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102 Reliability Project L02 within SHRP 2 was undertaken to cre ate methods by which travel time reliability can be monitored, assessed, and communicated to end users of the transporta tion system. The project developed guidance for operating agencies about how they can put reliability measurement methods into practice by enhancing existing monitoring sys tems or creating new ones. The projectâs main product is a guide that describes how to develop and use a TTRMS. A set of supporting appendices provide additional detail not found in the Guide. Travel time reliability is the absence of variation in travel times. If a system is reliable, people can get to where they want to go, when they want to be there, all the time. If a freeway is reliable, then its travel times are the same under all condi tions, all year long. It is similar to a vehicle that always starts when the key is turned on. Of course, no system or roadway is perfectly reliable; this project is intended to address this challenge. L02 focused on how to measure reliability, how to under stand what makes a system unreliable, and how to pinpoint mitigating actions. For example, a TTRMS will indicate the effects of congestion and whether operational actions miti gate the impacts. TTRMS analysis methods will let managers know if and how traffic incidents, weather, and other non recurring events affect reliability, and the extent of the effect. When actions are taken to mitigate unreliability, such as widening the shoulders or deploying more roadside assis tance trucks, the TTRMS will show the effects of those miti gations. For a discussion about selecting mitigation strategies, see Margiotta (2010); for a guide to effective freeway perfor mance measurement, see Margiotta et al. (2006). Figure 8.1 shows the travel times for a specific trip in the San Diego area that would have been experienced by some one who left at exactly the same time every weekday. It is clear from this figure that the travel times on this road way segment are not always the same; the system is unreliable. Not only does the travel time vary, but the spread in the times varies. At about midnight, the minimum and maximum are only 5 minutes different (50 minutes versus 55 minutes), but they differ by 110 minutes during the weekday afternoon peak (50 minutes versus 160 minutes). It is also clear that non recurring events have an impact. A good example is adverse weather, especially during the peak period. Traffic incidents also have an effect on travel time reliability, as do special events and unusually high demand. Even when no nonrecurring event is happening (the âno eventsâ data points), the travel times can vary widely. The TTRMS helps indicate when, why, and by how much travel time will vary. A TTRMS is designed to be an addÂon to an existing traf fic management system; its structure is shown in Figure 8.2. Inside the dotted line box are the three major modules of the TTRMS: a data manager, a computational engine, and a report generator. The data manager assembles incoming informa tion from traffic sensors and other systems, such as weather data feeds and incident reporting systems, and places them in a database that is ready for analysis as âcleaned data.â The computational engine works off the cleaned data to prepare pictures of the systemâs reliability: when it is reliable, when it is not, to what extent, under what conditions, and so forth. In the figure this is illustrated by âregime TTÂPDFs.â The report generator responds to inquiries from usersâsystem managers or travelersâand uses the computation engine to analyze the data and provide information that can then be presented to the inquirer or decision maker. Each of these modules is discussed and described in the Guide. In addition, case studies and use cases illustrate how these modules work together to produce answers to questions that managers would likely pose. The appendices provide further details about how each of the modules should work together and separately. Figure 8.3 shows an example of what to expect as a report from the TTRMS. The plot shows the distribution of travel C h a p t e r 8 Summary and Conclusions
103 times on I-8 westbound in San Diego across a 3-month period under various operating conditions. The distributions are shown in a cumulative fashion; the location of each line shows how many travel times are that value or shorter. For example, when traffic incidents occur during heavy (recur- rent) congestion, one-half (50%) of the travel rates (in sec- onds per mile) are up to 70 s/mi. That is, 50% of the travel rates are this long or smaller. The 90th percentile travel rate is 110 s/mi. Or put another way, nine out of every 10 vehicles is traveling at that rate or faster. The value in the results comes from comparing one distri- bution with another. For example, analysts can compare the distribution for high recurrent congestion and traffic incidents with high recurrent congestion without incidents. Without incidents, 50% of the vehicles are traveling at 58 s/mi instead of 70 s/miâconsiderably faster. And at the 90th percentile, the difference is even more dramatic: 65 s/mi versus 110 s/mi. Not only does the figure indicate that the difference between the two conditions is dramatic, but it also suggests that tak- ing actions to mitigate these impacts would produce signifi- cant benefits in terms of improving reliability. The mitigating actions would be intended to cause the travel times (or travel rates) during incidents to get much closer to those when there are no incidents. Moreover, after the mitigating actions were taken, the TTRMS would be able to show how reliability improved. In conclusion, a TTRMS will help an agency understand the reliability performance of their systems and monitor how 0 20 40 60 80 100 120 140 160 180 0:00 6:00 12:00 18:00 0:00 Tr av el T im e (m in ) Time of Day (hr:min) No Events Incidents Demand Special Events Weather Figure 8.1. Variation in travel times by time of day across a year. Color figure available online at www.trb.org/Main/Blurbs/168765.aspx. Figure 8.2. Reliability monitoring system overview, with boxes for modules and circles for inputs and outputs.
104 reliability improves over time by answering the following questions: ⢠What is the distribution of travel times in the system? ⢠How is the distribution affected by recurrent congestion and nonrecurring events? ⢠How are freeways and arterials performing relative to per formance targets set by the agency? ⢠Are capacity investments and other improvements really necessary given the current distribution of travel times? ⢠Are operational improvement actions and capacity invest ments improving the travel times and their reliability? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 40 50 60 70 80 90 100 110 120 130 140 Cu m ul ati ve Pr ob ab ili ty Travel Rate (sec/mi) Weather High Incident High Special Events Mod Incident Mod Weather Low Weather Mod Demand High DemandMod Normal Low Normal Uncongested Normal High Figure 8.3. Effect on travel rates of congestion and nonrecurring incidents.
