Gridlock along arterial systems in New England is often the result of adverse weather conditions, which can render the normal signal plans unsuitable. The current study has two main objectives: ( a) to assess the impact of inclement weather on traffic flow parameters at signalized intersections in northern New England and ( b) to evaluate the likely operational benefits of implementing special timing plans for inclement weather conditions. A signalized intersection in Burlington, Vermont, was selected, and at least 30 h of videotaped data were collected over a period of 3 months. The weather and road surface conditions were categorized into six different classes, and values for the saturation headways and start-up lost times were collected and statistically analyzed. A signalized collector was then selected as a case study for evaluating the likely benefits of developing special timing plans for inclement weather. Optimal signal plans were developed for the corridor for the six different weather conditions. The likely benefits of the special plans were then determined by comparing travel conditions under the optimal inclement weather timing plans with conditions assuming the optimal dry-condition plan would remain unchanged. The study reveals that inclement weather has a significant impact on saturation headways, particularly once slushy conditions start. Start-up lost time, however, does not appear to be significantly affected by inclement weather. The study also shows that there are benefits to be expected from implementing special signal plans for inclement weather.

Existing transportation planning modeling tools have critical limitations with respect to assessing the benefits of intelligent transportation systems (ITS) deployment. In this article, we present a novel framework for developing modeling tools for quantifying ITS deployments benefits. This approach is based on using case-based reasoning (CBR), an artificial intelligence paradigm, to capture and organize the insights gained from running a dynamic traffic assignment (DTA) model. To demonstrate the feasibility of the approach, the study develops a prototype system for evaluating the benefits of diverting traffic away from incident locations using variable message signs. A real-world network from the Hartford area in Connecticut is used in developing the system. The performance of the prototype is evaluated by comparing its predictions to those obtained using a detailed DTA model. The prototype system is shown to yield solutions comparable to those obtained from the DTA model, thus demonstrating the feasibility of the approach.

A methodology is presented that emulates the transportation improvement planning process using mathematical optimization techniques. The scheduling problem is formulated as a mixed integer linear program (MILP) and can be considered as a multiperiod network design problem. The three primary model components are discussed: ( a) the input module in which the network, traffic demand, and pool of potential projects are identified over the planning horizon; ( b) the benefits estimation module using network travel time as the benefit criterion; and ( c) the schedule builder, an MILP that attempts to maximize the total benefits subject to annual resources and project precedence constraints. The proposed method is applied in a case-study context to the Lisbon metropolitan region’s network, a portion of Portugal’s highway network, and the results are discussed.

In this paper, meta-optimization and cellular automata have been introduced as a modeling environment for solving large-scale and complex transportation problems. A constrained system optimum combined trip distribution and assignment problem was selected to demonstrate the applicability of the cellular automata approach over classical mixed integer formulation. A mathematical formulation for the selected problem has been developed and a methodology for applying cellular automata has been presented. A numerical example network was used to illustrate the potential for using cellular automata as a modeling environment for solving optimization problems.