Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Patrick Jaillet. / M.S.

Paratransit service operators in Canada are under increasing pressure to improve the operational productivity of their services due to increased demand and tightening financial constraints. To achieve this, Paratransit operators need to know their performance as compared to peer systems and the best practices within the industry. This will enable each operator to identify where and how much improvement should be made in order to be on a par with the industry?s best practices. Little research effort, however, has been devoted to the issue of how to measure and compare paratransit efficiency in a consistent and systematic manner. <br /><br /> This research focuses on evaluating the level of efficiency of individual paratransit systems in Canada with the specific objective of identifying the most efficient service agencies and the sources of their efficiency. By identifying the most efficient systems along with the influencing factors, it is possible that new service policies and management and operational strategies could be developed for improved resource utilization and quality of services. To achieve this objective, this research applies the analysis methodology called Data Envelopment Analysis (DEA) approach which is a mathematical programming based technique for determining the efficiency of individual systems as compared their peers involving multiple performance measures. Annual operating data from Canadian Urban Transit Association for Canadian paratransit systems of year 2001, 2002 and 2003 are used in this analysis. Regression analysis is performed to identify the possible relationship between the efficiency of a paratransit system and some measurable operating, managerial and other factors which could have an impact on the performance of paratransit systems. The regression analysis also allows for the calculation of confidence intervals and bias for the efficiency scores in order to assess their precision.

Demand Responsive Transport (DRT) is a relatively new form of public transport provision; it is an intermediate form somewhere between conventional bus and taxi services. Over the last decade, DRT services have grown in popularity mainly influenced by the development of transport telematics. The telematics-based DRT system, which forms the focus of this research, allows new generation DRT services to have greater flexibility in time and route design, and to enable immediate advance booking and response to travel requests. These DRT services have shown important advantages and benefits in several European cities and regions particularly as an alternative solution of public transport in low/dispersed demand areas and times. Moreover, DRT services have an important role to tackle social exclusion. However, several previous works reveal that many existing DRT services are still not performing to their true potential and there is still a research need to investigate DRT services from the passengers’ perspective. Therefore, this research studies DRT services from the passengers’ perspective by selecting the LinkUp DRT scheme as the case study. LinkUp is a telematics-based DRT scheme which operates as a public transport service in Tyne and Wear, UK with fully flexible routes in defined operating areas. A discrete choice model with latent variables is applied to model the passengers’ usage rate of the LinkUp DRT services. The assumption of the usage rate model developed in this research is that each passenger has an underlying utility for using the LinkUp services and the passengers who use LinkUp at different levels of frequencies have different levels of utility. The individual’s utility has an underlying latent variable and his usage rate of LinkUp in terms of number of trips per week serves as choice indicators. This study hypothesises that characteristics, and attitude and perception towards the LinkUp services of the passenger affect his utility. The passengers’ attitude and perception are constructed as latent variables (models) in the usage rate model. Therefore, the usage rate model consists of two sub-models: latent variable and discrete choice models which are specified as Multiple Indicators and MultIple Causes (MIMIC) and ordered probit models respectively. Three latent variables are proposed to quantify the passengers’ attitude and perception, which are latent Awareness, Satisfaction, and Relative Advantage. Consequently, the usage rate model is represented by the utility, which is hypothesised to be the function of the individual passenger’s characteristics and three latent variables. The results provide useful information for improving the LinkUp DRT scheme, implementing and developing telematics-based DRT services, further developing the travel behaviour model for DRT passengers, as well as for the DRT operators and policy makers.

Paratransit service operators in Canada are under increasing pressure to improve the operational productivity of their services due to increased demand and tightening financial constraints. To achieve this, Paratransit operators need to know their performance as compared to peer systems and the best practices within the industry. This will enable each operator to identify where and how much improvement should be made in order to be on a par with the industry?s best practices. Little research effort, however, has been devoted to the issue of how to measure and compare paratransit efficiency in a consistent and systematic manner. <br /><br /> This research focuses on evaluating the level of efficiency of individual paratransit systems in Canada with the specific objective of identifying the most efficient service agencies and the sources of their efficiency. By identifying the most efficient systems along with the influencing factors, it is possible that new service policies and management and operational strategies could be developed for improved resource utilization and quality of services. To achieve this objective, this research applies the analysis methodology called Data Envelopment Analysis (DEA) approach which is a mathematical programming based technique for determining the efficiency of individual systems as compared their peers involving multiple performance measures. Annual operating data from Canadian Urban Transit Association for Canadian paratransit systems of year 2001, 2002 and 2003 are used in this analysis. Regression analysis is performed to identify the possible relationship between the efficiency of a paratransit system and some measurable operating, managerial and other factors which could have an impact on the performance of paratransit systems. The regression analysis also allows for the calculation of confidence intervals and bias for the efficiency scores in order to assess their precision.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The University of Central Florida, with an enrollment of approximately 16,000 students, is being faced with parking, traffic and transportation problems. The University of Central Florida (UCF) is a commuter campus, with over 90% of the students arriving by automobile. Parking spaces cost over $800/space, and funding to build new spaces is scarce. Existing lots on the perimeter of the UCF campus offer a potential advantage to park and ride services or a shuttle serve around UCF. Research conducted for this paper evaluated the usage of a shuttle bus system around the UCF campus. The primary purpose of the shuttle is to move people around the campus, similar to the shuttle used by Disney. This is benefit primarily to the users, but it is also an asset to the whole campus, especially since it increased the general mobility of the University population and its accessibility to various locations and activities. The size of a shuttle travel area around the campus, routes that would serve all major areas of the campus and cost of the shuttle bus are the major points evaluated in the research report. The methodology included in this study references the Urban Transportation Planning Process (UTPP), which consists of four sub-models: (1) trip generation, (2) trip distribution, (3) modal split and (4) traffic assignment.

published_or_final_version / Transport Policy and Planning / Master / Master of Arts

The objective of this thesis will be to evaluate and assess the current Care Service being offered by GRTC and recommend economic efficient, equitable and sustainable opportunities for a better Human Mobility Service transportation effectiveness and coordination. Transportation demand management strategies that generate revenue and contain costs are required to meet the demands and needs of future aging populations without compromising quality of service. With the baby boomer population set to retire and advancing health care improvements, the elderly population and disabled segment of the population is bound to rise, increasing the demand for human mobility services. This trend and age wave is being felt across the U.S. and has affected all states, including the City of Richmond. Although the GRTC Transit System in Richmond provides a Human Mobility Service called the Care-Service for Disabled Elderly, findings show that the agency is operating at a loss and has no dedicated plan for a Human Mobility Service.