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As social media comes to permeate every aspect of modern life, public transit is no exception.

Transit agencies are increasingly making social media an integral part of their day-to-day management, using it to connect with riders about system alerts, live transit arrival information, service disruptions and customer feedback.

However, there is very little evidence to show how effective these efforts really are in achieving agency goals.

Measuring the Impacts of Social Media on Advancing Public Transit, a NITC project led by Jenny Liu of Portland State University, seeks to provide a better understanding of how transit agencies use social media and to develop some performance measures to assess the impacts of social media on promoting public transit.

This project aims to measure how social media actually impacts agency goals like increasing recruitment and retention of transit riders; increasing resources and customer satisfaction; addressing system performance efficiency; and improving employee productivity and morale.

A survey of 27 public transportation providers across the country found that although 94% of those surveyed agencies used some form of social media, only 28% had a social media plan or strategy prior to implementation.

Liu’s research explores the types of performance measures that could...

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A new NITC report from the University of South Florida makes it easier than ever for cities to collect useful bike data.

Cities like Portland, Oregon, Atlanta and San Francisco have started using smartphone apps to learn how people are using their bicycle infrastructure. The data can help planners decide what designs or upgrades are needed for the bicycle network.

The NITC project Rapidly Expanding Mobile Apps for Crowd-sourcing Bike Data to New Cities takes this idea a step further by creating a proof-of-concept multi-region architecture that would allow cities to share the same set of mobile apps, rather than each city launching its own.

This would significantly reduce the cost of deploying the apps.

Sean Barbeau of USF’s Center for Urban Transportation Research (CUTR) led the team in developing the open-source software that allows existing apps to communicate with regional servers.

With it, rather than each city having to modify and deploy their own iOS or Android app, all that a city would need to do is set up a server specific to their geographic area.

Having...

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Content Type: Professional Development Event

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New technologies such as smart phones and web applications constantly collect data on individuals' trip-making and travel patterns. Efforts at using these "Big data" products, to date, have focused on using them to expand or inform traditional travel demand modeling frameworks; however, it is worth considering if a new framework built to maximize the strengths of big data would be more useful to policy makers and planners.

In this presentation Greg Macfarlane will present a discussion on elements of travel models that could quickly benefit from big data and concurrent machine learning techniques, and results from a preliminary application of a prototype framework in Asheville, North Carolina.

Dr. Macfarlane is an analyst in the Systems Analysis Group of WSP | Parsons Brinckerhoff, developing and applying advanced travel demand models. His research and expertise includes trip-based models, activity-based models, integrated land-use/transport models, and micro-simulation of both travel...

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Heard of Vehicle Miles Traveled (VMT)? Wouldn’t it be great to know the corresponding value for walking and cycling?

This webinar discusses options for estimating the miles people walk and bicycle on the state-wide level, by investigating the practical considerations of trying to compute these values for one study state.

What strategies can be used, and what data sources do these require?

How do these strategies compare?

How do PMT/BMT estimates vary based on...

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A new NITC report introduces an important tool for safety analysis: a naturalistic method of data collection that can be used to improve the cycling experience.

Before now, most naturalistic studies (studies where data are collected in a natural setting, rather than a controlled setting) in bicycle safety research have been captured by stationary cameras and haven't followed cyclists along a route.

Researchers in this study used first-person video and sensor data to measure cyclists' reactions to specific situations.

Safety research in general has advanced significantly through naturalistic driving studies, which gather data from real drivers to illuminate the causes of traffic incidents both major and minor. For motorized vehicles, the U.S. Department of Transportation has been developing portable, vehicle-based data collection technologies since the early 1990s.

Portland State University researchers Feng Liu, Miguel Figliozzi and Wu-chi Feng sought to capture the cycling experience with physiological sensors and helmet-mounted cameras.

Their report, Utilizing Ego-centric Video to Conduct Naturalistic Bicycling Studies, offers a successful method for integrating video and sensor data to record...

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The City of Portland is exploring how distributed “Internet of Things” (IoT) sensor systems can be used to improve the available data that is usable by city engineers, planners, and the public to help inform transportation operations, enable assessments of public health and equity, advance Portland’s Climate Action Plan goals, and create opportunities for economic development and civic engagement.

The City is currently looking at how low-cost air quality sensors can be used to improve and increase real-time understanding of transportation-related pollutants. However, the state of low-cost air quality sensor technology is not usable off the shelf due to sensitivity limitations and interference issues.

This talk will share the results of a pilot evaluation study conducted by the Portland Bureau of Transportation (PBOT) along with background on other roadside air quality...

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NITC researchers have tested a method of collecting transportation behavior data using a smartphone app, with promising results.

The process could save transit agencies “hundreds of thousands of dollars,” says lead researcher Christopher Bone, and give them access to comprehensive, real-time data about their ridership, all without compromising passengers’ privacy.

Christopher Bone, Marc Schlossberg, Ken Kato, Jacob Bartruff and Seth Kenbeek of the University of Oregon designed a custom mobile application, which allows passengers to volunteer information about their travel habits, and recruited passengers to use it in a test case.

Their report, “Crowdsourcing the Collection of Transportation Behavior Data,” was released this month.

Download it here.

Participants were asked to use the app for three weeks on Lane Transit District’s EmX bus line located in the Eugene-Springfield area in western Oregon. Researchers placed sensors on the buses and at stops to detect when someone using the app was boarding. When a user came within range of a sensor,...

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Traffic congestion on urban roadways can influence operating costs and cause travel delays.

Portland State University master’s students Nicholas Stoll and Travis Glick will present a paper introducing solutions for locating the sources of congestion at the 2016 annual meeting of the Transportation Research Board.

With their faculty advisor, Miguel Figliozzi, Stoll and Glick looked into using bus GPS data to identify congestion hot spots.

By using high-resolution GPS data to visualize trends in bus behavior and movement, the researchers were able to examine the sources of delay on urban arterials.

These visualizations, which can be in the form of heat maps or speed plots like the one shown here on the right (an application of numerical method applied to a 2,000 ft segment of SE Powell), can be used by transportation agencies to identify locations where improvements are needed. For example, adding a queue jump lane at a congested intersection can improve flow.

The researchers used fine-grained bus data provided by TriMet to create the visualizations. Buses have been used as probes to estimate travel...

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Why model pedestrians?

A new predictive tool for estimating pedestrian demand has potential applications for improving walkability. By forecasting the number, location and characteristics of walking trips, this tool allows for policy-sensitive mode shifts away from automobile travel.

There is growing support to improve the quality of the walking environment and make investments to promote pedestrian travel. Despite this interest and need, current forecasting tools, particularly regional travel demand models, often fall short. To address this gap, Oregon Metro and NITC researcher Kelly Clifton worked together to develop this pedestrian demand estimation tool which can allow planners to allocate...

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Andy Kading, Graduate Student Researcher, Portland State University

Topic: Managing User Delay with a Focus on Pedestrian Operations

Across the U.S, walking trips are increasing. However, pedestrians still face significantly higher delays than motor vehicles at signalized intersections due to traditional signal timing practices of prioritizing vehicular movements. This study explores pedestrian delay reduction methods via development of a pedestrian priority algorithm that selects an operational plan favorable to pedestrian service, provided a user defined volume threshold has been met for the major street. This algorithm, along with several operational scenarios, were analyzed with VISSIM using Software-In-The-Loop (SITL) simulation to determine the impact these strategies have on user delays. One of the operational scenarios examined was that of actuating a portion of the coordinated phase, or actuated-coordinated operation. Following a discussion on platoon dispersion and the application of it in...

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