Shaping the New Future of Paratransit : An Agenda for Research and Practice

The TRB-sponsored International Paratransit Conference, “Shaping the New Future of Paratransit”, held in Monterey, CA in the United States (US) in October 2014 represented the first coming together of the international paratransit community in conference format since 1997. The conference itself drew a worldwide attendance from a cross-section of operators, technology providers, policymakers and researchers. The presentations from the conference were organised around a number of themes which, when brought together, represented a substantial manifesto for the flexible and demand responsive transport community. This paper looks at a number of these themes with an analysis to highlight the key points and common strands of worldwide experience.

Growth and Public Transportation Investments: Growth Management Strategies to Reduce Transportation Capital Costs In and Near Midwestern Urban Areas, 1995

Transportation and land-use are independent, inter-active systems. Land-use patterns shape local transportation demand, but transportation systems in turn influence land-use patterns. In attempting to satisfy transportation demand created by existing land-use patterns, transportation planners directly, if not always consciously or intentionally, influence future land-use patterns. This study examines that complex relationship. The purpose of the study was threefold: to compile the body of knowledge already existing; to apply this body of knowledge to the context of midsize cities in the Midwest; and, to make the knowledge accessible both to transportation planners and to public officials who make key decisions about land use.

In-depth analysis of the J.A.C.K. model

The Texas Transportation Commission (“the Commission”) is responsible for planning and making policies for the location, construction, and maintenance of a comprehensive system of highways and public roads in Texas. In order for the Commission to carry out its legislative mandate, the Texas Constitution requires that most revenue generated by motor vehicle registration fees and motor fuel taxes be used for constructing and maintaining public roadways and other designated purposes. The Texas Department of Transportation (TxDOT) assists the Commission in executing state transportation policy. It is the responsibility of the legislature to appropriate money for TxDOT’s operation and maintenance expenses. All money authorized to be appropriated for TxDOT’s operations must come from the State Highway Fund (also known as Fund 6, Fund 006, or Fund 0006). The Commission can then use the balance in the fund to fulfill its responsibilities. However, the value of the revenue received in Fund 6 is not keeping pace with growing demand for transportation infrastructure in Texas. Additionally, diversion of revenue to nontransportation uses now exceeds $600 million per year. As shown in Figure 1.1, revenues and expenditures of the State Highway Fund per vehicle mile traveled (VMT) in Texas have remained almost flat since 1993. In the meantime, construction cost inflation has gone up more than 100%, effectively halving the value of expenditure.

Using GIS to visualise and evaluate student travel behaviour

Using GIS to evaluate travel behaviour is an important technique to increase our understanding of the relationship between accessibility and transport demand. In this paper, the activity space concept was used to identify the nature of participation in activities (or lack of it) amongst a group of students using a 2 day travel-activity diary. Three different indicators such as the number of unique locations visited, average daily distance travelled, and average daily activity duration were used to measure the size of activity spaces. These indicators reflect levels of accessibility, personal mobility, and the extent of participation respectively. Multiple regression analyses were used to assess the impacts of students socio-economic status and the spatial characteristics of home location. Although no differences were found in the levels of accessibility and the extent of participation measures, home location with respect to a demand responsive transport (DRT) service was found to be the most important determinant of their mobility patterns. Despite being able to travel longer distances, students who live outside of the DRT service area were found to be temporally excluded from some opportunities. Student activity spaces were also visualised within a GIS environment and a spatial analysis was conducted to underpin the evaluation of the performance of the DRT. This approach was also used to identify the activity spaces of individuals that are geographically excluded from the service. Evaluation of these results indicated that although the service currently covers areas of high demand, 90% of the activity spaces remained un-served by the DRT service. Using this data six new routes were designed to meet the coverage goal of public transport based on a measure of network impedance based on inverse activity density. Following assessment of public transport service coverage, the study was extended using a Spatial Multi Criteria Evaluation (SMCE) technique to assess the effect of service provision on patronage.

Sustainable IT and IT for sustainability

Energy and sustainability have become one of the most critical issues of our generation. While the abundant potential of renewable energy such as solar and wind provides a real opportunity for sustainability, their intermittency and uncertainty present a daunting operating challenge. This thesis aims to develop analytical models, deployable algorithms, and real systems to enable efficient integration of renewable energy into complex distributed systems with limited information.

The first thrust of the thesis is to make IT systems more sustainable by facilitating the integration of renewable energy into these systems. IT represents the fastest growing sectors in energy usage and greenhouse gas pollution. Over the last decade there are dramatic improvements in the energy efficiency of IT systems, but the efficiency improvements do not necessarily lead to reduction in energy consumption because more servers are demanded. Further, little effort has been put in making IT more sustainable, and most of the improvements are from improved "engineering" rather than improved "algorithms". In contrast, my work focuses on developing algorithms with rigorous theoretical analysis that improve the sustainability of IT. In particular, this thesis seeks to exploit the flexibilities of cloud workloads both (i) in time by scheduling delay-tolerant workloads and (ii) in space by routing requests to geographically diverse data centers. These opportunities allow data centers to adaptively respond to renewable availability, varying cooling efficiency, and fluctuating energy prices, while still meeting performance requirements. The design of the enabling algorithms is however very challenging because of limited information, non-smooth objective functions and the need for distributed control. Novel distributed algorithms are developed with theoretically provable guarantees to enable the "follow the renewables" routing. Moving from theory to practice, I helped HP design and implement industry's first Net-zero Energy Data Center.

The second thrust of this thesis is to use IT systems to improve the sustainability and efficiency of our energy infrastructure through data center demand response. The main challenges as we integrate more renewable sources to the existing power grid come from the fluctuation and unpredictability of renewable generation. Although energy storage and reserves can potentially solve the issues, they are very costly. One promising alternative is to make the cloud data centers demand responsive. The potential of such an approach is huge.

To realize this potential, we need adaptive and distributed control of cloud data centers and new electricity market designs for distributed electricity resources. My work is progressing in both directions. In particular, I have designed online algorithms with theoretically guaranteed performance for data center operators to deal with uncertainties under popular demand response programs. Based on local control rules of customers, I have further designed new pricing schemes for demand response to align the interests of customers, utility companies, and the society to improve social welfare.

Desenvolvimento de um modelo computacional para a ampliação do atendimento do Programa de Acessibilidade Especial Porta a Porta - PRAE

Worldwide, the demand for transportation services for persons with disabilities, the elderly, and persons with reduced mobility have increased in recent years. The population is aging, governments need to adapt to this reality, and this fact could mean business opportunities for companies. Within this context is inserted the Programa de Acessibilidade Especial porta a porta PRAE, a door to door public transportation service from the city of Natal-RN in Brazil. The research presented in this dissertation seeks to develop a programming model which can assist the process of decision making of managers of the shuttle. To that end, it was created an algorithm based on methods of generating approximate solutions known as heuristics. The purpose of the model is to increase the number of people served by the PRAE, given the available fleet, generating optimized schedules routes. The PRAE is a problem of vehicle routing and scheduling of dial-a-ride - DARP, the most complex type among the routing problems. The validation of the method of resolution was made by comparing the results derived by the model and the currently programming method. It is expected that the model is able to increase the current capacity of the service requests of transport

Car ownership modeling and forecasts for China

A great increase of private car ownership took place in China from 1980 to 2009 with the development of the economy. To explain the relationship between car ownership and economic and social changes, an ordinary least squares linear regression model is developed using car ownership per capita as the dependent variable with GDP, savings deposits and highway mileages per capita as the independent variables. The model is tested and corrected for econometric problems such as spurious correlation and cointegration. Finally, the regression model is used to project oil consumption by the Chinese transportation sector through 2015. The result shows that about 2.0 million barrels of oil will be consumed by private cars in conservative scenario, and about 2.6 million barrels of oil per day in high case scenario in 2015. Both of them are much higher than the consumption level of 2009, which is 1.9 million barrels per day. It also shows that the annual growth rate of oil demand by transportation is 2.7% - 3.1% per year in the conservative scenario, and 6.9% - 7.3% per year in the high case forecast scenario from 2010 to 2015. As a result, actions like increasing oil efficiency need to be taken to deal with challenges of the increasing demand for oil.

De la desintegración Espacial a la Conformación regional en la Sabana de Bogotá. Una Propuesta para el desarrollo de la Ciudad Región en el siglo XXI

El presente trabajo de investigación aborda el tema del desarrollo regional en torno a una gran metrópoli como Bogotá y la Sabana de Bogotá. El crecimiento, expansión y relación con los municipios vecinos. Su entorno territorial; es un tema de discusión que cada día adquiere más fuerza desde hace varias décadas. Bogotá y la Sabana de Bogotá, se consideran en la actualidad como un caso representativo del caótico modelo de expansión urbana y crecimiento demográfico, enfrentado al desarrollo urbano como paradigma de la desigualdad de la ciudad latinoamericana. Son muchos los procesos y conflictos de naturaleza Regional y metropolitana que atraviesa la capital colombiana. Sin embargo esta investigación abordada el tema desde la necesidad de gobernanza y coordinación para el desarrollo territorial consensuado y equilibrado de la Región. La sabana de Bogotá está conformada por ciudades dormitorio, ciudades industriales, turísticas, agropecuarias, etc., es atravesada por el Río Bogotá, y en su centro la gran metrópoli, Bogotá D.C. centro de trabajo muy importante a nivel nacional, su área de influencia más próxima llega hasta: Caqueza, Zipaquira, Facatativa, Soacha, Fusagasuga y Girardot. Principales polos de desarrollo en la sabana y el departamento. Cundinamarca está compuesto por 15 provincias y Bogotá. Conformando un sistema de redes urbanas con necesidades comunes y demanda servicios públicos, de transporte, aseo, movilidad, salud, educación, espacio público y calidad ambiental. La metodología de esta investigación consiste en el análisis de la región a partir de la articulación de planes de ordenamiento territorial en un área de estudio propuesta. Mediante entrevistas con profesionales, expertos, funcionarios y empleados públicos y teniendo en cuenta las posibilidades legales en Colombia para el desarrollo territorial regional, establecer la situación real en materia de desarrollo para el departamento de Cundinamarca, evidenciando las necesidades del territorio y su desarrollo de una forma más compleja, valorando las sinergias y necesidades sociales, ambientales y económicas propias del crecimiento urbano, para proponer una serie de directrices que estructuren un desarrollo regional equilibrado en Bogotá y Cundinamarca. El análisis de los modelos del caso contribuye a fortalecer iniciativas para el desarrollo Regional de la Sabana de Bogotá como territorio sostenible: ambiental, económico y socialmente. En un sistema de redes que interconecte a Bogotá, con Cundinamarca, Colombia y el resto del mundo. Cundinamarca como región debe fijar estrategias y articular políticas en función de un modelo de desarrollo urbano regional para el departamento y la Sabana de Bogotá. Directrices departamentales básicas y fundamentales para el desarrollo territorial equilibrado que promueva ciudades sostenibles, compactas y con Calidad de vida para todos sus habitantes. ABSTRACT: This research addresses the issue of regional development around a big metropolis like Bogotá and Sabana de Bogota. The growth, expansion and relations with neighboring municipalities. Your local environment; It is an issue that becomes stronger every day for decades. Bogotá and Sabana de Bogotá, are considered today as a representative case of the chaotic model of urban expansion and population growth, urban development faced as a paradigm of inequality in Latin American city. Many processes and conflicts of Regional and metropolitan nature that crosses the Colombian capital. However this research addressed the issue from the need for governance and coordination for consensual and balanced territorial development of the region. The savannah of Bogota consists of bedroom communities, industrial cities, tourism, agriculture, etc., is crossed by the Bogota River, and at its center the great metropolis, Bogota DC center very important work at the national level, the area closest influence reaches: Caqueza, Zipaquira, Facatativa, Soacha, Fusagasuga and Girardot. Main centers of development in the savannah and the department. Cundinamarca is composed of 15 provinces and Bogota. Forming a system of urban networks with common needs and demand utilities, transportation, grooming, mobility, health, education, public space and environmental quality. The methodology of this research is the analysis of the region from the joint land use plans in the proposed study area. Through interviews with professionals, experts, public officials and employees and taking into account the legal possibilities in Colombia for regional territorial development, establish the real situation in development for the department of Cundinamarca, showing the region's needs and development of a more complex form, assessing synergies and own social, environmental and economic needs of urban growth, to propose a set of guidelines to structure a balanced regional development in Bogota and Cundinamarca. The analysis of case models helps to strengthen initiatives for regional development of the Sabana de Bogota and sustainable region: environmentally, economically and socially. In a network system that interconnects to Bogotá with Cundinamarca, Colombia and elsewhere. Cundinamarca region should set as joint strategies and policies based on a model of regional urban development for the department and the Sabana de Bogota. Basic and fundamental to balanced territorial development that fosters sustainable, compact and quality of life for all its inhabitants cities departmental guidelines.

Past and future trends in light truck sales. Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

Forecasting auto ownership and mode choice for U.S. metropolitan areas. Volume II: model equations. Final report.

Transportation Department, Office of University Research, Washington, D.C.

Study of automobile market dynamics. Volume I: description. Final report.

Transportation Systems Center, Cambridge, Mass.

Study of automobile market dynamics. Volume II: analysis. Final report.

Transportation Systems Center, Cambridge, Mass.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Optimal day-ahead scheduling of integrated urban energy systems

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.