The New York City Off-Hour Delivery (NYC OHD) program is the work of a private-public-academic partnership-a collaborative effort of leading private-sector groups and companies, public-sector agencies led by the New York City Department of Transportation, and research partners led by Rensselaer Polytechnic Institute. The efforts of this partnership have induced more than 400 commercial establishments in NYC to accept OHD without supervision. The economic benefits are considerable: the carriers have reduced operational costs and parking fines by 45 percent; the receivers enjoy more reliable deliveries, enabling them to reduce inventory levels; the truck drivers have less stress, shorter work hours, and easier deliveries and parking; the delivery trucks produce 55-67 percent less emissions than they would during regular-hour deliveries, for a net reduction of 2.5 million tons of CO2 per year; and citizens' quality of life increases as a result of reduced conflicts between delivery trucks, cars, bicycles, and pedestrians, and through the use of low-noise delivery practices and technologies that minimize the impacts of noise. The total economic benefits exceed $20 million per year. The success of the OHD program is due largely to the policy design at its core, made possible with the behavioral microsimulation. This unique optimization-simulation system incorporates the research conducted into an operations research/management science tool that assesses the effectiveness of alternative policy designs. This enabled the successful implementation of the project within the most complex urban environment in the United States.

Emissions produced by urban transportation activities are harmful to people's health and they also affect people's trip-making decisions. In this paper, we explore the multiple equilibrium behaviors considering human exposure to vehicular emissions. We assume that a portion of transportation users are environmental advocates and their route decisions are based on some composite cost functions comprise of a travel time component and an emission exposure component. We then study the multiple equilibrium behaviors with multiple types of users on a traffic network. The multiple equilibrium problems are further converted into variational inequality (VI) problems and they are solved using a method of successive average- (MSA-) based diagonalization method. Per the specific network setting, we find that as travelers become more concerned about their exposure to vehicular emissions, the system emission exposure, travel time, and the total cost get reduced; i.e., Pareto improving solutions are achieved. By analyzing the multiple equilibrium behaviors, we find that the system gets better if more users become environmental advocates. And the change of a small percentage of users should already lead to a good system improvement.

This study concerns about modeling the mixed equilibrium (ME) problem, including user equilibrium, system optimum, and Cournot-Nash players, with general common constraints (CCs) on transportation networks. The CCs capture the interactions of the decision variables of different players in ME, which could be internal interactions such as road link capacity constraints or external such as emission or congestion control policies. It is shown that ME with CCs can be modeled as a generalized Nash equilibrium problem (GNEP). The study proves that, under certain conditions, the GNEP-based ME is jointly convex, which can be reformulated as a variational inequality (VI). We then study the solution existence, uniqueness and solving method for the VI-based model, followed by a discussion on its potential applications. Numerical tests are conducted with common nonlinear link emission constraints as the CCs on a simple two-node, three-link network first, and then on the Nguyen Dupus network. The results show that modeling users’ route choice behavior with CCs is more general in evaluating system performance, planning link capacities, and making congestion or emission control related policies.

Urban freight transportation is crucial to the quality of life which at the same time also produces significant externalities. This paper proposes procedures and methods of using second-by-second GPS data for urban freight performance evaluation. The evaluation targets on three important measures of urban freight activities, including mobility, fuel consumption, and emissions. Based on detailed GPS trajectories, the vehicle mobility can be characterized using measures such as the number of deliveries made, service times at delivery stops, and the trip segment travel time between delivery stops, and between delivery stops and the warehouse. The fuel consumption and emissions can also be estimated using micro-scale emission models. A case study is conducted using GPS data provided by a grocery company with chaining stores in the New York metropolitan area. The case study justifies the feasibility of using GPS data for freight performance evaluation. The results also reveal that certain innovative freight policies (such as off-hour deliveries) could help improve the efficiency of urban deliveries and reduce vehicle fuel consumption and emissions.