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.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaf 76. / by Daniel Olivier Bursaux. / M.S.

Indiana University-Purdue University Indianapolis (IUPUI) / On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIXTM improves the accuracy of the model. Sample INRIXTM data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.

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)

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Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed.

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