Travel patterns in Canadian urban areas changed during the twentieth century. No longer is urban travel downtown oriented. In all but the smallest Canadian urban areas, travel has evolved into a polycentric pattern. Despite this Canadian public transit networks remain oriented to the older travel patterns because of shortages in planning capacity. The transit literature on performance monitoring focuses on “system” variables rather than “network” variables like how well transit networks match travel patterns. This research develops a method by which transit planners can monitor the performance of transit networks in their communities. Applying this methodology provides recommendations to planners on how to improve transit network structures to better facilitate polycentric urban travel. Future research should compare the network performance of Canadian transit systems.

Travel patterns in Canadian urban areas changed during the twentieth century. No longer is urban travel downtown oriented. In all but the smallest Canadian urban areas, travel has evolved into a polycentric pattern. Despite this Canadian public transit networks remain oriented to the older travel patterns because of shortages in planning capacity. The transit literature on performance monitoring focuses on “system” variables rather than “network” variables like how well transit networks match travel patterns. This research develops a method by which transit planners can monitor the performance of transit networks in their communities. Applying this methodology provides recommendations to planners on how to improve transit network structures to better facilitate polycentric urban travel. Future research should compare the network performance of Canadian transit systems.

The main objective of this study was to quantitatively explore the connection between network structure and network effect and its impact on transit usage as seen through the real-world experience of the Toronto and Melbourne transit systems. In this study, the comparison of ridership/capita and mode split data showed that Toronto’s TTC has better performance for the annual data of 1999/2001 and 2006. After systematically investigating travel behaviour, mode choice factors and the various evidence of the network effect, it was found that certain socio-economic, demographic, trip and other design factors in combination with the network effect influence the better transit patronage in Toronto over Melbourne. Overall, this comparative study identified differences that are possible explanatory variables for Toronto’s better transit usage as well as areas where these two cities and their transit systems could learn from one another for both short and long term transit planning and design.

The main objective of this study was to quantitatively explore the connection between network structure and network effect and its impact on transit usage as seen through the real-world experience of the Toronto and Melbourne transit systems. In this study, the comparison of ridership/capita and mode split data showed that Toronto’s TTC has better performance for the annual data of 1999/2001 and 2006. After systematically investigating travel behaviour, mode choice factors and the various evidence of the network effect, it was found that certain socio-economic, demographic, trip and other design factors in combination with the network effect influence the better transit patronage in Toronto over Melbourne. Overall, this comparative study identified differences that are possible explanatory variables for Toronto’s better transit usage as well as areas where these two cities and their transit systems could learn from one another for both short and long term transit planning and design.

Although not often discussed in planning curriculum across the United States, airports are a very significant part of the country’s transportation infrastructure. With so much emphasis placed on ground transportation modes such as light rail, bus, and bike, the impact of aviation on the transportation world gets lost in translation. This study seeks to bring airport planning back into the conversation by analyzing population projections, income, and industry and their affect on airport preparedness to meet increasing population demands. By using population projections through 2035 for the State of Texas, gross population and population growth rates were used to select counties within Texas that may be most in need of airport planning considerations. Counties along the Texas-Mexico border were analyzed in further detail for planning considerations and preparedness based on their population growth rates. Hidalgo County’s McAllen-Miller International Airport (MFE) was found to be proactively seeking changes to their airfield that matched recommendations based on their population increase. However, the level of coordination with city or county level authorities is lacking. / text

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This report is intended to shed light on mobile home parks as valuable contributors to the affordable housing stock throughout the United States, as well as in Austin, Texas. In many areas of the country, mobile home park losses to redevelopment have already been proven as problematic. While Austin has not experienced excessive park loss as of yet, as planning initiatives and development trends continue to effect property values, some of Austin’s mobile home parks may be at risk of redevelopment, which leaves the already vulnerable, and typically low-income residents at risk of losing homes and social networks. This paper evaluates current planning pressures in Austin, most specifically the addition of new rail routes, as having the potential to the affect property values of certain mobile home parks; therefore putting them at risk of redevelopment. Recommendations for protection and support of these parks are offered at the local and state level. / text

published_or_final_version / abstract / toc / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

It is observed that transit riders are responding to service changes while transit planning is responding to ridership changes, or that transit patronage and service supply are highly interrelated. It is also noticed that transit riders transfer from route to route, the introduction of new service may draw some riders from the existing routes, which implies transit patronage on a route is also affected by other parallel and intersecting routes. An analytic tool is needed to examine these complex relationships in the transit system. This study has developed a quantitative model by incorporating these interactions into a simultaneous system. The simultaneity of transit demand, supply and the interrelationship of inter-route effects are addressed in a three-equation simultaneous model: a demand equation, a supply equation and an equation for competing routes. These equations are estimated simultaneously using the three-stage-least-squares estimation method. The model is estimated at the route-segment level by the time of a day, and by the inbound and outbound directions. Data from Portland, Oregon metropolitan area are used as an extended case study. The socioeconomic and demographic data are allocated to an one-quarter-mile distance service area around a transit route by utilizing the technique of Geographic Information Systems (GIS). The data allocation significantly reduces the measurement error. Inter-route relationships are also identified using GIS. The estimation results show that a service change on a route increases the transit patronage on that route, but it also decreases the ridership on its competing routes, so the net effect of that service improvement is smaller than the ridership increase on the subject route. A conventional single equation model under-estimates the ridership responses on the subject route, and over-estimates the net patronage response. This study is the first research to discuss the net effects of a service change at the route level. The model can be implemented for system-level policy analysis and route-level service and land use planning. It is especially useful for "what-if" scenario analysis at the route level to simulate the ridership impacts of service and land use changes.