride facilities available have a less determinant effect on the customers’ mode shifting decisions, irrespective of their emotional response to public transit.

Place or residence (POR) and place of work (POW) are two spatial pivots defining patterns of travel behavior. These choices are considered part of long-term choice influencing short-term daily travel choices. Hence, POR-POW distributions are input into almost all daily travel demand models. However, in many cases, POW-POR is modelled in an ad-hoc way considering the gravity-based or entropy is maximizing aggregate modelling approach. Lack of data on the sequence of choices related to POR and POW is often blamed for avoiding using disaggregate choice model. Recognizing such data limitation, this paper presents an alternative methodology of modelling joint distribution of POW-POW that uses disaggregate choice models without necessarily knowing the sequence of POR and POW choices. It uses the conditional probability break downs of joint POR-POW choice probabilities as depicted in the Gibbs sampling approach. This allows capturing effects of household socioeconomic characteristics, zonal land-use characteristics, and modal accessibility factors in the POR-POW models. The model is applied for a case study in the city of Ottawa. Results reveal that the proposed methodology can replicate observed patterns of POR-POW with a high degree of accuracy.

The paper presents an investigation of the temporal transferability of activity scheduling process models and a Meta model of activity scheduling processed by using repeated cross-sectional datasets. Three repeated cross-sectional household travel survey datasets collected in the greater Toronto and Hamilton Area in the years 2001, 2006, and 2011 are used for the investigation. A random utility maximization based dynamic activity scheduling model is utilized to develop activity-travel scheduling models for non-workers and workers separately. Individual year-specific models are compared to identify the temporal stability of the effects of different variables in the model. Results are used to define temporal evolution components in the Meta models. Estimated models are tested for temporal transferability. Different transferability measures are used to test the temporal transferability of cross-sectional year-specific and the Meta models. Results demonstrate an approach of effectively using multiple repeated cross-sectional datasets as pseudo panel data to develop Meta models to improve the temporal transferability of activity scheduling models.

The paper presents the results of an investigation on daily activity-travel scheduling behaviour of older people by using an advanced econometric model and a household travel survey, collected in the National Capital Region (NCR) of Canada in 2011. The activity-travel scheduling model considers a dynamic time–space constrained scheduling process. The key contribution of the paper is to reveal daily activity-travel scheduling behaviour through a comprehensive econometric framework. The resulting empirical model reveals many behavioural details. These include the role that income plays in moderating out-of-home time expenditure choices of older people. Older people in the highest and lowest income categories tend to have lower variations in time expenditure choices than those in middle-income categories. Overall, the time expenditure choices become more stable with increasing age, indicating that longer activity durations and lower activity frequency become more prevalent with increasing age. Daily activity type and location choices reveal a clear random utility-maximizing rational behaviour of older people. It is clear that increasing spatial accessibility to various activity locations is a crucial factor in defining daily out-of-home activity participation of older people. It is also clear that the diversity of out-of-home activity type choices reduces with increasing age and older people are more sensitive to auto travel time than to transit or non-motorized travel time.

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).

The paper presents a dynamic discrete–continuous modelling approach to capture individuals’ tour-based mode choices and continuous time expenditure choices tradeoffs in a 24-h time frame. The analysis of traditional activity-based models are typically limited to activity-type, location and time expenditure choices. Besides, mode choice is often simplified to fit in a pre-defined activity schedule. However, decisions of tour departure time, tour mode choice and time expenditure choice for out-of-home activities are intricately inter-related, and common unobserved attributes influence these choices. This paper proposes a random utility maximization based dynamic discrete–continuous model for joint tour based mode and tour timing choices. Tour timing choice is modelled as continuous time allocation/consumption choice under 24-h time-budget. In the case of the tour-based mode choice component, it uses a modelling structure which harnesses the power of dynamic programming and discrete choice. A cross-sectional household travel survey dataset collected in the Greater Toronto and Hamilton Area in 2016 is employed for the empirical investigation in this study. Empirical model shows the capability of handling all possible mode combinations within a tour including ride-hailing services (e.g., Uber, Lyft). Empirical results reveal that individuals variations in time expenditure choice are defined by activity type, employment status, and vehicle ownership. In terms of mode choice, it is clear the emerging transportation service users have different travel pattern than conventional mode users. This modelling framework has the potential to test a wide range of policies.