The rapid expansion of low-density suburban areas in North America has led to new travel patterns that require transit services to be more flexible. Flex-Route transit service, which combines fixed-route transit service with elements of demand-responsive transit service, has emerged as a viable transit option to address the travel needs of the residents of these areas. Existing literature in this field, however, is limited and lacks any comprehensive analysis of Flex-Route planning, design and scheduling. This research aims at exploring Flex-Route transit service to provide detailed guidelines for the planning and design of the service, as well as developing a new scheduling system for this type of unique service. Accordingly, the objectives of this research are: assessing the practicality of Flex-Route transit service in serving low-density suburban areas; identifying essential Flex-Route planning steps and design parameters; determining the feasibility and cost of replacing fixed-route transit with Flex-Route service; and developing a Flex-Route-specific dynamic scheduling system that relies on recent developments in computer and communication technologies. In this regard, we develop an analytical model that addresses several design parameters and provide a detailed analysis that includes, among other parameters, finding optimal values for Flex-Route service area and slack time. Furthermore, the analytical model includes a feasibility and cost analysis that estimates the cost incurred by several stakeholders if Flex-Route service is chosen to replace fixed-route service. The core of the scheduling system is a new developed algorithm – the Constrained-Insertion Algorithm- that exploits the powerful search techniques of Constraint Programming. The scheduling system can handle the daily operations of Flex-Route transit services; it accepts daily (or dynamic) inputs and, in minimal time, produces very cost-effective and reliable schedules. Moreover, the scheduling system has the ability to be used as simulation tool to allow transit operators to assess the feasibility and performance of proposed Flex-Route transit services before implementation. The applicability of the analytical model as well as the performance of the scheduling system were subsequently evaluated and validated through process that included testing on a case study in the City of Oakville, Canada.

The rapid expansion of low-density suburban areas in North America has led to new travel patterns that require transit services to be more flexible. Flex-Route transit service, which combines fixed-route transit service with elements of demand-responsive transit service, has emerged as a viable transit option to address the travel needs of the residents of these areas. Existing literature in this field, however, is limited and lacks any comprehensive analysis of Flex-Route planning, design and scheduling. This research aims at exploring Flex-Route transit service to provide detailed guidelines for the planning and design of the service, as well as developing a new scheduling system for this type of unique service. Accordingly, the objectives of this research are: assessing the practicality of Flex-Route transit service in serving low-density suburban areas; identifying essential Flex-Route planning steps and design parameters; determining the feasibility and cost of replacing fixed-route transit with Flex-Route service; and developing a Flex-Route-specific dynamic scheduling system that relies on recent developments in computer and communication technologies. In this regard, we develop an analytical model that addresses several design parameters and provide a detailed analysis that includes, among other parameters, finding optimal values for Flex-Route service area and slack time. Furthermore, the analytical model includes a feasibility and cost analysis that estimates the cost incurred by several stakeholders if Flex-Route service is chosen to replace fixed-route service. The core of the scheduling system is a new developed algorithm – the Constrained-Insertion Algorithm- that exploits the powerful search techniques of Constraint Programming. The scheduling system can handle the daily operations of Flex-Route transit services; it accepts daily (or dynamic) inputs and, in minimal time, produces very cost-effective and reliable schedules. Moreover, the scheduling system has the ability to be used as simulation tool to allow transit operators to assess the feasibility and performance of proposed Flex-Route transit services before implementation. The applicability of the analytical model as well as the performance of the scheduling system were subsequently evaluated and validated through process that included testing on a case study in the City of Oakville, Canada.

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This research aims at developing a better understanding of commuters preferences and mode switching behaviour towards local transit for work trips. The proposed methodological approach incorporates three main stages. The first introduces a conceptual framework for modal shift maximized transit route design model that extends the use of demand models beyond forecasting transit ridership to the operational extent of transit route design. The second deals with designing and implementing a socio-psychometric COmmuting Survey for MOde Shift (COSMOS). Finally, the third stage focuses on developing econometric choice models of mode switching behaviour towards public transit. Advanced mode shift models are developed using state-of-the-art methodology of combining Revealed Preference (RP) and Stated Preference (SP) information. The results enriched our understanding of mode switching behaviour and revealed some interesting findings. Some socio-psychological variables have shown to have strong influence on mode shift and improved the models in terms of fitness and statistical significance. In an indication of the superiority of the car among other travel options, strong car use habit formation was realized for car drivers, making it hard to persuade them to switch to public transit. Further, unlike conventional choice models, the developed mode shift models showed that travel cost and in-vehicle travel time are of lower importance compared to other transit Level of Service (LOS) attributes such as waiting time, service reliability, number of transfers, transit technology, and crowding level. The results also showed that passengers are more likely to shift to rail-based modes (e.g. LRT and subway) than rubber-tyred modes (e.g. BRT). On the other hand, the availability of park-and-ride facilities as well as both schedule and real-time information provision did not appear to be significant for mode switching to public transit for work trips. This research provides evidence that mode shift is a complex process which involves socio-psychological variables beside common socio-demographic and modal attributes. The developed mode switching models present a new methodologically sound tool for evaluating the impacts of alternative transit service designs on travel behaviour. Such tool is more desirable for transit service planning than the traditional ones and can aid in precisely estimating transit ridership.

This research aims at developing a better understanding of commuters preferences and mode switching behaviour towards local transit for work trips. The proposed methodological approach incorporates three main stages. The first introduces a conceptual framework for modal shift maximized transit route design model that extends the use of demand models beyond forecasting transit ridership to the operational extent of transit route design. The second deals with designing and implementing a socio-psychometric COmmuting Survey for MOde Shift (COSMOS). Finally, the third stage focuses on developing econometric choice models of mode switching behaviour towards public transit. Advanced mode shift models are developed using state-of-the-art methodology of combining Revealed Preference (RP) and Stated Preference (SP) information. The results enriched our understanding of mode switching behaviour and revealed some interesting findings. Some socio-psychological variables have shown to have strong influence on mode shift and improved the models in terms of fitness and statistical significance. In an indication of the superiority of the car among other travel options, strong car use habit formation was realized for car drivers, making it hard to persuade them to switch to public transit. Further, unlike conventional choice models, the developed mode shift models showed that travel cost and in-vehicle travel time are of lower importance compared to other transit Level of Service (LOS) attributes such as waiting time, service reliability, number of transfers, transit technology, and crowding level. The results also showed that passengers are more likely to shift to rail-based modes (e.g. LRT and subway) than rubber-tyred modes (e.g. BRT). On the other hand, the availability of park-and-ride facilities as well as both schedule and real-time information provision did not appear to be significant for mode switching to public transit for work trips. This research provides evidence that mode shift is a complex process which involves socio-psychological variables beside common socio-demographic and modal attributes. The developed mode switching models present a new methodologically sound tool for evaluating the impacts of alternative transit service designs on travel behaviour. Such tool is more desirable for transit service planning than the traditional ones and can aid in precisely estimating transit ridership.

A thorough understanding of transit customers’ preferences and travel behaviour is fundamental to offering a high-quality urban transportation system. The dominant approach in transit quality literature is rooted in understanding current transit users’ preferences. However, disregarding the heterogeneity in transit customers’ desired quality yields suboptimal conclusions regarding their preferences. Therefore, an effective transit system should strive to understand the broad spectrum of transit and non-transit users’ preferences to increase transit ridership. Towards that end, this research aims at deciphering the heterogeneity associated with transit customers’ service desired quality. The research utilized a primary dataset elicited from an online survey that was part of Hamilton Street Railway (HSR) Public Engagement efforts in Hamilton, Ontario, Canada. The research employed state-of-the-art discrete choice models (e.g., error components logit models, latent class choice models, nested logit models) along with multivariate statistical and spatial analysis. In this respect, this dissertation quantified and unveiled latent heterogeneity in transit customers’ preferences and its implications on their willingness to pay for service improvements through various techniques and specifications. Unlike the conventional classifications for transit customers, our research classifies transit customers into three latent segments: Direct Trip Enthusiastic (DTE), Cost-Sensitive (CS), and Real-time Information Supporter (RIS). The dissertation also investigated and further quantified the influence of subjective psychological factors in shaping transit customers’ preferences towards service attributes. For instance, environmental consciousness is found to be associated with less sensitivity to walking time while higher appreciation to at-stop real-time information provision. Furthermore, the research highlighted how the built environment and its contextual effects influence customers’ travel behaviour while accounting for variations in socioeconomic characteristics. The spatial analysis concluded that the built environment's influence is not equally efficacious over geography. Overall, this research presents a unique contribution to the knowledge of public transit research for practitioners, policymakers, and academia. / Dissertation / Doctor of Philosophy (PhD)