Evaluation of the methodology for determining cycling flows in Lithuania
Author:
Affiliation:
1. Departament of Roads, Faculty of Environment Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Abstract
Publisher
VILNIUS TECH
Reference31 articles.
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2. Akar, G., Chen, N., & Gordon, S. I. (2016). Influence of neighborhood types on trip distances: Spatial error models for Central Ohio. International Journal of Sustainable Transportation, 10(2), 284-293. https://doi.org/10.1080/15568318.2014.903447
3. Barberan, A., Silva, J. A., & Monzon, A. (2017). Factors influencing bicycle use: A binary choice model with panel data. Transportation Research Procedia, 27, 253-260. https://doi.org/10.1016/j.trpro.2017.12.097
4. Bhowmick, D., Saberi, M., Stevenson, M., Thompsonc, J., Winters, M., Nelson, T., Zarpelon Leaog, S., Seneviratne, S., Pettit, C., Vu, H. L., Nice, K., & Beck, B. (2022). A systematic scoping review of methods for estimating link-level bicycling volumes. Transport Reviews. https://doi.org/10.1080/01441647.2022.2147240
5. Dadashova, B., & Griffin, G. P. (2020). Random parameter models for estimating statewide daily bicycle counts using crowdsourced data. Transportation Research Part D: Transport and Environment, 84, 102368. https://doi.org/10.1016/j.trd.2020.102368
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