Abstract
Transportation is a basic social and economic need but those mobility options conceived a generation ago may not be economically or environmentally sustainable with rising urban populations. According to the U.S. Department of Transportation (USDOT), the average U.S. household produces about 9.5 trips a day. About half of these trips are within three miles, but fewer than 2 percent of those trips are made by bicycle. Private vehicles like cars, pick-up trucks, and SUVs, account for almost 50 percent short distance trips (i.e., trips within 3 miles), in most U.S. metro areas. As a result, commuters are spending increased amounts of time in congestion, which has associated costs such as wasted fuel and emissions. Micromobility (defined as shared bikes, e-bikes and e-scooters) represents a significant opportunity to replace short distance trips made by personally owned vehicles (POVs) and provide first-and last-mile solutions for underserved public transit riders. The purpose of this research is to estimate the number of short distance POV trips that could be replaced by micromobility options and the resulting environmental benefits, and to develop policy recommendations that could assist policymakers in better understanding where the greatest opportunities for expanding active transportation exist.
Description
Transportation is a basic social and economic need but those mobility options conceived a generation ago may not be economically or environmentally sustainable with rising urban populations. According to the U.S. Department of Transportation (USDOT), the average U.S. household produces about 9.5 trips a day (United States Department of Transportation, 2019). About half of these trips are within three miles, but fewer than 2 percent of those trips are made by bicycle. Private vehicles like cars, pick-up trucks, and SUVs, account for almost 50 percent of short distance trips (i.e., trips within 3 miles), in most U.S. metro areas (INRIX, 2019). As a result, commuters are spending increased amounts of time in congestion, which has associated costs such as wasted fuel and emissions. Micromobility (defined as shared bikes, e-bikes and e-scooters) represents a significant opportunity to replace short distance trips made by personally owned vehicles (POVs) and provide first-and last-mile solutions for underserved public transit riders. As of 2017, more than 55 bicycle sharing systems are in operation in major cities across the United States, with approximately 100,000 shared bicycles (National Association of City Transportation Officials, 2017). While, shared micromobility options are available to users, cities still have not figured out an effective way to get commuters to incorporate active transportation into their daily commuting routines or how to effectively integrate these emerging transportation options into the existing transportation infrastructure. The purpose of this research is to estimate the number of short distance POV trips that could be replaced by micromobility options and the resulting environmental benefits, and to develop policy recommendations that could assist policymakers in better understanding where the greatest opportunities for expanding active transportation exist.
Our hypothesis is that micromobility could replace a large number of short distance trips by POVs and improve congestion but there are seasonality and trip purpose limitations in ridership that have not been explored in much detail. The research agenda will first estimate the upper bound potential for micromobilty to replace short distance trips made by POVs, considering weather, time of day, physical, and trip purpose limitations, and assess the changes in greenhouse gas (GHG) emissions and energy. An outcome of our first objective will be a geospatial model that will detail the number of short distance trips made by POVs and micromobility modes, by origin and destination, and how this could change if individuals began to use more active forms of transportation. The second part of the agenda focuses on simulating scenarios where car trips are replaced with micromobility, to estimate the congestion, fuel, and travel time implications for road users. Scenarios such as implementing a congestion fee for cars entering the downtown area will also be modeled to assess the number of trips that could be diverted through toll collections. Finally, we will make recommendations to policymakers on the best locations to place shared bikes and scooters, how many are needed to satisfy potential peak hour demands, and those congestion demand management strategies that could encourage micromobility use. Recommendations will be made from the results obtained in this analysis, engaging with city transportation officials, and by reviewing the policies and best practices of cities that have deployed these modes of transport.
For this study, we plan to conduct a comparative analysis using the cities of Pittsburgh, PA and Seattle, WA as case studies, utilizing several readily available datasets such as 2017 Puget Sound Regional Council Household Travel Survey, Darsky hourly weather data, Healthy Ride bike share data, Scoobie scooter share data, and the “Make My Trip Count Survey” from the Green Building Alliance. Seattle and Pittsburgh were chosen as case studies for this analysis, due to the availability of micromobility trip data and existing relationship with city transportation officials. The methodology and results from this analysis can be applied to other metropolitan areas.
The research team is uniquely positioned to accomplish the three research aims outlined above due to previous work applying modeling and simulation and geospatial tools to emerging technologies in transportation. Corey Harper is an expert in autonomous vehicle impacts in urban environments and data visualization. As part of a previous research project, he estimated the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from underserved populations and made recommendations to policymakers on how to best accommodate this new demand. This paper was published in Transportation Research Part C: Emerging Technologies, and selected among papers in all of Elsevier’s 2,500 academic journals for the Elsevier Atlas Award, an award giving to papers that could have a positive impact on people’s lives. Sean Qian is an expert in transportation network modeling and simulation and has previously used dynamic traffic assignment simulation models to assist the city of Pittsburgh in building more accessible and safer bicycle infrastructure.
References
INRIX. (2019, September 9). Shared Bikes and Scooters Could Replace Nearly 50 Percent of Downtown Vehicle Trips. Retrieved December 3, 2019, from Inrix website: http://inrix.com/press-releases/micromobility-study-us-2019/
National Association of City Transportation Officials. (2017). Bike Share in the U.S.: 2017. Retrieved December 3, 2019, from National Association of City Transportation Officials website: https://nacto.org/bike-share-statistics-2017/
United States Department of Transportation. (2019). Household Travel in America. Retrieved December 3, 2019, from https://www.fhwa.dot.gov/policy/2010cpr/chap1.cfm
Timeline
July 2020-November 2020
Task 1: Identify the Potential for Micromobility in Urban Areas and Estimate Energy and Environmental Benefits
Estimate the upper bound potential for micromobilty to replace short distance trips made by POVs, considering weather, time of day, physical, and trip purpose limitations, and assess the impacts to GHG emissions and energy. Develop a geospatial model that details the number of short distance vehicle and micromobility trips by origin and destination, with current traffic patterns and if micromobility modes replaced a portion of trips made by POVs.
November 2020-April 2021
Task 2: Simulation of Evening Peak Hour Congestion with Updated Travel Demands
Simulate scenarios during the summer and winter months where a portion of evening peak hour car trips are replaced with micromobility modes to estimate the congestion, fuel, and travel time implications for road users. Scenarios such as implementing a congestion fee for cars entering the downtown area will also be modeled to assess the number of trips that could be diverted with congestion demand management policies.
April 2020-June 2021
Task 3: Develop Policy Recommendations
Develop policy recommendations on the best locations to place shared bikes and scooters, how many are needed to satisfy potential peak hour demands, and those congestion demand management strategies that could encourage micromobility mode use. Recommendations will be made from the results obtained in this analysis, by engaging with city transportation officials, and by reviewing the policies and best practices of cities that have deployed these modes of transport. Results will be disseminated in the form of peer-reviewed papers, conference presentations, policy briefs, and social media.
Strategic Description / RD&T
Deployment Plan
A policy brief and peer-reviewed paper of findings and recommendations from the tasks outlined in the previous section will be compiled, providing a blueprint on the potential for micromobility and how cities across the country could encourage its use. We will travel to the Pittsburgh Department of Mobility and Infrastructure to interact with stakeholders about the implications of our research and receive feedback. We will also schedule a meeting during TRB, to meet with the Mobility Services Manager for the City of Seattle to discuss about the implications of our research and receive feedback.
Expected Outcomes/Impacts
Publication in research journal and conference proceedings. Engagement with policymakers, development of policy recommendations, and distribution of a policy brief.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| cdharper@andrew.cmu.edu |
Harper, Corey |
Presidential Postdoctoral Researcher |
PI |
Faculty - Research/Systems |
| bethannh@andrew.cmu.edu |
Hockenberry, Beth |
Research Administrator |
Other |
Other |
| seanqian@cmu.edu |
Qian, Sean |
Associate Professor |
Co-PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$34970.00
Total Project Budget (from all funding sources)
$69940.00
Documents
| Type |
Name |
Uploaded |
| Presentation |
2020_-_Harper_Micromobility.pptx |
March 15, 2020, 2:19 p.m. |
| Data Management Plan |
DMP_Micromobility_Harper.pdf |
March 15, 2020, 3:25 p.m. |
| Presentation |
Analysis of the Potential for Micromobility to Replace Short Car Trips in Urban Areas, And Impacts on Congestion |
March 26, 2021, 8:16 a.m. |
| Progress Report |
310_Progress_Report_2020-09-30 |
Sept. 28, 2020, 6:50 a.m. |
| Publication |
Societal Impacts of a Complete Street Project in a Mixed Urban Corridor: Case Study in Pittsburgh |
March 21, 2021, 5:04 p.m. |
| Publication |
Improving Short-Term Travel Speed Prediction with High-Resolution Spatial and Temporal Rainfall Data |
March 26, 2021, 8:10 a.m. |
| Presentation |
Advancing Towards a Smarter and More Sustainable Transportation System |
March 26, 2021, 8:16 a.m. |
| Presentation |
Advancing Towards a Smarter and More Sustainable Transportation System |
March 26, 2021, 8:16 a.m. |
| Progress Report |
310_Progress_Report_2021-03-31 |
March 26, 2021, 8:16 a.m. |
| Final Report |
Final_Report_-_310.pdf |
Aug. 1, 2021, 9:50 a.m. |
| Publication |
Net-societal and net-private benefits of some existing vehicle crash avoidance technologies |
May 2, 2022, 9:10 a.m. |
| Publication |
Economic and Behavioral Dimensions of Urban Transport Policy |
May 2, 2022, 9:11 a.m. |
| Publication |
Socioeconomic and usage characteristics of transportation network company (TNC) riders |
May 2, 2022, 9:11 a.m. |
| Publication |
Travel impacts of a complete street project in a mixed urban corridor |
May 2, 2022, 9:12 a.m. |
| Publication |
Environmental Impacts of Short Car Trip Replacement with Micromobility Modes |
May 2, 2022, 9:12 a.m. |
| Publication |
Congestion and environmental impacts of short car trip replacement with micromobility modes |
May 2, 2022, 9:13 a.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| City of Pittsburgh |
Deployment Partner Deployment Partner |
| City of Seattle |
Deployment Partner Deployment Partner |
| Healthy Ride |
Deployment Partner Deployment Partner |
| Puget Sound Regional Council |
Deployment Partner Deployment Partner |
