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PacTrans Annual Report 2017

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PACTRANS STUDENT RESEARCHERS ASSIST FHWA WITH NEW TRAVEL TIME RELIABILITY DATA GUIDE Currently, researchers in the Smart Transportation Applications and Research Laboratory (STAR Lab) are working on development of a travel time reliability data guide (RDG) for the Federal Highway Administration (FHWA). The project team also includes members from Carnegie Mellon University and Leidos. The main purpose of the RDG is to help anyone who may ever need to do a study on travel reliability work with the data they need to complete their analyses from start (e.g., data collection) to finish (proper interpretation of results). Put simply travel time reliability is a measure of consistency in travel time along a certain path. It has been an area receiving increased attention after it was selected as a focus area under the second Strategic Highway Research Program (SHRP 2). An important operational measure, reliability is a critical metric as it can help allow road users to better plan their travel. For instance, a traveler from a suburban area to a downtown central business district would likely prefer to know their trip will take them one hour the vast majority of the time (even if the trip could be completed in a shorter time when considering only distance and not congestion level), than having no knowledge of how long of a trip they may be in for. Hence, travel time reliability is often studied by constructing distributions of travel times along certain routes that occur under a variety of conditions. Such distributions then allow inferences to be made; for example, "on route X, 95% of the travel times during the morning peak period on weekdays are less than Y minutes." With little thought, one can imagine numerous factors that can affect travel time reliability including weather, special events, work zones, incidents, inadequate base capacity, fluctuations in demand, and traffic control devices. Each of these factors has the ability to increase travel time on a certain route, or even network wide, beyond what would be experienced under ideal or freeflow conditions. Further, the magnitude of the factor and interaction between multiple factors can make it such that different travel times along the same route are observable under different conditions. As such, analysts studying reliability must answer a variety of questions about these factors in order to complete a proper, data-driven analysis of travel time reliability. For instance, they must decide what kind of data to acquire (e.g., crash data, weather data, volume data, etc.), where to get the data from, how to aggregate the data (what if weather data is only provided hourly, but traffic volume data is available every five minutes?, what if data are missing for certain time periods?), how to compute travel time (in many cases, travel times are not directly measured and only average speeds along adjacent segments are available), what metrics to analyze, and many others. Ultimately, however, most questions deal with collection, storage, cleaning, aggregating, processing/modeling, and interpretation of diverse, multi-source data. How to answer these questions properly and how to provide a uniform means of analyzing travel time reliability that is accessible to people with varying backgrounds and understandings of statistics and data science is precisely the goal of the RDG. 35 2014-2015 Annual Report

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