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

被引:0
|
作者
Raida, Afrida [1 ]
Ohlms, Peter B. [2 ]
Chen, T. Donna [1 ]
机构
[1] Univ Virginia, Dept Civil & Environm Engn, Charlottesville, VA USA
[2] Virginia Transportat Res Council, Charlottesville, VA 22903 USA
关键词
planning and analysis; data sources; performance metrics; public transportation; big data; ridership analysis;
D O I
10.1177/03611981231197667
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:431 / 443
页数:13
相关论文
共 50 条
  • [1] Ways of increasing transit ridership-lessons learned from successful transit agencies
    Tabassum, Nawshin
    Kalantari, Hannaneh Abdollahzadeh
    Kaniewska, Justyna
    Ameli, S. Hassan
    Ewing, Reid
    Yang, Wookjae
    Promy, Noshin Siara
    CASE STUDIES ON TRANSPORT POLICY, 2025, 19
  • [2] Current State of Practice in Transit Ridership Prediction: Results from a Survey of Canadian Transit Agencies
    Diab, Ehab
    Kasraian, Dena
    Miller, Eric J.
    Shalaby, Amer
    TRANSPORTATION RESEARCH RECORD, 2019, 2673 (08) : 179 - 191
  • [3] Influence of weather conditions on transit ridership: A statistical study using data from Smartcards
    Arana, P.
    Cabezudo, S.
    Penalba, M.
    TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2014, 59 : 1 - 12
  • [4] Assessment of the transit ridership prediction errors using AVL/APC data
    You-Jin Jung
    Jeffrey M. Casello
    Transportation, 2020, 47 : 2731 - 2755
  • [5] Assessment of the transit ridership prediction errors using AVL/APC data
    Jung, You-Jin
    Casello, Jeffrey M.
    TRANSPORTATION, 2020, 47 (06) : 2731 - 2755
  • [6] Segmenting transit ridership: From crisis to opportunity
    Tiznado-Aitken, Ignacio
    Palm, Matthew
    Farber, Steven
    TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2024, 190
  • [7] Substitutes or complements? Examining effects of urban rail transit on bus ridership using longitudinal city-level data
    Yang, Chao
    Yu, Chengcheng
    Dong, Wentao
    Yuan, Quan
    TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2023, 174
  • [8] Ridership-Boosting Strategies for Transit Agencies with Different Characteristics: Insights from a Nationwide Survey
    Chen, Andong
    Li, Wei
    Lee, Chanam
    Towne Jr, Samuel D.
    Zhong, Sinan
    Ory, Marcia G.
    TRANSPORTATION RESEARCH RECORD, 2024,
  • [9] The relationship between transit rich neighborhoods and transit ridership: Evidence from the decentralization of poverty
    Wang, Kyungsoon
    Woo, Myungje
    APPLIED GEOGRAPHY, 2017, 86 : 183 - 196
  • [10] HISTOSPLINE INTERPOLATION FOR DATA INPUT INTO THE TRANSIT RIDERSHIP FORECASTING-MODEL
    LINDQUIST, PS
    ITE JOURNAL-INSTITUTE OF TRANSPORTATION ENGINEERS, 1986, 56 (11): : 31 - 36