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Page 73
Suggested Citation:"Chapter 8 - Conclusions." 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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Page 73
Page 74
Suggested Citation:"Chapter 8 - Conclusions." 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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Page 74
Page 75
Suggested Citation:"Chapter 8 - Conclusions." 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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Page 75

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73 Conclusions Maintaining transit assets, such as vehicles, guideway, and facilities in good repair enables transit agencies to maximize the quality of the service they provide and minimize transit agency and passenger costs over time. As assets deteriorate, they tend to become more unreliable, require more maintenance, and become more likely to fail in service. Likewise, improving asset conditions typically results in reduced maintenance costs and increased asset reliability. Passen- gers’ perceptions of the conditions of transit vehicles and facilities can affect the overall quality of their journey, and, regardless of those perceptions, passengers may experience increased delay as a result of asset failures. Research Highlights Previously, the link between transit service quality and asset condition, while understood at an intuitive level, was not well quantified. The research described in this report explores the dif- ferent dimensions of transit service quantity, describing how these may relate to physical asset conditions. Also, the researchers propose an overall measure of service quality termed Effective Journey Time (EJT). This measure captures the total time a passenger spends for a transit trip, with adjustments for the quality of the time from the passenger’s perspective. It is conceptually similar to other service quality measures described in the literature, most notably the London Underground Journey Time Metric (TfL 2014). Asset conditions affect EJT in two basic ways: through asset failures that may generate delay and through adjustments for customer percep- tions that vary as a function of condition. Four case studies were performed using EJT to show the effects of changes in asset conditions for vehicles and facilities for bus and rail systems. Based on the experience gained from the case studies, step-by-step guidance has been developed for evaluating the effects of asset condition changes on service quality. The guidance is supported by a set of spreadsheet tools for calculating EJT using either a simplified or comprehensive approach. The EJT tools provided are intended for analysis of specific changes in condition resulting from deterioration or improvement of a specified set of assets. The tools are directly applicable for helping quantify benefits of a given investment. Further, the simplified version of the tool can be used to quickly characterize overall changes in EJT predicted basic changes in fleet age and speed restrictions. Table 8-1 summarizes a set of scenarios generated using default values for the simplified EJT tool. As indicated in the table, replacing a fleet is predicted to reduce EJT by 5%. Allowing a fleet to increase in age such that the average age is equal to vehicle useful life is predicted to increase EJT by 8 to 12%. Allowing fleet age to increase to one-and-a-half times useful life causes EJT to increase by 38% for buses, 63% for heavy rail, and 101% for light rail. In the case of rail systems, severe speed restrictions caused by deteriorated track conditions cause a C H A P T E R 8

74 The Relationship Between Transit Asset Condition and Service Quality further increase in EJT of 6 to 8%, relative to the base case with minimal speed restrictions. The EJT changes can be equated to user costs by multiplying EJT by the value of time. Transit agencies can use the research to perform their own analyses of potential service quality impacts for internal use, or to share with planning partners, legislators, and the public. Alter- natively, they can reference the case studies or illustrative results provided with the guidance to demonstrate representative effects of reductions in quality resulting from asset deterioration and improvements from improving asset conditions. Opportunities for Further Research Although the results of the research are intended to be of immediate value for transportation agencies, several areas have been identified through this effort where additional research may be merited and/or where there are general opportunities for improving data collection, aggrega- tion, and reporting that could provide a better assessment of service quality impacts over time. These include • Developing standards for transit service quality calculations and key supporting param- eters. EJT is proposed as an overall measure of service quality, because no other standard measure has been established in the United States for combining the different dimensions of service quality. Transit agencies would benefit from development of standard measures of service quality, be it EJT or some other measure. To the extent that the measure incorporates adjustments for perceptions of different types of time spent by a passenger (e.g., sitting versus standing versus walking), it should include recommendations for standard factors to use for such adjustments. • Establishing an archive for AVL data. Some transit agencies make their performance data publicly available. The opportunity exists for an independent body to begin archiving this information and developing summaries across modes, routes, and agencies summarizing service performance. Such an archive would be a valuable resource for supporting calcula- tions of vehicle headways and headway standard deviations, for documenting changes in performance over time, and for various other applications. • Better quantifying the link between passenger perceptions and asset condition. The EJT model includes an adjustment for IVT spent on a vehicle in poor condition based on research performed in New Zealand (Douglas 2016, Steer Davies Gleave 1991). Attempts to bolster this research with data from customer satisfaction surveys obtained in the case Scenario Description Change in EJT Relative to Base Case (Average Fleet Age Equal to 0.5X Useful Life, Minimal Speed Restrictions for Rail) Bus Light Rail Heavy Rail New Fleet -5% -5% -5% Average Fleet Age Equal to Useful Life 8% 12% 10% Average Fleet Age Equal to 1.5X Useful Life 38% 101% 63% Extensive Speed Restriction (25% of guideway operating at restricted speeds) N/A 6% 6% Extensive Speed Restriction, Average Fleet Age Equal to Useful Life N/A 19% 17% Extensive Speed Restriction, Average Fleet Age Equal to 1.5X Useful Life N/A 109% 70% Table 8-1. Change in EJT predicted at a network level using the simplified EJT model.

Conclusions 75 studies were unsuccessful. Further research is needed to better determine how asset condi- tions affect perceptions, independently from the actual performance of the asset. Ideally this research should extend beyond vehicles and include perceptions of transit facilities and other customer-facing assets. • Improving data on asset failure rates. Data from the NTD and other sources document vehicle failure rates in terms of MDBF. However, this measure is strongly influenced by operating and maintenance practices that vary among transit agencies. Very limited data are available on other assets besides vehicles (e.g., guideway and facility components). Further research is needed to better quantify asset failure rates. In the case of vehicles, this research would ideally establish the frequency of different types of repairs and separately detail the likelihood that a vehicle requiring repair will fail in service. • Extending the EJT model. To the extent that the EJT model presented in this report pro- vides a valuable resource for characterizing transit service quality, various extensions and enhancements to the model may be of value. These include better treatment of infrequent service, improved modeling of cascading delays resulting from a vehicle or guideway failure, and prediction of EJT across multiple modes or lines.

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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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