Wildlife everywhere faces a growing challenge: moving safely across an increasingly fragmented landscape. Roads, urban development, and other human-made barriers can make it harder for animals to access food, shelter, and breeding areas, while also increasing the risk of wildlife-vehicle collisions. To address this issue, researchers at Portland State University (PSU) worked on a groundbreaking effort to understand and protect the travel patterns of Oregon’s wildlife.

The Oregon Connectivity Assessment and Mapping Project (OCAMP) was a multi-year collaboration aimed at mapping wild species' habitats and travel corridors across the state. The research team created an interactive Priority Wildlife Connectivity Areas Map of Oregon, which can be used to protect wild animals' ability to move from place to place. The project was funded in part by the Oregon Department of Transportation (ODOT), to help the agency identify and mitigate the impacts of transportation infrastructure on Oregon's wildlife.

ABOUT THE PROJECT 

In June of 2019, the Oregon Legislative Assembly passed House Bill 2834, which mandated that the Oregon Department of Fish and Wildlife (ODFW) develop a Wildlife Corridor Action Plan to provide guidance for the designation and protection of wildlife corridors in Oregon. The bill also directed the Oregon Department of Transportation (ODOT) to establish a program to reduce wildlife-vehicle collisions in areas where wildlife corridors identified in the Wildlife Corridor Action Plan intersect with proposed or existing public roads. 

Rachel Wheat, the Wildlife Connectivity Coordinator for ODFW, was the project coordinator. The PSU research team was led by Martin Lafrenz of the Geography department, Catherine De Rivera of Environmental Sciences and Management, and Daniel Taylor-Rodriguez of the Department of Mathematics and Statistics. Lafrenz is a geomorphologist who studies human alteration of the water cycle and the landscape, De Rivera studies how anthropogenic changes in habitat connectivity affect animal populations and ecosystems, and Taylor-Rodriguex focuses on applying statistical methods to large scale problems, with an emphasis on ecological applications. The research was supported by PSU masters students Amanda Temple, Claire Brumbaugh-Smith, and Alana Simmons, and PhD students Leslie Bliss Ketchum and Jacob Schultz.

A large number of researchers, conservation groups, agencies and others were involved in helping to complete OCAMP – part of Oregon's larger conservation strategy – and the data used in the project came from many sources.

"A lot of biologists worked on this project, and many of them know each other. So we reached out to certain people, and then they reached out to more people. There were a lot of connections that happened, for over a year, and people supplied us with whatever data they had on animal presence or tracking," Lafrenz said. Lafrenz's team mapped out key habitats and travel routes used by different species, and Taylor-Rodriguez's team used the data to ensure that the maps aligned with real-world species movement and habitat use.

CONNECTING SCIENCE TO TRANSPORTATION SOLUTIONS

While the Priority Wildlife Connectivity Areas map has many applications — from land conservation to renewable energy siting — one of the most immediate uses is in transportation planning. ODOT can use this map to identify road segments that pose the greatest risk for wildlife-vehicle collisions and determine the best locations for wildlife crossings, such as underpasses or overpasses.

Wildlife-vehicle collisions are not only a danger to animals but can also endanger human safety, and can be costly. Strategic planning informed by OCAMP data means transportation agencies can reduce these risks while supporting Oregon’s biodiversity.

In the past, connectivity mapping in Oregon relied heavily on expert opinion, which left decision-makers without the robust data needed to guide policy and planning. The OCAMP project filled a major knowledge gap, with science-based connectivity models for 54 species representing different movement patterns and habitat needs. These models were combined to create the Priority Wildlife Connectivity Areas Map, which offers a statewide picture of the most critical areas for wildlife movement.

"We used tracking data where we had it, and presence and absence data to validate where the animals were. Then we put all the species together. In the final map, what you notice about it is that it's not for a particular species. It's just animal corridors, generalized. If we are really interested in a specific animal, we can always drill back in the data. But what the Legislature wanted was just a map of animal corridors in general that they could use to say, Okay, you're going to do this project. It's going to impact this corridor. So you need to put some kind of a crossing structure or fencing or some sort of mitigation," Lafrenz said.

WHO CAN USE THIS RESEARCH?

"Now that we have this information, the next big step is to shift into implementation. So how do we make sure that this tool is being used effectively? There are a lot of ways that we've done that, within the State government specifically. We're working with ODOT, to make them aware of where those Priority Wildlife Connectivity Areas intersect with our state highway system. We're also working with the Department of Land Conservation and Development, to guide siting and mitigation for new developments," Wheat said. 

ODFW has presented the Priority Wildlife Connectivity Areas map and tools to organizations including the Association of Oregon Counties and the League of Oregon Cities, as well as Federal land management agency partners.

"There are applications for the Bureau of Land Management, the U.S. Forest Service, the Fish and Wildlife Service, Land Trusts, soil and water conservation districts, watershed councils; basically anyone that's working in the conservation realm can benefit from this specific tool. There are also use cases for members of the public," Wheat said.

A paper by the research team, Visualizing Connectivity for Wildlife in a World Without Roads, was published in the journal Frontiers in Environmental Science.

"In that paper, we took southwestern Oregon and erased all the roads. And then we reran our model and compared, where would animals move if there were no roads as opposed to where we think they're moving now? We found that roads had a strong impact on connectivity. Roads influenced connectivity well beyond the footprint of the roadway," Lafrenz said.

Modeling wildlife movement in the absence of existing roads allowed for critical evaluation of where mitigation activities, such as wildlife crossing structures and fencing, could be most beneficial. This novel approach has practical applications for increasing connectivity for wildlife across roads. The high-resolution Priority Wildlife Connectivity Areas map also represents a new innovation in connectivity mapping; an area in which other states might follow Oregon's lead.

"Other states have engaged in statewide connectivity planning and mapping. One thing that sets OCAMP apart is that we used a much newer modeling approach, with much finer-scale spatial data. A lot of the tools that have been produced in other states in the past are at a very coarse spatial scale.Our maps are at a 30 meter resolution. So you can scale down to very small-scale efforts and get into very fine detail," Wheat said.

HOW INDIVIDUALS CAN HELP

ODFW developed a project specifically for roadkill in Oregon which makes use of data from iNaturalist, an online social network for recording observations of wildlife. 

"One of the things that we get asked a lot in our public communication is, how can the average person help provide information for connectivity? And one of the best ways that we found to do that is with iNaturalist," Wheat said.

The state has some information on where large-bodied wildlife, like deer and elk, are killed on roadways, because their maintenance crews remove them. But ODFW has very little information on smaller-bodied species. That's where iNaturalist comes in.

"Anyone with a cell phone can go out and snap a photo of a roadkill observation that they see. And then we can draw on that information to help identify roadkill hotspots and find the areas where we really need to focus on doing some sort of mitigation, whether that's crossing structures, habitat modification, or fencing to try to keep wildlife from getting killed on the road," Wheat said.

With a wide variety of applications for individuals, organizations, and governments, the Priority Wildlife Connectivity Areas map provides a critical tool for planning a connected future. For PSU researchers, OCAMP is an example of how science can inform policy and deliver real-world benefits.

Every time a pedestrian pushes a button to cross the street, it creates a useful data point about how many people are walking through that intersection. Now, transportation planners and engineers in Oregon have easy access to that data: The newest feature we've added to BikePed Portal is a dashboard that lets you explore the Oregon Department of Transportation (ODOT)'s pedestrian push-button data from all over the state.

ODOT has shared this data with Portland State University (PSU) for use in BikePed Portal, so that users can see where and how people interact with pedestrian crossings at signalized intersections. The dashboard (watch a walkthrough here) is still being developed and is anticipated to eventually include data from more agencies in addition to ODOT.

WHY IS THIS DATA HELPFUL?

Many, if not all, active transportation projects rely on pedestrian volumes to measure pedestrian exposure, or the number of potential opportunities for a pedestrian to be involved in a crash with a moving vehicle. By analyzing usage patterns, practitioners can prioritize upgrades at high-demand or problematic locations, ensure compliance with ADA standards, and inform pedestrian infrastructure planning. The potential applications of push-button data are numerous, and just a couple of examples are highlighted below.

The new dashboard is a useful resource to help agencies improve signal timing, accessibility, and safety for all road users, as well as guide future investments in walkability. 

DERIVING PEDESTRIAN COUNTS 

Researchers are exploring methods to convert the push-button data into complete pedestrian counts for the transportation network. At the 2025 annual meeting of the Transportation Research Board (TRB), Sirisha Kothuri, Joe Broach and Elizabeth Yates of PSU presented a study along with Mahyar Vahedi Saheli and Patrick Singleton of Utah State University on "Pedestrian Volumes from Push-Button Traffic Signal Data in Oregon: Estimating Models and Assessing Model Transferability [PDF - add link to poster)" in which they used the ODOT data, along with video recordings, to estimate pedestrian counts. They also developed a workflow to integrate pedestrian traffic counts into ODOT’s enterprise traffic data system. 

The initial research was funded by ODOT, as well as some funding support for the dashboard. Learn about the models used to calculate estimated pedestrian volumes in the final report, or watch a recorded seminar to learn more.

ODOT's research unit has also documented a use case for the data—see Pedestrian Traffic Estimation for Liquidation Costs—and is developing more.

ANALYZING PEDESTRIAN CRASHES

At Utah State, Singleton used similar data in Utah to do safety analysis. One paper examined the frequency and severity of crashes involving pedestrians, and another developed improved methods to predict crashes at signalized intersections. The research team wanted to examine whether the “safety in numbers” effect applies to pedestrian safety in the US. Both papers used push-button data as a novel data source to measure pedestrian exposure.

As pedestrian safety and accessibility continue to be top priorities in transportation planning, innovative uses of existing infrastructure—like push-button data—are opening up new possibilities for research, analysis, and informed decision-making. The addition of this new dashboard to BikePed Portal marks an important step in making pedestrian activity more visible for agencies across Oregon. By leveraging this data, planners and researchers can better understand walking patterns, identify safety risks, and ultimately create safer, more walkable communities. 

As the dashboard grows to include data from more jurisdictions, its value will only increase—offering a powerful tool to support equitable and evidence-based improvements in pedestrian infrastructure.

PARTNER WITH PSU: ADD YOUR DATA TO BIKEPED PORTAL

Cities and agencies interested in partnering across the region to improve transportation data access should reach out to our team at bikepedportal@pdx.edu. We are interested in adding more push-button data, as well as other types of nonmotorized data. 

We accept data in multiple formats (including EcoCounter API, TrafX, manual, turning movement), and convert it all into a standardized format. We offer quality control, quality assurance, and more. Learn about the services that BikePed Portal offers.

METADATA AND DOCUMENTATION

Additional resources have recently been added to BikePed Portal:

• Manual count metadata and data documentation can be found here.
• Pedestrian estimated push button documentation can be found here.
• A video tutorial for organizations interested in having BikePed Portal host their active transportation counts can be found here.

Photo by Mariakray/iStock

Portland State University's Transportation Research and Education Center (TREC) is a multidisciplinary hub for all things transportation. We are home to the Initiative for Bicycle and Pedestrian Innovation (IBPI), the data programs PORTAL and BikePed Portal, the Better Block PSU program, and PSU's membership in PacTrans, the Pacific Northwest Transportation Consortium. Our continuing goal is to produce impactful research and tools for transportation decision makers, expand the diversity and capacity of the workforce, and engage students and professionals through education, seminars, and participation in research. To get updates about what's happening at TREC, sign up for our monthly newsletter or follow us on social media. 

Over the past several years, in a series of research projects, researchers at Portland State University (PSU) have been developing a new approach to estimate active transportation volumes using machine learning.

This emerging method, which can predict how many people will be biking or walking on any given road, trail or segment of a transportation network at any time, offers promising applications for transportation agencies and state departments of transportation (DOTs). These organizations can use accurate bicycle and pedestrian volume information to track changes over time, prioritize projects, plan and design new infrastructure, conduct safety analyses and estimate public health impacts.

"These methods are still evolving, and it's still in the research phase. But I think the time is not far off when we will start using these methods as more mainstream," said Sirisha Kothuri of the Maseeh College of Engineering and Computer Science, the lead researcher on this series of projects.

The method Kothuri and other researchers are developing is referred to as "data fusion" because it involves combining multiple data sources, including traditional permanent and short-term counting methods as well as newer crowdsourced data streams from entities like Strava and Streetlight.

HOW DOES IT WORK?

Traditional permanent and short-term counting methods can directly provide counts, but are limited to certain locations or short periods of time. Meanwhile, crowdsourced data (such as Strava or StreetLight) can cover a wider area but with less accuracy, as they only capture a subset of users.

Fusing the two methods together–potentially with the use of deep learning algorithms–is a promising way to get the best of both.

The researchers train a computer model on existing count data from certain locations, then use that trained model to predict volumes at locations where there is count data that the model hasn't seen. They then compare the model's predictions with the actual count data to see how accurate it is.

Using long short-term memory networks and deep neural networks, the method involves the combining of static variables—such as network characteristics, demographics, and land use— with dynamic crowdsourced data and count data from different regions. The research has shown that crowd-sourced data alone cannot replace traditional count data. For the method to work, both are necessary.

Regional data is also key to the success of the model: the more local count data the model can be trained on, the better its accuracy will be for the area in which it will be used.

The models tend to fare better when using Monthly Average Daily Bicyclists (MADB) as a target, rather than Annual Average Daily Bicyclists (AADB), because breaking each counter down into monthly units gives them more data points to work with.

"Basically, the more data a model has, the smarter it gets," said Banafsheh Rekabdar, an Assistant Professor of Computer Science in the Maseeh College of Engineering and Computer Science who worked with Kothuri on the latest project.

The graphic below offers an overview of the path of data from original sources as it moves through the process developed by the researchers:

A SERIES OF RESEARCH EFFORTS FUNDED BY MULTIPLE ORGANIZATIONS 

These research efforts got underway in 2018 with funding from the National Institute for Transportation and Communities (NITC). NITC launched a pooled fund project with support from the DOTs of Oregon, Virginia, Colorado, Utah, and the District of Columbia, as well as Central Lane MPO and the Cities of Portland and Bend, Oregon. With matching funds from NITC, those agencies came together to fund the initial project Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes, with a research team led by Kothuri made up of researchers from PSU and the University of Texas at Arlington. The objective of this study was to fuse traditional count data with crowdsourced data, land use and sociodemographic data to estimate bicycle volumes on a network. It was the first large scale of its kind to include data from multiple regions and years to generate bicycle volumes using data fusion techniques.

Next came "Estimating Bicyclist Volumes with Crowdsourced Data," a study funded by the Washington Department of Transportation (WSDOT), which built on the initial efforts and focused on the transferability of bicycle volume models that were estimated as part of the NITC pooled study.. As part of a case study for this project, the researchers showed how bicycle volumes can be estimated for certain high-risk crash corridors rather than the entire network using data fusion techniques, which can be a critical input for safety analyses.

Kothuri and her team then focused on another NITC study which focused on adapting the bicycle volume estimation techniques to the pedestrian context.This study used data fusion techniques to combine crowdsourced data (Strava pedestrian data) along with static contextual data to model 2-hour PM peak pedestrian volumes.

On the bike side, the WSDOT study was followed by a NITC technology transfer initiative aimed at improving the accuracy of the bicycle volume estimates using machine learning techniques.

The latest report to come out of these efforts, Improving the Accuracy and Precision of Bicycle Volume Estimates Using Advanced Machine Learning Approaches (PDF) by Sirisha Kothuri, Banafsheh Rekabdar and Joe Broach of Portland State University, pushed the needle forward on using advanced techniques to extrapolate data over a large transportation network. Two PSU graduate students also worked on the project: Saba Izadkhah, who is working toward a PhD in computer science, and Andrew Wagner, a computer science masters student.

A paper based on this work was presented at the Institute of Electrical and Electronics Engineers' International Conference on Artificial Intelligence x Science, Engineering and Technology at the beginning of October. Kothuri also presented updates on the data fusion method at the 2024 Pacific Northwest Transportation Consortium (PacTrans) Conference.

"We know that for pedestrians, injuries and fatalities are at an all time high. Bicyclist safety is also of top concern. So these estimates are really critical for agencies right now," Kothuri said.

Portland State University's Transportation Research and Education Center (TREC) is a multidisciplinary hub for all things transportation. We are home to the Initiative for Bicycle and Pedestrian Innovation (IBPI), the data programs PORTAL and BikePed Portal, the Better Block PSU program, and PSU's membership in PacTrans, the Pacific Northwest Transportation Consortium. Our continuing goal is to produce impactful research and tools for transportation decision makers, expand the diversity and capacity of the workforce, and engage students and professionals through education, seminars, and participation in research. To get updates about what's happening at TREC, sign up for our monthly newsletter or follow us at the links below.

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In order to make sure bicyclists' needs are considered when improving a transportation system, planners and engineers need to know how many people are biking, and where. 

Traditional bicycle counters can provide data for limited sections of the bike network; often these counters are installed at important locations like trails or bridges. While limited in location, they count everyone who bikes by. Meanwhile, GPS & mobile data cover the entire transportation network, but that data only represents those travelers who are using smartphones or GPS. Combining the traditional location-based data sources with this new, crowdsourced data could offer better accuracy than any could provide alone. 

"Knowing how many people are bicycling on a street is really important for a number of reasons. As just a few examples, bicycle volumes give you a way to understand safety data and determine crash rates. They provide insight into where and how bicycle trips are taking place, which can help plan for new or improved facilities," said Nathan McNeil of Portland State University.

Supported by a pooled fund grant administered by the National Institute for Transportation and Communities (NITC), Dr. Sirisha Kothuri of Portland State University led a research project aimed at fusing traditional and emerging data sources together, to derive bicycle volumes for an entire transportation network. The team developed three models and tested them in six cities: Dallas, Texas; Portland, Bend and Eugene, Oregon; Boulder, Colorado; and Charlotte, North Carolina. Learn more about the project in this research highlight video.

Related research: This is one of many NITC studies advancing the collection, methodology, and analysis of multimodal data that supports professionals and researchers in understanding and predicting human travel behavior in order to optimize those systems for both the providers and users. Learn about more NITC research in the area of multimodal data and modeling.

DEVELOPING THREE BIKE COUNT MODELS

With Kothuri as principal investigator, the research team included Joe Broach and Nathan McNeil of PSU; Kate Hyun, Stephen Mattingly and Md. Mintu Miah of the University of Texas at Arlington; Krista Nordback of the University of North Carolina's Highway Safety Research Center, and Frank Proulx of Frank Proulx Consulting LLC. 

First, the team conducted a literature review while cataloging and evaluating the available third-party data sources and existing applications. They chose the six study sites to represent a variety of urban and suburban contexts, with plenty of geographical diversity, and existing bike data available. Of the six, Boulder, Charlotte and Dallas constituted basic sites, where one year of data (2019) was used for modeling. Portland, Bend, and Eugene in Oregon were considered enhanced sites, where three years of data (2017–2019) were used for model estimation. 

The team chose three relatively new data sources: Strava, Streetlight Data, and GPS data from bike share systems in the case study cities. After collecting demographic, network, bike count and emerging data from the new sources for each of the cities, they developed three sets of models: 

1. One with pooled data from all six cities,
2. another with just the pooled data from the three Oregon cities,
3. and finally a set of city-specific models. 

The researchers then applied the results to each of the six study sites. The city-specific models generally performed the best, showing the most accuracy in predicting bicycle volumes. The scripts used to run the models will soon be published to GitHub, and a link will be posted on the project page for anyone interested in accessing the models.

In general, the various data sources appeared to be complementary to one another; that is, adding any two data sources together tended to outperform each data source on its own. Adding even more data should continue to refine accuracy. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, big data sources like Strava and StreetLight actually make the old “small” data even more important.

"We will need more ground-truth counts for low-volume sites to capture the variety of locations, and that will make more robust models," said Kate Hyun of UTA.

BETTER MODELS PROVIDE MORE ACCURATE PERFORMANCE MEASURES FOR TRANSPORTATION AGENCIES

Josh Roll, Research Analyst & Data Scientist at the Oregon Department of Transportation, served as the chair for the project’s technical advisory committee. He believes the outcome of this research could help transportation agencies get a better handle on how many people are biking in their communities. 

“At ODOT we just adopted "Bicycle Miles Traveled" as a new key performance measure, and we need a way to measure it, so this project very much helps to fill the gap on how we're going to do that. This research used cutting-edge data fusion techniques that could lay the groundwork for how transportation agencies like ODOT monitor bicycle activity across the system,” Roll said.

For transportation agencies wishing to support active travel to meet various sustainability, public health, and climate-related goals, quickly having accurate data for the entire network would be a giant leap in the right direction.

Robust, organized, and accessible count programs will be essential to get the most out of emerging data sources. The more good, vetted data are available, the better models based on emerging sources will perform, so professionals managing bicycle count programs should focus on making data uniform and widely usable.

"In order to integrate all of these disparate data sources – automated and manual counts, opt-in apps like Strava, passively collected background data like Streetlight, and GPS-enabled bike sharing systems — into one coherent system, data professionals should organize their data to best take advantage of these new data fusion possibilities. This means making sure nonmotorized data are accurate, consistent, and useful," said Sirisha Kothuri, lead researcher on the project. 

ABOUT THE PROJECT

Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes

Sirisha Kothuri, Joe Broach and Nathan McNeil, Portland State University; Kate Hyun, Stephen Mattingly, and Md. Mintu Miah of University of Texas at Arlington; Krista Nordback of the University of North Carolina's Highway Safety Research Center, and Frank Proulx of Frank Proulx Consulting LLC. 

• Download the Final Report (PDF)
• Download the Project Brief (PDF)
• Watch a recorded March 10, 2022 Webinar
• Watch the research highlight video

This research was funded by a pooled fund grant through the National Institute for Transportation and Communities, with additional support from the Oregon Department of Transportation, Virginia DOT, Colorado DOT, Central Lane MPO, Portland Bureau of Transportation, District DOT, and Utah DOT.

Photo by Lacey Friedly

RELATED RESEARCH

To learn more about this and other NITC research, sign up for our monthly research newsletter.

• Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections
• Data-Driven Mobility Strategies for Multimodal Transportation
• Biking and Walking Quality Counts: Using “BikePed Portal” Counts to Develop Data Quality Checks

The National Institute for Transportation and Communities (NITC) is one of seven U.S. Department of Transportation national university transportation centers. NITC is a program of the Transportation Research and Education Center (TREC) at Portland State University. This PSU-led research partnership also includes the Oregon Institute of Technology, University of Arizona, University of Oregon, University of Texas at Arlington and University of Utah. We pursue our theme — improving mobility of people and goods to build strong communities — through research, education and technology transfer.

A new study launches next month, funded by the National Institute for Transportation and Communities (NITC). Researchers at Portland State University and the University of Texas at Arlington will explore the use of crowdsourced data to estimate pedestrian counts. The project team consists of Sirisha Kothuri and Nathan McNeil of Portland State University, and Kate Hyun and Stephen Mattingly of the University of Texas at Arlington. 

WHAT ARE PEDESTRIAN COUNTS USED FOR?

"You know that saying that if you can't measure it, you can't change it? For most streets, we might have some intuitive sense of if there are a lot of people walking there or not, but we rarely have data to back it. This project will assess how crowdsourced data can help to establish the level of pedestrian activity on streets throughout the transportation network," McNeil said.

Knowing how many pedestrians or bicyclists are using a link or a network is the foundation for measuring nonmotorized travel. Count data are useful for monitoring trends, planning new infrastructure, and for conducting safety, health, and economic analyses. The lack of widely available pedestrian count data precludes meaningful safety studies, which have become critically important, especially with the nationwide increase in pedestrian crashes over the last decade.

Several automated technologies have been developed to count bicyclists and pedestrians. Many advances in counting technology are more efficient than the old person-with-clipboard method, but cost and other considerations still limit direct observation to small subsets of entire networks. However, the emergence of crowdsourced data such as Strava and StreetLight has allowed for the collection of large-scale datasets over broad areas of the network. 

WHAT WILL BE STUDIED

While several research studies have evaluated and applied bicycle data from these datasets, no study has yet looked at pedestrian count estimates from these data sources or assessed how these compare to traditional pedestrian counts and other measures of pedestrian activity, such as pedestrian actuations from traffic signals. The researchers will evaluate pedestrian data estimates from the crowdsourced data sets and explore how these can be used along with traditional count data and sociodemographic data to derive count estimates.

"The lack of pedestrian counts limits our ability to conduct safety analyses, estimate changes in demand, determine where infrastructure improvements need to be made, and quantify the benefits of walking. We hope that the findings from the study can help agencies estimate pedestrian counts on their network and use them for a variety of applications," Kothuri said.

This project is one seven new research efforts funded by the latest General Research grant from NITC. Read about the other six projects here.

ABOUT THE PROJECT

Exploring the Use of Crowdsourced Data Sources for Pedestrian Count Estimations

Sirisha Kothuri and Nathan McNeil, Portland State University; Kate Hyun and Stephen Mattingly, University of Texas at Arlington

Photo by Dmytro Varavin/iStock

The Transportation Research and Education Center (TREC) at Portland State University is home to the National Institute for Transportation and Communities (NITC), the Initiative for Bicycle and Pedestrian Innovation (IBPI), and other transportation programs. TREC produces research and tools for transportation decision makers, develops K-12 curriculum to expand the diversity and capacity of the workforce, and engages students and professionals through education.

"Transformative Transportation Survey Methods: Enhancing Household Transportation Survey Methods for Hard-To-Reach Populations," is a new article published in the September 2021 issue of Transportation Research Part D. It was co-authored by Amy Lubitow, a sociology faculty member at Portland State University, Erika Carpenter, a sociology graduate student, and Julius McGee, a faculty member in urban studies and planning.

The study explores the challenges that hard-to-reach populations face in completing household activity surveys. Researchers drew on qualitative data from hard-to-reach populations regarding the limits of the Oregon Household Activity Survey and found evidence that the survey methods lack social, cultural, and linguistic applicability for Black, Indigenous and other people of color, as well as low-income populations. The authors argue that Oregon’s household travel survey prioritizes certain ways of understanding and experiencing mobility that are, by default, exclusionary. The article concludes in sharing insights regarding how transportation professionals might improve data collection efforts. Broader efforts for transportation equity (and ultimately towards transportation justice) cannot be achieved when the data used to inform transportation planning fails to accurately reflect all populations.

The paper draws on findings from a NITC research project led by Lubitow: Advancing Transportation Equity through Inclusive Travel Survey Data Methods. 

Photo by santypan/iStock

The National Institute for Transportation and Communities (NITC) is one of seven U.S. Department of Transportation national university transportation centers. NITC is a program of the Transportation Research and Education Center (TREC) at Portland State University. This PSU-led research partnership also includes the Oregon Institute of Technology, University of Arizona, University of Oregon, University of Texas at Arlington and University of Utah. We pursue our theme — improving mobility of people and goods to build strong communities — through research, education and technology transfer.

How can we use a variety of data-driven speed management strategies to make transportation safer and more efficient for all modes–whether you’re driving, walking or taking transit?

The project was led by Yao Jan Wu, director of the Smart Transportation Lab at the University of Arizona. Co-investigators were Xianfeng Terry Yang of the University of Utah, who researches traffic operations and modeling along with connected automated vehicles, and Sirisha Kothuri of Portland State University, whose research has focused on improving signal timing to better serve pedestrians. Join them on Sept 15, 2021 for a free webinar to learn more.

"We want to improve mobility for all users, be it pedestrians, vehicle drivers or transit riders, and there are different strategies to do this. How do we harness data to drive us to these strategies?" Kothuri said.

Funded by the National Institute for Transportation and Communities (NITC), this multi-university collaboration addressed the question from three angles:

• Wu and his students in Arizona looked at the impact of speed management strategies on conventional roadways.
• Yang and his team examined the effects of speed management strategies on connected corridors, coordinating with transit signal priority (TSP) systems.
• Kothuri and her PSU team came up with an approach to estimate pedestrian delay at signalized intersections.

The aim of their combined research efforts was to investigate the possibility of developing and implementing more innovative speed management strategies that are effective for multimodal transportation and can be applied in both conventional roadways and "connected" roadways - i.e. equipped with vehicle-to-infrastructure or infrastructure-to-infrastructure communication capabilities.

IMPACT OF SPEED FEEDBACK SIGNS ON TRAFFIC FLOW AND SAFETY

Working with Pima County, Arizona, Wu and the Arizona research team evaluated the mobility and safety impacts of speed feedback signs on conventional roadways. Ina Road, a major signalized arterial in Tucson, was selected as the study corridor. This corridor was chosen because of the existing speed feedback signs along the corridor between signalized intersections, and due to the presence of advanced traffic data collection systems. Traffic data were collected for four weeks (May 28-June 25, 2018), and the existing signs were disabled for two weeks (June 11th-June 25th) during the data collection.

Using MioVision’s TrafficLink platform and high-resolution data, the researchers measured:

• Percentage arrival on red: The percentage of vehicles that arrived at the intersection when the signal was red.
• Split failure: The occurrence of leftover demand (when at least one vehicle in the queue was not able to go, but had to wait for the next green cycle) for a specific approach at an intersection.
• Intersection delay: Total amount of time that all vehicles spend in the intersection queue while waiting to pass the intersection.

For a given time of day before and after disabling the speed feedback signs, only a little variation in traffic flow was observed. Similar traffic flow peaks for all the segments suggest that arterial mobility and traffic flow were not affected by disabling the signs. But what about the signs' effect on safety?

Data from the Pima Association of Governments show that the total number of severe crashes (four) on the study corridor all occurred before implementing the speed feedback signs in 2015. Moreover, using speed as a performance indicator, the researchers found a reduction in drivers' speeds along each link of the corridor, in between intersections (see page 21 of the final report for a table of speed results on weekdays and weekends). The reduction in the link speed was significant during the times the feedback signs were enabled, suggesting a reduced likelihood of severe crashes.

RETIMING SIGNALS FOR TRANSIT SIGNAL PRIORITY

Yang and the Utah team explored the impact of a speed management strategy on a connected corridor in Salt Lake City, Utah: how does signal retiming impact a transit signal priority (TSP) system?

Although TSP is a promising way to reduce bus delays at intersections, improve transit operational reliability, and consequently increase transit ridership with improved service, the effectiveness of TSP is subject to things like bus schedule, signal timing plan, passenger flows, etc. Considering this, the Utah Department of Transportation (UDOT) adopted a speed management strategy – signal coordination and retiming –  to improve the effectiveness of TSP. UDOT implemented signal coordination along a stretch of Redwood Road, a connected corridor with dedicated short-range communication, and applied several signal timing plans with the aim of maximizing the benefits of TSP. 

In general, researchers found, the average rate of TSP served before signal retiming was 33.13%, which is lower than that of 35.29% after signal retiming. This means that more buses requesting signal priority had their requests met, after the signals were retimed. In other words, the speed management strategies were also helping to improve bus reliability. 

In addition, bus travel time and bus running time were reduced after signal retiming. All of these findings indicate that the speed management strategy implemented along this connected vehicle corridor results in an improvement of TSP and bus service.

ESTIMATING PEDESTRIAN DELAY

There is technology, like signal controllers that can record high-resolution data, capable of recording pedestrian delay; but not every intersection is equipped with this technology because it is costly. Agencies are upgrading their infrastructure when possible, but there are still a lot of intersections where there is no means of knowing how long of a delay a pedestrian may experience. 

The PSU team, led by Kothuri, developed an alternative method for estimating pedestrian delay by using controller data for estimating multimodal signal performance measures.

Traditionally, signal timing is calibrated to prioritize vehicle movement, and long delays for pedestrians can increase noncompliance, causing unnecessary risk. So the team's goal was to find a way to use data to estimate what the pedestrian delay would be, at intersections that are not equipped with the latest infrastructure. Researchers took data from Ina Road, the same study corridor in Pima County, Arizona used to evaluate speed management strategies, and used finite mixture modeling to model pedestrian delay. Results showed that their method was able to successfully model the delay fluctuations with less than 10% mean absolute error. This method can be applied to intersections with similar characteristics as the test intersections. So cities and agencies that do not have signal controllers to capture pedestrian delay can use this finite mixture modeling method to figure out where they need to apply strategies to reduce that delay. 

The application of the proposed method could be beneficial to transportation agencies in three capacities:

1. providing a more reliable, robust, and accurate approach for estimating pedestrian delay at signalized intersections where sensors are not available to collect pedestrian delay;
2. a tool for analyzing the risk of pedestrians violating the signal;
3. calibrating a network-wide model for estimating pedestrian delay at all intersections without the need to use additional resources

OUTCOMES OF THE RESEARCH

An effective strategy for improving mobility needed to consider both motorized and non-motorized modes of transportation. The three main achievements of this project were:

1. Evaluate the impact of speed management strategies along conventional arterials using smart sensor data;
2. Understand the role of conventional speed management strategies in supporting connected arterials;
3. Examine the possibility of using controller event-based data to estimate multimodal signal performance measures.

Improved multimodal speed management strategies foster a safer community that will, in turn, encourage more people to walk and bike. This project addressed data-driven multimodal speed management strategies for traditional corridors using traffic sensors, and for future evaluation of connected vehicle-based strategies. The project also strengthened relationships between the three universities and their local partners, including Pima County and the Utah DOT.

"This project highlighted the strong collaboration we have among the universities. Trying to find innovative solutions during the process tied our universities together, bringing local resources together as well," Wu said.

ABOUT THE PROJECT

Data-Driven Mobility Strategies for Multimodal Transportation

Yao-Jan Wu, University of Arizona; Xianfeng Yang, University of Utah; Sirisha Kothuri, Portland State University

• Download the Final Report (PDF)
• Download the Project Brief (PDF)
• Register for the September 15, 2021 webinar

This research was funded by the National Institute for Transportation and Communities, with additional support from Pima County Public Works Administration, Portland State University, University of Arizona, University of Utah, and Utah Department of Transportation.

RELATED RESEARCH

To learn more about this and other NITC research, sign up for our monthly research newsletter.

• Connected Vehicle System Design for Signalized Arterials
• Improving Walkability Through Control Strategies at Signalized Intersections
• Development Of Low-Cost Radar-Based Sensor For Multi-Modal Traffic Monitoring

Photo by csfotoimages/iStock

The National Institute for Transportation and Communities (NITC) is one of seven U.S. Department of Transportation national university transportation centers. NITC is a program of the Transportation Research and Education Center (TREC) at Portland State University. This PSU-led research partnership also includes the Oregon Institute of Technology, University of Arizona, University of Oregon, University of Texas at Arlington and University of Utah. We pursue our theme — improving mobility of people and goods to build strong communities — through research, education and technology transfer.

A national non-motorized count data archive, BikePed Portal provides a centralized standard count database for public agencies, researchers, educators, and other curious members of the public to view and download bicycle and pedestrian count data. It includes automated and manual counts from across the country, and supports screenline and turning movement counts.

BikePed Portal was established in 2015 by Transportation Research and Education Center (TREC) researchers at Portland State University through a pooled fund grant administered by the National Institute for Transportation and Communities (NITC). Other project partners include the Federal Highway Administration, Oregon Department of Transportation, Metro, Lane Council of Governments, Central Lane MPO, Bend MPO, Mid Willamette Valley Council of Governments, Rogue Valley Council of Governments, City of Boulder, City of Austin, Cycle Oregon, and Oregon Community Foundation.

If you’re interested in using BikePed Portal for archiving bicycle and pedestrian counts for your community, please contact us at bikepedportal@pdx.edu.

Hau Hagedorn, associate director of TREC, has been a driving force behind BikePed Portal since its conception. In early 2018 data scientist Tammy Lee joined TREC to manage our transportation data program. A primary focus of her role at TREC is the continued development and implementation of BikePed Portal, and she’s written quite a few case study blogs using BPP. Celebrating the launch of the new dashboard, we interviewed Hau and Tammy to learn more.

Before we jump into discussing BikePed Portal, could you share why access to better quality bike and ped data is so important for the transportation industry?

HAU HAGEDORN (ASSOCIATE DIRECTOR, TREC at PORTLAND STATE)

In general, data is powerful in decision-making. Agencies need to know how many people are biking and walking. How many trips happen per year? Per facility? What does that look like over time? Site by site, trail by trail, system by system. Accurate, centralized data (or, lack of it) can make or break the case for building or enhancing bike and walk facilities. Right now, the lack of significant data is a barrier. One thing that we hear a lot in pedestrian studies is ‘people don't actually walk there, we never see them.’ When you actually do the work and go out and count, intersections or segments or facilities, it becomes apparent that people are using it beyond expectations. In active transportation, we are always trying to play catch-up to the levels of vehicle data that is collected and analyzed. Beyond application for case studies, data like this is critical to advancing active transportation research scope and impact. The NCHRP released some great guidance, the Guidebook on Pedestrian and Bicycle Volume Data Collection that also details some use cases across the U.S.

USE CASES FOR BICYCLE AND PEDESTRIAN COUNT DATA IN A NATIONAL ARCHIVE 

Understand how existing infrastructure is used: How many trips per year happen on a facility?  Show changes over time, site by site, trail by trail, system by system.

Demonstrate impacts with before/after studies: Communicate how funds have been used, and evaluate the effects of new infrastructure on pedestrian and/or bicycle activity.

Tell the story: Share count data in infographics, articles, grant reporting, and annual reports while answering how people are using the system and how it’s grown.

Guide prioritization: There is potential to use data for system planning, as well as prioritize where needs are, particularly for bike-ped projects.

Make the case: Use data to support grant applications and validate identified needs.

Change design: Using volume, a shared use path LOS calculator could be used to determine how wide a facility should be designed or improved.

Improve data quality: Providing count device maintainer information about the quality of the data – both filtering “bad” data and letting the maintainer know when a device may be broken.

Increase access and ease of data: Support data requests automatically so staff don’t have to individually respond to data requests.

Your team developed BikePed Portal – a central data repository for national bike and ped data. Why is this significant?

TAMMY LEE, Ph.D. (TRANSPORTATION DATA PROGRAM ADMINISTRATOR, TREC at PORTLAND STATE)

Most often agencies have a lot of count files of all different types, and they're all siloed on different computers, different machines. There isn’t consistency in how those data files are maintained, stored, or created. It can be overwhelming. So using a central data repository is an opportunity for agencies to standardize their data and store it in one shared location. That way, it's just not sitting on someone's computer but instead accessible by multiple people within an organization.

What is unique about BikePed Portal compared to what else is out there? Is there a feature you are most excited about?

HAU

U.S. cities and jurisdictions collect data for their specific entity, which means you have to go to various sites to compare the volumes of walking and biking across a state or the country. In the BikePed Portal dashboard that comparison is easier, especially with the performance measures we have set up. For example, within the Portland region, we have Metro collecting count data, we have the City of Portland collecting counts, the Tualatin Hills Park & Recreation District, City of Beaverton, as well as the state of Oregon. Once we get all of that data into BikePed Portal, and these agencies authorize the view of that data, you have all of those disparate data sources in one place. It provides a more comprehensive view of what the actual volumes are across an entire network and system. I’m most excited about the potential for stronger coordination between these entities as they collaborate on infrastructure projects and other types of active transportation programs and planning.

Who are the intended users of BikePed Portal?

TAMMY 

U.S. transportation agencies, planners, and advocates who need bike/ped counts, as well as those in academia like students, researchers, and educators. Right now you can download the hourly data we have by site. There are a few sites that have years and years worth of data, which is pretty impressive for a long-term dataset. If you're a researcher, and you're trying to figure out which city or which jurisdiction has the most data, you can just poke around BikePed Portal. It saves that researcher the time and frustration of having to reach out to different cities asking, does this data exist? Can I access this data? And, once they receive the data, it's all standardized and easy to use. Versus if they had reached out to those agencies, they’re going to get some text files, csv files, handwritten pdfs, machine unreadable Excel files.

Last year we released research on bike and ped count data QA/QC from Portland State researcher Nathan McNeil. How important was this QA/QC process to the design and function of BikePed Portal?

TAMMY

We make it a point to ingest different types of data sources and put them together all in one location. You can have counts from EcoCounter, from different makers like Traffix, and manual counts, and it all gets inputted into BikePed Portal. And so because of that, and because we have a nice database in which to do it, we're able to use this really large and diverse sample size to do an analysis developing QA/QC metrics. Count data can have some really large peaks. For example in Portland, there's the World Naked Bike Ride Day - it’s huge. That day would automatically get flagged for the data owner because the volume is so high. But the data owner is given the choice to validate or invalidate it. They know their data better than any outsider. That validation process helps researchers and others who download the data; they can check, was this data QA/QC'd by the owner? If it wasn't, then they can have that chance to sort of validate it themselves and figure out if they want to use it or not.

What's next? Is there another evolution or feature in the works?

HAU

Our next goal is really broadening the user base and expanding the datasets available in BikePed Portal. Also, we’re really focused on integrating AADNMT calculations (Annual Average Daily Non-Motorized Traffic). For cities that meet a certain volume of counts, we will be providing that metric. Just like with traffic data, this gives you a sense of the volume of usage for your analysis area. If anyone is interested in learning more about BikePed Portal and how they can use it for archiving bike and pedestrian counts for your community, they can reach out to us at bikepedportal@pdx.edu.

RELATED RESEARCH

To learn more about this and other TREC research, sign up for our monthly research newsletter.

• Online Non-motorized Traffic Count Archive
• Biking and Walking Quality Counts: Using “BikePed Portal” Counts to Develop Data Quality Checks
• BikePed Portal Phase II

The Transportation Research and Education Center (TREC) at Portland State University is home to the National Institute for Transportation and Communities (NITC), the Initiative for Bicycle and Pedestrian Innovation (IBPI), and other transportation programs. TREC produces research and tools for transportation decision makers, develops K-12 curriculum to expand the diversity and capacity of the workforce, and engages students and professionals through education.

Principal Investigator:  Sirisha Kothuri, Portland State University

BIKE/PED COUNT SURVEY: CALL FOR INPUT

Researchers at Portland State University, University of Texas at Arlington, University of North Carolina at Chapel Hill and Toole Design Group are conducting a scan to identify locations where bicycle counts are taking place around North America, and hope to enlist your help! If you collect bike count data (or oversee counts) in your jurisdiction, please consider taking our quick survey to tell us a little bit about your count locations and data.

The survey can be accessed here: tinyurl.com/BikeCounterScan

THE NEW PROJECT

Active transportation modes such as bicycling are associated with benefits like lower congestion and emission levels, and improvements in public health. Many cities are interested in increasing bicycle activity, but in order to understand what works, cities require accurate accounting of bicycle traffic. This requires re-thinking the way we conduct estimation methods, data inputs, and modeling techniques.

To that end, a group of local agency partners pooled resources to fund the research project: Exploring Data Fusion Techniques to Derive Bicycle Volumes on a Network. With NITC matching funds, the project is supported by a pooled fund grant of $200,000. These partners include the DOT's of Oregon, Virginia, Colorado, Washington D.C. and Utah; Central Lane MPO, and the Cities of Portland and Bend, Oregon.

NITC POOLED FUND GRANT

Earlier this year the National Institute for Transportation and Communities (NITC) released an RFP for a collaborative project that addressed a group of partners' common needs. We ended up selecting two Pooled Fund projects for 2018, and the funds pooled by the agencies were then matched 1:1 by NITC. The research team for the second project, which will explore equity implications of automated transit fare collection methods, will be announced later next month.

THE RESEARCH TEAM

Once the project was chosen for funding, researchers from all six NITC campuses were invited to submit proposals to tackle it. The selected team:

Portland State University

• Sirisha Kothuri, Ph.D., Senior Research Associate
• Joseph Broach, Ph.D., Research Associate
• Nathan McNeil, Ph.D., Research Associate

University of Texas at Arlington

• Kate Hyun, Ph.D., Assistant Professor
• Stephen Mattingly, Ph.D., Associate Professor

THE ISSUE

To date, jurisdictions have often relied on observed counts of cyclists—either short-duration manual or longer-term automated counts—in a limited set of locations. Based on these limited datasets, models are then developed to extrapolate network-wide conditions. Recently, however, new sources of bicycling activity data have emerged from GPS-based smartphone apps—Strava, Ride Report, Map My Ride, and others—and GPS-enabled public bike sharing systems. These emerging data sources have potential advantages as a complement to traditional count data, since they are collected continuously and for larger portions of the bicycle network. However, the representativeness of these new data sources is still uncertain, and their suitability for producing bicycle volume estimates has yet to be rigorously explored.

RESEARCH OBJECTIVES

The research team aims to evaluate, demonstrate, and document the potential of emerging data sources to inform network-wide bicycle volume estimates. Research questions include:

• What third-party bicycle volume and route data are available to augment conventional count data, and how have they been used to date?
• How can we evaluate these emerging data sources for factors such as accuracy, coverage, completeness, and representativeness?
• How can we combine emerging third-party and conventional data sources to estimate bicycle volumes across entire networks?
• What relative marginal value do different data sources and model forms add in predicting bicycle volumes relative to acquisition and processing costs?

ANTICIPATED OUTCOMES OF THE RESEARCH

• A review of existing bicycle volume estimation methods and a catalog of potential third-party data sources and applications to date;
• Evaluation of emerging third-party bicycle data sources as inputs into network-wide volume estimation;
• Demonstration of the development, application, and validation of models that combine multiple data sources to improve predict bicycle volumes network-wide;
• Comparison of the relative value of different data sources and modeling techniques in bicycle volume estimation; and,
• Documentation and packaging of methods developed for sharing and re-use by others, including making available scripts used in the analysis on GitHub or other open source repository.

WHAT’S NEXT?

This project stands to bring significant improvements to the completeness and reliability of bicycle counts, which will in turn help planners and engineers design routes and networks to best meet the needs of transportation system users. We will post updates over the next year or two, as the project unfolds.

This research was funded by the National Institute for Transportation and Communities, with additional support from Oregon DOT, Virginia DOT, Colorado DOT, Washington D.C. DOT, Utah DOT, Central Lane MPO, the City of Portland, Oregon and the City of Bend, Oregon; and from Portland State University and the University of Texas at Arlington.

To learn more about this and other transportation research, sign up for our monthly newsletter.

The National Institute for Transportation and Communities (NITC), one of five U.S. Department of Transportation national university transportation centers, is a program of the Transportation Research and Education Center (TREC) at Portland State University. The NITC program is a Portland State-led partnership with the University of Oregon, Oregon Institute of Technology, University of Utah and new partners University of Arizona and University of Texas at Arlington. We pursue our theme — improving mobility of people and goods to build strong communities — through research, education and technology transfer.