Effects of data aggregation (buffer) techniques on bicycle volume estimation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Transportation,Development,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11116-023-10452-7.pdf
Reference24 articles.
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2. Broach, J. et al.: Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes. Transporation Research Record, Washington, DC (2023). https://doi.org/10.1177/03611981231182388.
3. Buehler, R.: Determinants of bicycle commuting in the Washington, DC region: the role of bicycle parking, cyclist showers, and free car parking at work. Transp. Res. Part d: Transp. Environ. 17(7), 525–531 (2012). https://doi.org/10.1016/j.trd.2012.06.003
4. Chen, P., Zhou, J., Sun, F.: Built environment determinants of bicycle volume: a longitudinal analysis. J. Transp. Land Use 10(1), 655–674 (2017)
5. Fagnant, D.J., Kockelman, K.: A direct-demand model for bicycle counts: the impacts of level of service and other factors. Environ. Plann. B. Plann. Des. 43(1), 93–107 (2016). https://doi.org/10.1177/0265813515602568
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