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« Previous: Chapter 3 - Framework for Relating Transit Asset Condition and Service Quality
Page 22
Suggested Citation:"Chapter 4 - Case Studies." National Academies of Sciences, Engineering, and Medicine. 2018. The Relationship Between Transit Asset Condition and Service Quality. Washington, DC: The National Academies Press. doi: 10.17226/25085.
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Suggested Citation:"Chapter 4 - Case Studies." National Academies of Sciences, Engineering, and Medicine. 2018. The Relationship Between Transit Asset Condition and Service Quality. Washington, DC: The National Academies Press. doi: 10.17226/25085.
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Suggested Citation:"Chapter 4 - Case Studies." National Academies of Sciences, Engineering, and Medicine. 2018. The Relationship Between Transit Asset Condition and Service Quality. Washington, DC: The National Academies Press. doi: 10.17226/25085.
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Suggested Citation:"Chapter 4 - Case Studies." National Academies of Sciences, Engineering, and Medicine. 2018. The Relationship Between Transit Asset Condition and Service Quality. Washington, DC: The National Academies Press. doi: 10.17226/25085.
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22 Case Studies Case Study Overview The research team conducted four case studies to demonstrate the asset condition/service quality framework described in Chapter 3. The case studies were selected in consultation with the project panel and were intended to encompass a wide set of operating characteris- tics and assets. In the summaries, the case study transit agencies remain anonymous, but are represented geographically. The four case studies and the outcome of each were as follows: • New England Transit Agency Heavy Rail Line—Used the EJT model to show the effect of vehicle and track improvements on effective passenger journey time and cost. • Western Transit Agency Bus Route—Compared the EJT model to the TAPT model. • Mid-Atlantic Transit Agency Heavy Rail Line—Compared journey time results from the EJT model to actual journey time data. • New Zealand Bus Systems—Explored an alternate and simplified model of journey time. To conduct each case study, the research team first met with the case study transit agency to confirm the scope of the work, review the condition/service quality framework described in Chapter 3, and collect data. The research team then prepared a brief memorandum confirming the scope and proceeded to perform the analysis. The case study results were described in write-ups detailing case study scope, inputs, outputs, and outcomes. Draft write-ups were provided to the participants and revised based on participant feedback. Additional details of the analysis approach and results for each case study are provided in Appendix C. Case Study 1: New England Transit Agency Heavy Rail Line The goal of this case study was to use the EJT model to show the effects of vehicle and track improvements on effective passenger journey time and cost. Specifically, EJT was used to com- pare the level of service on one rail line in the transit system before and after two significant improvements—new vehicles and track upgrades—were made to the line in the late 2000s. The New England Transit Agency provided most of the data used to compute each of the parameters required for the EJT model. Once the parameters were determined, the research team intro- duced the following variations in the parameters to compare level of service in 2006 and 2012, before and after the improvements had been made: • Vehicle failure rate. The failure rate was slightly lower in 2012 due to the new vehicles that replaced the old fleet. C H A P T E R 4

Case Studies 23 • In-vehicle comfort factor. Here an adjustment factor of 1.02 was used for 2012 (old vehicles). • Travel times. The travel times were longer in 2006 due to speed restrictions in place because of track issues. The EJT model predicted that improvements to both track and vehicles would save approximately 2.2 minutes per passenger mile in peak periods and 1.7 minutes per passen- ger mile in off-peak periods. Assuming a personal value of time of $12.55/hr (the FHWA default value for 2014), the total annual benefit was predicted to be approximately $18 mil- lion dollars. (This is benefit, not actual dollars earned or saved, and it is used as an expres- sion to standardize the value of time.) The results also showed that when improvements are made to vehicles only, while leaving track unimproved, the annual benefit is approximately $1 million. Both of these scenarios indicate that there is a significant benefit to journey time from changes made to the line between 2006 and 2012. A large portion of the benefit can be attributed to the reduction in slow zones resulting from track improvements. Replacing the vehicle fleet also resulted in gains. The new cars are assumed to enhance the customer experience, reducing effec- tive travel time cost slightly. Also, the new cars are less likely to fail in service. However, the lower failure rate per car is partially offset by the fact that there are a greater number of cars per train in the improved scenario. As a result, the benefit of replacing the fleet, though positive, is much smaller than that from improving track conditions. Case Study 2: Western Transit Agency Bus Route For the Western Transit Agency case study, the research team focused on a single, long, and highly reliable bus route with the goal of testing the EJT model against the TAPT model. Param- eters were determined for vehicle running time, running time distribution, and vehicle failure rate. Then, using data provided by the transit agency on the failure rate, failure duration, and the number of people on a bus, the research team calculated a baseline journey time on the rep- resentative route. To see how the journey time might change in the future, the team simulated the effect on EJT of a potential, future deterioration in condition. If the fleet is allowed to age 15 years, the model predicts that EJT per passenger will increase approximately 8%. This increase is largely a result of applying the in-vehicle comfort adjustment factor of 1.2 (which applies to IVT only and not wait time). If the adjustment factor is omitted, the increase is approximately 0.25%. Translating those results into a cost in terms of dollars per passenger based on a personal value of time of $12.55/hr indicates that an increase of 5 years in fleet age increases journey cost by just a penny per passenger. 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. 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. As a final step in the analysis, the research team converted the per passenger cost into a cost per bus and substituted this cost into TAPT, replacing its cruder estimate of user costs. With the EJT results included, user costs increase significantly as the fleet ages, and the cost-minimizing replacement age decreases from 14 years to 9 years. Although the user costs predicted from the EJT model are greater than that predicted with the TAPT model, several parameters are incorporated in the prediction of total lifecycle costs in TAPT, and it is not necessarily the case that adding EJT model results will always result in a shift in the cost-minimizing replacement age. Further, in this particular case, the TAPT results

24 The Relationship Between Transit Asset Condition and Service Quality obtained previously for the Western Transit Agency from Robert et al. (2014b) were not recali- brated to account for changes in fleet composition since the prior case study. Case Study 3: Mid-Atlantic Transit Agency Heavy Rail Line The goal of this case study was to directly compare modeled and actual journey time results in order to validate the EJT model. While not always possible, the research team leveraged the uniquely rich set of data available at the Mid-Atlantic Transit Agency, including farecard data, a detailed incident log, on-time performance data, and quarterly customer satisfaction survey results, to achieve this goal. The research team obtained journey time data for two specific dates: a representative “good” day with few delays, and a representative “bad” day with multiple guide- way and vehicle failures. The research team performed the following analyses as part of the case study: • North Line EJT Model. Developed an EJT model for the North Line representing typical operations of the line, as well as operations during the incident on the “bad” day. • Analysis of Farecard Data. Estimated average North Line journey time using data for the “good” day. Calculated effects on journey time for each incident on the “bad” day, comparing journey times encountered during each incident to the same period of the “good” day. • Comparison of Modeled and Actual Results. Compared EJT model results to farecard data to assess differences between modeled results and actual journey times recorded by the farecard system. Two basic questions were posed in comparing modeled and actual results for the Mid-Atlantic Agency’s North Line: • How well does the EJT model predict actual performance of the North Line on a typical day; and • How well does the model predict the level of service impacts of the guideway incident on the “bad” day? Regarding the predictions of the EJT model for a typical day, overall the model appeared to provide a very good approximation of the performance of the North Line on a typical day. The EJT model predicts an observed journey time (unadjusted wait time plus IVT) of 19.0 minutes per passenger for the AM peak period, and 18.4 minutes overall for the day. The estimated North Line journey time for the “good” day was 19.0 minutes for the AM peak period, and 18.7 minutes overall for the day. However, 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. The farecard data does not detail wait time versus IVT and does not adjust time for customer perceptions. Also, the farecard data does not provide information on buffer time passengers need to allow, although this could certainly be estimated by analyzing multiple days’ worth of farecard data. The EJT model also appears to do a reasonable job of approximating the effect of the guideway incident on the “bad” day. To simulate the incident in the EJT model, the headways on the line were increased and additional running time was added in each direction. The resulting model predicts an unadjusted journey time of 33.7 minutes. Based on the farecard data, calculations show that the actual journey time (North Line portion only) was 34.9 minutes. Overall the model matches the data relatively well, but there are several caveats. Because the delay was added to train running times, most of the delay was incurred as IVT. Accounts 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 overall, compared to a single line.

Case Studies 25 Case Study 4: New Zealand Bus Systems The goal of this case study was to explore the New Zealand Transport Agency’s (NZTA) Economic EEM (NZTA 2016) as a possible alternative (or enhancement) to the EJT model. The EEM describes a range of user benefits and provides a way to calculate the economic benefits of making improvements to transit services, such as reliability improvements, increased service frequency, interchange reduction, and other user benefits such as comfort. To determine if user benefit factors from the EEM could be used to verify or enhance adjustment factors in the EJT model, the EEM was used to compute an IVT for comparison with the In-Vehicle comfort fac- tor used in the EJT model. In addition, a benefit dollar value was computed from the EEM and compared to results from the EJT model in order to ascertain whether the EEM could be used as an alternative approach to EJT. The research team also sought to develop adjustment factors to address customer perceptions of asset conditions based on time series analysis. The research team considered an analysis of customer satisfaction and average fleet age, as well as, bus quality vs. age. Summary Findings The overall results from the case studies are as follows: • The EJT model proposed in Chapter 3 can be used to demonstrate effects of changes in asset condition. All of the case studies illustrate how declining conditions result in increased EJT and greater costs to transit users. In the New England Transit Agency, Western Transit Agency, and Mid-Atlantic Transit Agency case studies this was established by calculating EJT as described in Chapter 3 for a set of origin-destination pairs for a given route or line. For the NZTA case study a simplified, but conceptually similar, approach was used based on the EEM. • In some cases, transit agencies may have sufficient data to calculate the components of jour- ney time directly. The Mid-Atlantic Transit Agency case study illustrates that detailed farecard data can be used as an alternative to modeling to obtain journey time. At the same time, the modeled results for the Mid-Atlantic Transit Agency compared quite well to the calculated journey time results, suggesting that modeling may yield a reasonable approximation of jour- ney time where farecard data are unavailable. Another alternative is to use a simplified model that requires less data. The New Zealand case study demonstrated that a model similar to that in the EEM can be used to demonstrate benefits of improved condition. • Constructing alternative scenarios appears to be the most effective means for communicating analysis results. The case study experience suggests that showing results for different scenarios is more effective than showing how EJT trends upwards as asset conditions decline. The New England Transit Agency and Mid-Atlantic Transit Agency case studies, in particular, present compelling examples of the potential consequences of asset disrepair. • Efforts to establish the effect of asset condition on customer perceptions were inconclusive. The research team examined available customer satisfaction data for the Western Transit Agency, the Mid-Atlantic Transit Agency, and NZTA. In the case of the Western Transit Agency, there was not enough of a difference in condition between different sets of buses to observe a change in satisfactions. In the case of the Mid-Atlantic Transit Agency, satisfaction has declined in recent years, but the available data do not lend themselves to distinguishing between the effects of on-time performance and asset condition. Thus, detailed analyses were considered, but not performed, in these cases. Analysis of customer data was performed for the NZTA case study, but the results indicated that other factors are at play in the satisfaction data, making it difficult to discern a change in satisfaction as an asset ages.

Next: Chapter 5 - Guidance for Calculating Effects of Changes in Asset Condition on Transit Service Quality »
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TRB's Transit Cooperative Research Program (TCRP) Research Report 198: The Relationship Between Transit Asset Condition and Service Quality documents the development of a quantitative method for characterizing service quality and demonstrates how this quantitative measure varies with changes in asset condition. It provides guidance on how asset condition and transit service quality relate in terms of investment prioritization.

Three Excel spreadsheets–a simplified Effective Journey Time (EJT) Calculator, a comprehensive EJT Calculator, and a worked example demonstrating the use of the comprehensive EJT Calculator—provide quantitative methods. Transit agencies may use this report and tools to better manage existing transit capital assets and make more efficient and effective investment decisions.

Disclaimer - This software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences, Engineering, and Medicine or the Transportation Research Board (collectively "TRB") be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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