Anonymous probe vehicle data are currently being collected on major interstates and arterials throughout the United States. Probe data are used to assign average speeds to pre-define roadway segments of varying lengths. These segments are known as Traffic Message Channels (TMCs).Through the analysis of this probe data for each TMC, transportation agencies have been making progress in the development of agency wide performance measures to better plan and manage spatially distributed infrastructure assets (1, 2).

In 2012, there were 139 incidents in which people got hit by subway trains in New York City, compared with 146 in 2011. Most of the victims slipped or fell or went on to the tracks to fetch personal belongs. A promising approach to reduce future occurrence of such tragedies is distributed sensor nodes that detect obstacles and monitor train motion [1]. In such applications, an important limitation is the near impossible task of maintaining numerous sensors and microsystems.

Activity-based travel demand models, which have their theoretical foundation explicitly rooted in the accepted notion that travel is derived from daily activities, offer an attractive, but complex, alternative to the conventional four-step forecasting models. Recent implementations of such approaches via microsimulations have shown promise for practical application of this theoretical construct. I propose a one-year study that develops a framework of an activity-travel microsimulator based on the Household Activity Pattern Problem (HAPP).

In recent years, there is growing interest in promoting the smart growth concept that aims at revitalizing land use and transportation patterns to avoid “sprawl” and to reduce negative externalities of transportation systems. The fundamental idea behind such a concept is to revitalize land use and transportation patterns to avoid “sprawl” and to replace it by safe, livable, sustainable, environmentally-sound, and green-mode-oriented communities.

Research Problem and Background

The overall objectives of the Integrated Incident Management System (IIMS) are to collect and communicate incident data (Location, type/ severity, digital images, and expedited reporting among agencies providing emergency response and incident/traffic/transit/emergency management services for the purpose of improving the incident response and clearance process, thus reducing congestion/emissions and enhancing highway safety to both the responder and multiple users along the transportation corridors.