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dc.contributor.authorTolami Hemmati, Sahar 17:47:01 (GMT) 17:47:01 (GMT)
dc.description.abstractSignalized intersections are typically the capacity bottlenecks within urban road networks. The performance of signalized intersections is typically quantified on the basis of average vehicle delay and maximum queue lengths. In practice, these measures of performance are commonly estimated using tools that implement the methods from the Highway Capacity Manual. These methods, which have been derived from deterministic and stochastic queuing theory, estimate delay and queue length on the basis of geometry, signal timings, turning movement counts (TMC), vehicle stream composition, etc. The cost and effort required to acquire these data, and particularly the TMCs, result in TMCs being collected for a single day every several years. Thus, estimates of intersection performance are often several years out of date and do not capture day-to-day and seasonal variations in conditions that occur throughout the year. Many transit agencies have deployed Automatic Vehicle Location (AVL) and Automatic Passenger Count (APC) systems on their fleet of transit vehicle. This thesis proposes a methodology to estimate the stopped delay and maximum queue length at signalized intersections on the basis of archived AVL/APC data. This provides the advantage of being able to: (1) estimate intersection performance on the basis of field measurements rather than models; (2) no additional cost or effort is required to acquire the data; and (3) performance can be evaluated throughout the year. Unlike previous methods, the proposed methodology is applicable to intersections with near-side transit stations. The proposed model is evaluated using both simulation and field data and shown to provide satisfactory results.en
dc.publisherUniversity of Waterlooen
dc.subjectTraffic signal delayen
dc.subjectTransit vehicle operationsen
dc.subjectBus dwell timeen
dc.subjectAVL/APC applicationsen
dc.titleDelay and Queue Length Estimation at Signalized Intersections Using Archived Automatic Vehicle Location and Passenger Count Data from Transit Vehiclesen
dc.typeDoctoral Thesisen
dc.subject.programCivil Engineeringen and Environmental Engineeringen
uws-etd.degreeDoctor of Philosophyen

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