The National Academies Press

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From page 25... ... AVL or APC data apply to many of the performance measures listed in TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System (26) Read the entire page →
From page 26... ... Stop-level running time and scheduling Stop Running time net of holding time Stop Door open and close times, incident codes, control messages, on and off counts. 10% for mean running time; 100% for analysis and scheduling based on extreme values Speed and traffic delay Stop, interstop 10% Analyzing and Scheduling Running Time Dwell time analysis Stop Door open and close times, on and off counts, farebox transactions, incident codes. Read the entire page →
From page 27... ... 100% Shared route analysis, including headways and load on a trunk Varies Data structures for shared routes. Higher Level Analysis Geographic demand and service quality analysis Stop GIS with stop locations. Read the entire page →
From page 28... ... Yet, some route-period combinations need more than this standard, and others less, because they do not have the same running time variability. AVL data allows an agency to actually measure 95th-percentile running times and use that to set recovery times. Read the entire page →
From page 29... ... 4.4 Running Time Analyzing and scheduling running time is one of the richest application areas for archived AVL-APC data.Without AVL data, agencies must set running times based on small manual samples, which simply cannot account for the running time variability that comes with traffic congestion. Buses are scheduled at the timepoint level; therefore, scheduling demands timepoint data. Read the entire page →
From page 30... ... 4.4.1 Allowed Time, Half-Cycle Time, and Recovery Time A common analysis examines the distribution of observed running time for scheduled trips across the day compared with scheduled running time, also called "allowed time."An example is given in Figure 2, where the vertical bars show mean observed running time, the short lines show 85th-percentile values, and the arrows indicate maximum observed running times. Heavy horizontal lines show scheduled running time. Read the entire page →
From page 31... ... 4.4.3 Choosing Homogeneous Running Time Periods Another problem in running time analysis is choosing the boundaries of running time periods within which allowed time is constant. Establishing periods of homogeneous running time involves a trade-off between short periods within which scheduled running times match the data well versus longer periods of constant allowed time but greater variability.A common logic for resolving this trade-off is first to determine, for each scheduled trip, an ideal allowed time (e.g., mean running time, or 85th-percentile running time, depending on the desired feasibility criterion) Read the entire page →
From page 32... ... . In current practice, agencies with customer information systems like trip planners and real-time information systems estimate stop-level departure and running time by interpolation between timepoints; using stoplevel AVL data to develop stop-level schedules offers an obvious improvement. Read the entire page →
From page 33... ... How close the mean deviation is to zero indicates whether the scheduled running time is realistic. If the mean deviation suddenly jumps, it means the allowed segment time is unrealistic. Read the entire page →
From page 34... ... , generated by TriTAPT Figure 4. Schedule deviation along a route. Read the entire page →
From page 35... ... Passenger waiting time on routes with long headways is closely related to schedule adherence. Chapter 6 shows how it is possible to determine excess waiting time from the spread between the 2nd-percentile and 95th-percentile schedule deviation. Read the entire page →
From page 36... ... As part of this project, analyses of passenger waiting time based on headway data were developed (see Chapter 6) Read the entire page →
From page 37... ... Using established thresholds, trips can be categorized and counted by degree of crowding. In Figure 7, four of the scheduled trips in the period analyzed had no valid APC data. Read the entire page →
From page 38... ... In the future, there may be scheduling tools that account for within-day and between-day variation in demand, as well as within-day and between-day variation in running time, in order to design route schedules that respond to how both demand and running times vary across the day, using statistical methods to limit the probability of overcrowding and insufficient recovery time. 4.7.3 Passenger Crowding There is a strong relationship between vehicle crowding and passengers' experience of crowding, but the perspectives are different. Read the entire page →
From page 39... ... and unpublished studies by TriMet using AVL-APC data indicate that much of the variance in running time and schedule adherence can be explained by operator behavior. An analysis of performance by operator could be a valuable tool for training operators and for experimenting with different methods of supervision and control. Read the entire page →
From page 40... ... . Integrating AVL-APC data with GIS models requires data structures that link geographic locations to stops and route segments, and a process to extract and aggregate results for the selected stops and segments. Read the entire page →

From page 25...

... AVL or APC data apply to many of the performance measures listed in TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System (26)