In this project, the feasibility of using mobile traffic sensors for binary vehicle classification (i.e., to distinguish passengers from trucks) on arterial roads is investigated. Here mobile sensors refer to those that move with the traffic flow they are monitoring such as global positioning system (GPS), smart phones, among others. Features of vehicle dynamics (e.g. speed related, acceleration/deceleration related, among others) are extracted from vehicle traces collected from real world arterial roads.

In this research, the feasibility of using mobile traffic sensors for binary vehicle classification on arterial roads is investigated. Features (e.g. speed related, acceleration/deceleration related, etc.) are extracted from vehicle traces (passenger cars, trucks) collected from real world arterial roads. Machine learning techniques such as support vector machines (SVM) are developed to distinguish passenger cars from trucks using these features.

This report presents an overview of the Open Mode Integrated Transportation System (OMITS), introduces its key components and algorithms in the recent development and implementation, and demonstrates the working mechanism of dynamic transit service.

In the U.S., there are about 38 million licensed drivers over age 65; about 1/8 of our population. By 2024, this figure will DOUBLE to 25%. The current research is intended to address the driving capabilities of our older population, as accident and injury risk has been statistically shown to increase with advanced age. Our primary objective was to perform a preliminary Pilot Study (N=10) that allows our team to analyze the impact of supplementing traditional driver evaluation using state-of-the-art driving simulation technologies.

Statistics show that in the U.S., there are about 38 million licensed drivers over age 65; about 1/8 of our population. By 2024, this figure will DOUBLE to 25%. The current research is intended to address the driving capabilities of our older population, as accident and injury risk has been statistically shown to increase – normalized per mile driven – with advanced age.

This research project seeks to increase knowledge about coordinating effective multi-modal evacuation for disasters. It does so by identifying, evaluating, and assessing current transportation management approaches for multi-modal evacuation planning. The research increases equity by identifying strategies for evacuation of all residents, including carless residents during a disaster.

The University Transportation Research Center - Region 2, supported a study entitled “Barriers to Resource Coordination for Multi-Modal Evacuation Planning.” Extreme events that require large-scale evacuation are a great concern for disaster planners and emergency managers; most state and local municipalities are ill-prepared to handle large-scale evacuations.