published journal article
working paper
Network design problems concern flows over networks in which a fixed charge must be incurred before an arc becomes available for use. The uncapacitated, multicommodity network design problem is modeled with (i) aggregate, and (ii) disaggregate “forcing” constraints. [Forcing constraints ensure logical relationships between the fixed charge-related and the flow-related decision variables.] A new lower bound for this problem referred to as the capacity improvement (CI) bound-is presented; and an efficient implementation scheme (using shortest path and linearized knapsack programs) is described. A key feature of the CI lower bound is that based on the LP relaxation of the aggregate version of the problem. A numerical example illustrates that the CI lower bound (i) can be as tight as the disaggregate LP relaxation, and (ii) can converge to the optimal objective function value of the IP formulation.
conference paper
published journal article
published journal article
book/book chapter
research report
This Study examines the institutional and economic feasibility of increasing the utilization of the private sector to provide and organize commuter transportation services. These include both private services not subsidized with public funds — private commuter bus service, buspools, and employer vanpool programs, and privately provided services sponsored by public agencies — privately contracted commuter bus service and transit agency vanpooling programs. Based on case studies of urban areas where these services exist, the report determines that they demonstrate significant economic advantages compared to transit agency commuter bus services, but face major institutional and market obstacles to widespread adoption.
working paper
Trucks have a significant impact on infrastructure, traffic congestion, energy consumption, pollution and quality of life. To better understand truck characteristics, comprehensive high resolution truck data is needed. Higher quality truck data can enable more accurate estimates of GHGs and emissions, allow for better management of infrastructure, provide insight to truck travel behavior, and enhance freight forecasting. Currently, truck traffic data is collected through limited means and with limited detail. Agencies can obtain or estimate truck travel statistics from surveys, inductive loop detectors (ILD) and weigh-in-motion (WIM) stations, or from manual counts, each of which have various limitations. Of these sources, WIM and ILD seem to be the most promising tools for capturing detailed truck information. Axle spacing and weight from existing WIM devices and unique inductive signatures indicative of body type from ILDs equipped with high sampling rate detector cards are complementary data sources that can be integrated to provide a synergistic resource that otherwise does not exist in practice, a resource that is able to overcome the drawbacks of the traditional truck data collection methods by providing data that is detailed, link specific, temporally continuous, up-to-date, and representative of the full truck population. This integrated data resource lends itself very readily toward detailed truck body classification which is presented as a case study. This body classification model is able to predict 35 different trailer body types for FHWA class 9 semi-tractors, achieving an 80 percent correct classification rate. In addition to the body classification model, the large data set resulting from the case study is itself a valuable and novel resource for truck studies.
Phd Dissertation
Shared-use mobility systems, which enable users to have short-term access to transportation modes on an on-demand basis, have experienced tremendous growth over the last decade. However, most of the existing systems suffer from two confounding issues: the lack of modeling tools to understand, simulate and predict their behavior and the lack of integration with the existing transit network. To address those issues, this dissertation focuses on investigating the operational challenges of bikesharing systems, with an emphasis on the rebalancing operations and the modeling of a new mobility concept, Car2work, which builds upon existing carsharing ideas and successfully integrates with existing transit networks. A methodological framework to solve the bikesharing rebalancing problem is proposed. The novelties of the approach are that it is proactive instead of reactive, as the bike redistribution occurs before inefficiencies are observed, and uses the outputs of a demand-forecasting technique to decompose the inventory and the routing problem. The decomposition makes the problem scalable, responsive to operator inputs, and able to accommodate user-specific models. Simulation results based on data from the Hubway bikesharing system show that system performance improvements of 7% in the afternoon peak could be achieved.Car2work main goal is to connect commuters with workplaces while leveraging the line-haul capabilities of existing public transit systems and guaranteeing a trip back home, efficiently tackling the “last mile” problem that is a limiting characteristic of public transit. It differs from the traditional dynamic-ridesharing approaches because it is designed for recurrent commuting trips where commuters announce their (multiple) trips in advanced and an automated all-or-nothing matching strategy is performed, guaranteeing a ride home. The problem is formulated as a pure binary problem that is solved using an aggregation/disaggregation algorithm that renders optimal solutions. The solution approach is based on decomposing the problem into a master problem and a sub-problem, reducing the number of decision variables and constraints. As a result, various instances of the problem can be solved in reasonable amount of time, even when considering the transit network. The model can be used to simulate a widespread implementation of the concept.
book/book chapter