Browsing by Subject "Route model"

Now showing 1 - 2 of 2
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    Data-Driven Support Tools for Transit Data Analysis, Scheduling and Planning
    (Intelligent Transportation Systems Institute Center for Transportation Studies, 2011-07) Liao, Chen-Fu
    Many transit agencies in the U.S. have instrumented their fleet with Automatic Data Collection Systems (ADCS) to monitor the performance of transit vehicles, support schedule planning and improve quality of services. The objective of this study is to use an urban local route (Metro Transit Route 10 in Twin Cities) as a case study and develop a route-based trip time model to support scheduling and planning while applying different transit strategies. Usually, timepoints (TP) are virtually placed on a transit route to monitor its schedule adherence and system performance. Empirical TP time and inter-TP link travel time models are developed. The TP-based models consider key parameters such as number of passengers boarding and alighting, fare payment type, bus type, bus load (seat availability), stop location (nearside or far side), traffic signal and volume that affect bus travel time. TP time and inter-TP link travel time of bus route 10 along Central Avenue between downtown Minneapolis and Northtown were analyzed to describe the relationship between trip travel time and primary independent variables. Regression models were calibrated and validated by comparing the simulation results with existing schedule using adjusted travel time derived from data analyses. The route-based transit simulation model can support Metro Transit in evaluating different schedule plans, stop consolidations, and other strategies. The transit model provides an opportunity to predict and evaluate potential impact of different transit strategies prior to deployment.
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    Field Testing and Evaluation of a Wireless-Based Transit Signal Priority System
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, 2011-10) Liao, Chen-Fu; Davis, Gary A.
    Most signal priority strategies implemented in various U.S. cities used sensors to detect buses at a fixed or preset distance away from an intersection. Traditional presence detection systems, ideally designed for emergency vehicles, usually send a signal priority request after a pre-programmed time offset as soon as transit vehicles are detected without the consideration of bus readiness. As part of the Urban Partnership Agreement, Metro Transit, Minnesota Department of Transportation (MnDOT), and the City of Minneapolis have implemented Transit Signal Priority (TSP) along Central Avenue from north Minneapolis (2nd Street SE) to south of I-694 (53rd Avenue NE) with total of 27 intersections. Transit performance before and after the deployment of a TSP strategy was examined through the data analysis process to evaluate the effectiveness and benefit of a TSP strategy. The objective of this study is to deploy and validate a wireless-based TSP strategy developed from earlier studies by considering bus schedule adherence, location and speed. A TSP onboard system using embedded computer was developed to interface with EMTRAC radio modules to bypass the EMTRAC TSP algorithm on current buses. Field experiments were performed by installing University of Minnesota (UMN) TSP units on four RTE10 buses for two weeks. The EMTRAC algorithm was temporary disabled on the test vehicles. Link travel time and node dwell time on the TSP equipped route segments are compared. The results indicated the UMN TSP algorithm gain additional 3-6% of travel time reduction as compared to other RTE10 buses operating during the two-week test period.