Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

Author:

Raida Afrida1ORCID,Ohlms Peter B.2ORCID,Chen T. Donna1ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA

2. Virginia Transportation Research Council, Charlottesville, VA

Abstract

Researchers and planners require ridership data to study factors that influence people’s choice to use transit. However, the data can be challenging to obtain directly from transit agencies. Crowdsourced big data platforms such as StreetLight promise easily accessible ridership-related data in standard formats. It is important to assess the reliability of these data, particularly for transit agencies serving small- to medium-sized cities, which are less likely than agencies in large cities to have ridership data in standard formats. In this study, hourly ridership data from 2019 were collected from four bus transit agencies and one rail agency in Virginia and compared with StreetLight data. Comparisons for rail data were made on a station-to-station basis. Bus data comparisons were made at the city-limit level and at an aggregated-route level for each agency. In sum, StreetLight could not provide 2019 bus activity data for more than half of the localities in Virginia. Comparisons between transit agency and StreetLight data showed smaller root mean square errors when longer periods were analyzed (e.g., 4 versus 2 months). Although order of magnitude of ridership may indicate whether StreetLight can provide bus activity data, the former was not found to be correlated with the accuracy of the latter. Using data from StreetLight’s current algorithm might not be appropriate without verification against agency data, especially for agencies in small- to medium-sized cities.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference31 articles.

1. StreetLight Data. Bus & Rail Metrics Methodology, Data Sources, and Validation White Paper, Version 1.1. 2021. https://learn.streetlightdata.com/bus-and-rail-methodology. Accessed June 30, 2022.

2. Tsapakis I., Cornejo L., Sánchez A. Accuracy of Probe-Based Annual Average Daily Traffic (AADT) Estimates in Border Regions. Texas A&M Transportation Institute, College Station, 2020. https://static.tti.tamu.edu/tti.tamu.edu/documents/TTI-2020-1.pdf. Accessed June 30, 2022.

3. Turner S., Koeneman P. Using Mobile Device Samples to Estimate Traffic Volumes. Publication MN/RC 2017–49. Minnesota Department of Transportation, St. Paul, 2017. https://mdl.mndot.gov/_flysystem/fedora/2023-01/201749.pdf. Accessed June 28, 2022.

4. Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes

5. Commonwealth of Virginia. How the Commonwealth Is Using Transit and Transportation Demand Management to Reduce Congestion and Use of Single-Occupant Vehicles. 2019. https://www.drpt.virginia.gov/media/ctknq4yk/2019-transit-tdm-report.pdf. Accessed July 25, 2022.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3