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Archive for the FY 2016 Final Reports Category

Innovative Approach to Estimating Demand for Intercity Bus Services in a Rural Environment

Because existing models have their limitations, there is a significant need for a model to estimate demand for intercity bus services, especially in rural areas. The general objective of this research was to develop an intercity mode choice model that can be incorporated into a statewide travel demand model

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Florida TDM Clearinghouse

The Florida Statewide Transportation Demand Management (TDM) Clearinghouse provides technical assistance in support of Florida’s commuter assistance program to achieve Florida’s 2060 vision and goals as identified in the Florida Department of Transportation’s (FDOT) 2060 Florida Transportation Plan. The overall objective of the Clearinghouse is to improve

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Transit Service Reliability: Analyzing Automatic Vehicle Location (AVL) Data for On-Time Performance and Identifying Conditions Leading to Service Degradation Final

Transit agencies are increasingly using AVL data to support key performance indicators, for responding to service complaints, or for reviewing and improving the quality of service. The main objective of this study was to investigate the use of AVL data for improving transit service reliability: 1) improving

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Guidebooks for Estimating Total Transit Usage through Extrapolating Incomplete Counts

This report provides guidance for transit agencies to estimate transit usage for reporting to the National Transit Database (NTD) when their counting procedure that is designed to perform full counts misses some trips. Transit usage refers to unlinked passenger trips (UPT), passenger miles traveled (PMT), and average

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Estimating Ridership of Rural Demand-Response Transit Services for the General Public

The objective of this study is to develop a model for estimating demand for rural demand-response transit services for the general public. Lack of data for demand-response service characteristics and geographic coverage has limited the development of such models. This study developed two models for estimating demand.

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