OTREC turned its education efforts on a decidedly younger crowd March 13: sixth graders. A class from Rochester, N.Y., visited Portland on a trip geared toward improving bicycling in their own community.

The students, from Genesee Community Charter School, visited the OTREC offices to learn about active transportation research methods. They took part in group exercises designed to get them thinking about the planning and engineering challenges of transportation systems set up to serve multiple transportation modes.

The highlight of the day came when the students took to Portland’s streets — OTREC’s living laboratory — to conduct research of their own. Armed with bicycle-counter tubes and infrared detectors, students counted cyclists and pedestrians passing on the Broadway cycle track on Portland State University’s campus.

Other students verified the technology with manual counters.

Students moved on to their next stop on a four-day tour of Portland with a better sense of what kind of data researchers collect and how they can use those data to inform policy. Given their experience — the students already have influenced their city on policy ranging from Erie Canal re-watering to an urban art corridor to skate parks — they stand a good chance of using Portland’s lessons to build a bike-friendly Rochester.

This fall, the Friday transportation seminar series at Portland State University has focused on data collection and how information is used to make transportation investments. The Oct. 26 seminar, with the University of Minnesota’s Greg Lindsey, covered tracking and modeling travel behavior.

Engineers and planners alike have relied on traffic counts for their traffic models, but data behind bike and pedestrian travel has been fuzzy. Now, researchers such as Lindsey are offering new methods for conducting bike and pedestrian counts on trails and multiuse paths.

With little guidance from the Federal Highway Administration, Lindsey said, most of the efforts in creating best practices have bubbled up from communities like the Twin Cities, chosen as Nonmotorized Transportation Pilot Cities. Lindsey and his researchers monitored six trails in Minneapolis, using inductive loops and infrared beams.

To address calibration problems and offer validity to their field numbers, Lindsey also sent students into the field to verify counts. The technology allowed for finer-grained detail, especially over a 24-hour period. OTREC Director Jennifer Dill noted, “Too much in the past we’ve lumped “bike and peds” together and your work and analysis is demonstrating that they truly are different modes, with different behaviors.”

Lindsey stressed the importance of conducting this type of research, and measuring our “bicycle miles traveled” and “pedestrian miles traveled” in...

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If you weren’t one of the 10,000 people who attended the Transportation Research Board’s Annual Meeting in January, there are fifty students and twenty faculty for PSU, UO, OSU and OIT who can tell you what they learned there.  OTREC's bright yellow lanyards made our presence especially visible! PSU student Brian Davis blogged about his experience, OTREC’s Jon Makler was interviewed in a local newspaper, and the Oregon “delegation” at the conference was covered by both local and national blogs. Team OTREC filed some daily debriefs, highlighting presentations on topics such as federal stimulus investments in Los Angeles and Vermont’s efforts to address their transportation workforce crisis with returning military veterans (as well as the...

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Abstract: We propose to decompose residential self-selection by understanding its formation process. We take a life course perspective and postulate that locations experienced early in life have a lasting effect on our locational preferences in life. In other words, what was experienced spatially is a key factor contributing to our residential self-selection and our preferences in residential locations are formed long before our own self-selection begins.  We further hypothesize that prior locational influence interacts with period effect such that the same location experienced in different periods may have distinct effects.  Using an empirically collected dataset in the New York Metropolitan Region, we estimated a series of models to test these hypotheses. The results demonstrate that prior locational influence precedes residential self-selection. Furthermore, we show a variety-seeking behavioral pattern resulted from locations experienced during adolescence.

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PRESENTATION SLIDES

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OVERVIEW

Every day transit riders ask the same question: when’s the next one coming? To answer this question, transit agencies are transitioning to providing real-time transit information through smartphones or displayed at transit stops. 

The proliferation of transit planning and real time arrival tools that have hit the market over the past decade is staggering. Yet with transit ridership on the decline, agencies can’t afford to ignore the importance of providing accurate, real time information to their customers. Real-time transit information improves the reliability and efficiency of passenger travel, but barriers have prevented some transit agencies from adopting the GTFSrealtime v1.0 technology. A new NITC-funded study in May led by Sean Barbeau of the University of South Florida seeks to...

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PRESENTATION ARCHIVE

OVERVIEW

With so many Probe-Data Vendors in the market, and the fact that each offers their own unique solutions, it can be challenging to identify which vendor(s) would best meet the needs of an organization. Based on a study prepared for the Seattle Department of Transportation, this presentation will provide highlights around eight Probe-Data Vendors and their capabilities, limitations, and quality of data.

KEY LEARNING OUTCOMES

  • An understanding of primary vendors that offer probe data and related products
  • Key probe data sources used to put together data summaries
  • An understanding of the types of platforms that exist and basic analytical capabilities
  • Data quality considerations from each of the vendors offering probe data

SPEAKER

Scott Lee, CEO, IDAX Data Solutions

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Summary: A growing concern related to large-truck crashes has increased in the State of Texas in recent years due to the potential economic impacts and level of injury severity that can be sustained. Yet, studies on large truck involved crashes highlighting the contributing factors leading to injury severity have not been conducted in detail in the State of Texas especially for its interstate system.  In this study, we analyze the contributing factors related to injury severity by utilizing Texas crash data based on a discrete outcome based model which accounts for possible unobserved heterogeneity related to human, vehicle and road-environment. We estimate a random parameter logit model (i.e., mixed logit) to predict the likelihood of five standard injury severity scales commonly used in Crash Records Information System (CRIS) in Texas – fatal, incapacitating, non-incapacitating, possible, and no injury (property damage only). Estimation findings indicate that the level of injury severity outcomes is highly influenced by a number of complex interactions between factors and the effects of the some factors can vary across observations. The contributing factors include drivers’ demographics, traffic flow condition, roadway geometrics, land use and temporal...

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Abstract: Existing regional travel forecasting systems are not typically set up to forecast usage of bicycle infrastructure and are insensitive to bicyclists' route preferences in general. We collected revealed preference, GPS data on 162 bicyclists over the course of several days and coded the resulting trips to a highly detailed bicycle network model. We then use these data to estimate bicyclist route choice models. As part of this research, we developed a sophisticated choice set generation algorithm based on multiple permutations of labeled path attributes, which seems to out-perform comparable implementations of other route choice set generation algorithms. The model was formulated as a Path-Size Logit model to account for overlapping route alternatives. The estimation results show compelling intuitive elasticities for route choice attributes, including the effects of distance and delay; avoiding high-volumes of vehicular traffic, stops and turns, and elevation gain; and preferences for certain bike infrastructure types, particularly at bridge crossings and off-street paths. Estimation results also support segmentation by commute versus non-commute trip types, but are less clear when it comes to gender. The final model will be implemented as part of the regional travel forecasting system for Portland, Oregon, U.S.A.

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