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The focus of this study is on determining the change in capacity requirements and desirable shelter locations as a result of link capacity changes during evacuation. A cell transmission-based system optimal dynamic traffic assignment (SO-DTA) formulation first proposed by Ziliaskopoulos is extended by introducing probabilistic capacity constraints. The p-level efficient points method first proposed by Prékopa is used to deal with probabilistic capacity constraints of the proposed stochastic SO-DTA model. The model captures the probabilistic nature of link capacities that change in response to the impacts of events such as hurricanes and earthquakes that can destroy or damage highway links. First, a simple single-destination example network is studied to show the effectiveness of the proposed model. Then the impact of using stochastic and deterministic link capacities is also analyzed with a simplified multiple-origin, multiple-destination version of the Cape May, New Jersey, network. Desirable shelter locations are evaluated by letting the stochastic SO-DTA model assign flows generating the minimum systemwide travel time. The results indicate that introducing probabilistic link capacities can adjust the overall flow in the network as well as shelter utilization. Thus, if planners consider the predictions of the deterministic model, they may face the risk of not having sufficient food, medicine, and other emergency supplies in shelters. This paper suggests a more realistic approach to evacuation planning to avoid the inefficiencies that created problems after such recent major disasters as Hurricane Katrina and the tsunami in Southeast Asia.

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This paper investigates temporal and weather-related variation in taxi trips in New York City. A taxi trip data-set with 147 million records covering 10 months of activity is used. It is shown that there are substantial variations in ridership, taxi supply, trip distance, and pickup frequency for different time periods and weather conditions. These variations, in turn, cause variations in driver revenues which is one of the main measures of taxi supply-demand equilibrium. The findings are then used to discuss the anticipated impacts of two recently enacted taxi regulation changes: the first fare increase since 2006 and the E-Hail pilot program which allows taxi hailing with smart phone applications. The fare increase is estimated to cause varying levels of revenue increase for different time periods. E-Hail apps are not expected to offer considerable improvements at all times, but rather when both adequate taxi supply and demand occur simultaneously.

Travel time reliability in New York City was analyzed with three travel time reliability measures. A classification and regression tree model was used for the analysis. Instead of analysis of conventional peak and off-peak periods, day-of-week (DOW) and time-of-day (TOD) periods were determined on the basis of each travel time reliability measure. DOW and TOD periods were identified on the basis of average travel time and each selected measure. Travel time reliability measures formulated to explain the same phenomenon classified different periods as having similar characteristics. The results agreed with the literature that reliability measures should be based on temporal periods such as DOW and TOD; however, the selection of time periods should be measure specific. The impact of New York City's urban grid network on travel time and speed distributions is also discussed. The travel time distribution patterns reported in the literature for freeways do not exist for the city. Therefore, caution is suggested for transferring reliability measures across different network structures.

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This paper provides an evaluation of taxi dispatching procedures at New York City's John F. Kennedy International Airport (JFK). Curbside data collection and interviews with airport stakeholders were conducted to describe and quantify conditions for taxi drivers and passengers at JFK. A literature review was performed to identify operational similarities and differences between JFK and other high-volume airports with centralized taxi dispatching and to identify potential solutions for application at JFK. The outcomes of this study include (a) characterization of relationships between airportwide and terminal-level passenger demands and available taxi supply at JFK, (b) identification of sources of inefficiency in existing taxi dispatching procedures and taxi operations, and (c) identification of approaches for addressing supply–demand imbalances and next steps in evaluating those approaches.