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From page 87... ... C-1 Case Studies Case Study Approach The research team conducted four case studies to demonstrate the asset condition/service quality framework described previously. The case studies were selected in consultation with the project panel and were intended to encompass as wide a set of operating characteristics and assets as possible. Read the entire page →
From page 88... ... C-2 The Relationship Between Transit Asset Condition and Service Quality of the effort in this case study was using data provided by the New England Transit Agency, and found in other available sources, to compute each of the parameters required for the EJT model. The headway standard deviation was approximated based on the transit agency's published schedule and on-time performance data, assuming headways and run times are distributed based on a gamma distribution, consistent with the approach used by Richter, Ilzig, and Rudnicki (2009) Read the entire page →
From page 89... ... Case Studies C-3 improvements remain the same. As shown in the table, the annual benefit for this case is approximately $1 million. Read the entire page →
From page 90... ... C-4 The Relationship Between Transit Asset Condition and Service Quality Results EJT Model Parameters for vehicle running time, running time distribution, and vehicle failures were used in the spreadsheet model to compute EJT in terms of minutes and dollars per passenger, given current conditions and under different scenarios in which buses are allowed to deteriorate over time. Table C-3 summarizes the journey time results. Read the entire page →
From page 91... ... Case Studies C-5 Further deterioration results in more significant increases, up to $1.20 per passenger for a 15-year increase in age accounting for the adjustment factor. Comparison to TAPT Although the per passenger cost increase resulting from asset deterioration may seem modest, it is a greater cost than that predicted by TAPT, given the simplified user cost model it uses. Read the entire page →
From page 92... ... C-6 The Relationship Between Transit Asset Condition and Service Quality note is the transit agency's farecard data. Through its farecard system, the transit agency recently began obtaining detailed travel time data for its rail system, with records of the origin, destination, start time (time of entry through the fare gate) Read the entire page →
From page 93... ... Case Studies C-7 • Analysis of Farecard Data. Estimated average North Line journey time using data for the "good" day. Read the entire page →
From page 94... ... C-8 The Relationship Between Transit Asset Condition and Service Quality As summarized in the table, the EJT for the North Line averages 28.5 minutes incorporating various adjustments for customer perceptions. Of this total, approximately half is for time spent in vehicles, 30% is for wait time, and the remainder is buffer time. Read the entire page →
From page 95... ... Case Studies C-9 For instance, for a trip from Station 12 on the North Line to Station 15 on the East Line made beginning at 8:05 AM, one would most likely transfer between lines at Station 16 on the North Line. For this trip, the North Line portion of the trip would be Station 13 to Station 16, and the time for this portion of the trip would be estimated based on the travel times for other passengers who made a trip from Station 13 to Station 16 (without making a transfer) Read the entire page →
From page 96... ... C-10 The Relationship Between Transit Asset Condition and Service Quality from 5:00 AM to 3:30 PM, the North Line journey time averaged approximately 31 minutes -- 69% greater than average. Figure C-4 is a heat map showing the data for the AM peak on the "good" day and using different colors to represent the North Line journey time between each North Line origin (horizontal axis) Read the entire page →
From page 97... ... Case Studies C-11 Figure C-6 shows the difference between the 2 days. Cases where there was little or no increase are shown in green. Read the entire page →
From page 98... ... C-12 The Relationship Between Transit Asset Condition and Service Quality 19.0 minutes for the AM peak period and 18.7 minutes overall for the day. Only the North Line portion of the journey time was predicted and that portion of the trip was estimated in the case of the farecard data. Read the entire page →
From page 99... ... Case Studies C-13 from the "bad" day suggest that most passengers were instead delayed in the station while waiting to board the train. The discrepancy in delay location does not affect the total significantly, but given the varying adjustments for waiting and IVT, the EJT model likely understates the effective time incurred. Read the entire page →
From page 100... ... C-14 The Relationship Between Transit Asset Condition and Service Quality Analysis Approach The case study analysis focused on the following two main aspects: • Explore the EEM as an enhancement or an alternative approach to EJT. To determine if user benefit factors from the EEM could be used to verify or enhance perfection factors in the EJT model, the EEM was used to compute an IVT for comparison with the In-Vehicle comfort factor used in the EJT model. Read the entire page →
From page 101... ... Case Studies C-15 If there was a real relationship between the relative age of the fleet and reliability as experienced by customers, one would expect satisfaction to decline as bus fleets get older. However, in this high-level view, the opposite is the case, indicating that other factors are at play in the satisfaction data, such as • Traffic conditions -- while Auckland has the youngest fleet, it also has the most congested traffic conditions • Driver behavior or other intangible factors relating to the journey • How well (or poorly) Read the entire page →
From page 102... ... C-16 The Relationship Between Transit Asset Condition and Service Quality References Richter, M.; Ilzig, K.; and Rudnicki, A Read the entire page →

From page 87...

... C-1 Case Studies Case Study Approach The research team conducted four case studies to demonstrate the asset condition/service quality framework described previously. The case studies were selected in consultation with the project panel and were intended to encompass as wide a set of operating characteristics and assets as possible.