People with disabilities tend to face more difficulties and constraints when they are travelling for tourism purposes, compared to non-disabled people. This is problematic because mobility in general (and tourism in particular) is, more or less, seen as a ‘human right’ and has the potential to contribute to peoples’ well-being. Tourism travel should, therefore, be available for everyone. This thesis aims to contribute to the understanding of tourism travel for people with mobility-disabilities by studying the experiences of parents to wheelchair carried children with cerebral palsy. The main study objectives are threefold, namely: (1) which constraints these families face throughout the tourism travel chain; (2) how the child’s disability affect these families’ destination decision processes; and (3) which improvements that can be made in order to make it easier for these families to participate in tourism travel. An interview study was made including 13 parents to wheelchair children with cerebral palsy and these interviews were analysed by using thematic analysis. The findings show that different constraints and barriers (intrinsic, interactive and environmental) are evident during all different phases of the tourism travel chain and affect these families’ choices regarding type of tourism trip, which destination they travel to, which transport modes that they can use, which requirements they have in terms of accommodation and which limitations they face when participating in different activities. Some suggestions regarding improvements are also provided, based on what the parents require in order to make tourism travel easier for them.

It is possible to observe certain stagnation of the winter tourism which is linked with winter sports. The overall number of participants is not growing any more, however, new ski resorts are being set up and the competition is continuously rising. In order to stay competitive, it is necessary to take into account reasons which leads to the final decision of a tourist to choose certain ski resort. This thesis concentrates on the concepts that influence the process of choosing the destinationThese concepts, which are based on foreign literature are following: destination image, place attachment, loyalty. During the winter season of 2015 there was also a research conducted at the recently developing destination Lipno nad Vltavou, which was based on observations and interviews with the visitors. The above mentioned concepts were applied here. Powered by TCPDF (www.tcpdf.org)

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

The thesis examines post-event displacement of households in the year following Hurricane Harvey. Using data gathered from a three-page mail survey conducted approximately 1-year after the storm, this study examines two primary research objectives. First this thesis aims to identify variables that predict displacement or non-displacement after the disaster. Second, this study explores patterns in the destination and duration of displaced households following Hurricane Harvey. Logistic regression analyses were used to examine the extent to which household composition characteristics and level of damage sustained during Hurricane Harvey predicted post-disaster displacement. Next, independent sample t-tests and descriptive statistical analyses were used to identify patterns in the destination of post-event relocations. Research findings indicate in the overall binary logistic regression model that after Hurricane Harvey, being White, level of home damage, wind damage, and number of days a member of the household returned home post-Harvey increased the likelihood of a household being displaced. Analysis of the survey responses also indicated that many households made multiple moves following Hurricane Harvey and specifically, displaced households were more likely to stay with a friend or relative. Additionally, this study found that with each additional relocation, the duration of stay at each destination increased while the distance from their pre-disaster home decreased. This thesis advances understanding of what predicts household displacement after a disaster and offers new insights into where people go during the short-term and beginning of the long-term recovery phases.

This study contributes to the literature on national long-distance travel demand modeling by providing an analysis of households' annual destination choices and time allocation patterns for long-distance leisure travel purposes. An annual vacation destination choice and time allocation model is formulated to simultaneously predict the different destinations that a household visits and the time it spends on each of these visited destinations, in a year. The model takes the form of a Multiple Discrete-Continuous Extreme Value (MDCEV) structure (Bhat, 2005; Bhat, 2008). The model assumes that households allocate their annual vacation time to visit one or more destinations in a year to maximize the utility derived from their choices. The model framework accommodates variety-seeking in households' vacation destination choices in that households can potentially visit a variety of destinations rather than spending all of their annual vacation time for visiting a single destination. At the same time, the model accommodates corner solutions to recognize that households may not necessarily visit all available destinations. An annual vacation time budget is also considered to recognize that households may operate under time budget constraints. Further, the paper proposes a variant of the MDCEV model that avoids the prediction of unrealistically small amounts of time allocation to the chosen alternatives. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical data for this analysis comes from the 1995 American Travel Survey Data, with the U.S. divided into 210 alternative destinations. The empirical analysis provides important insights into the determinants of households' leisure destination choice and time allocation patterns. An appealing feature of the proposed model is its applicability in a national, long-distance leisure travel demand model system. The annual destination choices and time allocations predicted by this model can be used for subsequent analysis of the number of trips made (in a year) to each destination and the travel choices for each trip. The outputs from such a national travel modeling framework can be used to obtain national-level Origin-Destination demand tables for long-distance leisure travel.

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.

The Utah Department of Transportation (UDOT) manages and maintains a complex state-wide network of highways. Recent incidents such as the collapse of the I-35W bridge in Min- neapolis, Minnesota, and the I-85/Piedmont Road fire and subsequent bridge collapse in Atlanta, Georgia, have brought identification of transportation network vulnerabilities to the forefront of UDOT’s planning efforts. Traditional estimates of transportation network impacts have focused on increases to user travel time or the volume of affected traffic, but studies of these disasters have revealed that when facing a degraded transportation network, people adjust their trip making in terms of destination, mode, and route choice. The objective of this thesis is to evaluate the relative systemic criticality of highway links on Utah’s highway network using a logit-based model sensitive to changes in destination choice, mode choice, and route path. The current Utah Statewide Travel Model (USTM) does not incorporate user mode or destination choice, making it unsuitable for this task in its present condition. Consequently, this thesis develops a logit-based model structure that evaluates the cost of impaired destination choices and mode choices for home-based and non-home-based personal trips resulting from a damaged highway network. The choice model logsums capture the total value of user choices and can be readily converted to monetary values, making them ideal for this purpose. The logit-based model is then applied to 40 highway links located at strategic locations on Utah’s network. When compared with a more traditional travel time increase estimation, the logsum and travel time models provide categorically different cost estimates, where the logsum results are typically lower than travel time estimates, with implications for policy making and UDOT’s planning strategy. The results further suggest that freight trips are likely more important considerations than passenger trips, and should be considered in future research.

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