How Does Land-Use and Urban Form Impact Bicycle Flows--Evidence from the Bicycle-Sharing System (BIXI) in Montreal

By Ahmadreza Faghih Imani, Naveen Eluru, Ahmed El-Geneidy, Michael G. Rabbat, Usamah Haq. 2014

461 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/652ae4dd16b5f909d7895ee7e0b09bdbe564f59b

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In Transportation

Citations in the corpus

Bikeshare: A Review of Recent Literature (2016, background): “Some researchers have examined the factors associated with higher and lower levels of docking station activity (e.g. Faghih-Imani et al., 2014), finding that weather and the presence of restaurants have a predictable impact of station activity.”
Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City (2017, background): “…bicycling infrastructure, land use and built environment, public transportation infrastructure, temporal and meteorological attributes (Rixey, 2013; Faghih-Imani et al., 2014; Gebhart and Noland, 2014; Faghih-Imani and Eluru, 2015; Wang et al., 2015; Faghih-Imani and Eluru, 2016a; Faghih-Imani and…”
Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach (2018, result): “As a result, more bikeshare stations should be built in suburban areas to shorten the average riding distance and potentially attract more people to apply bikeshare as a feeder mode to metro, consistent with the results of Parkin et al. [62] and Faghih-Imani A et al. [63].”
Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China (2018, background): “A Shanghai study showed that passenger flow in the morning peak was approximately two times than that of the evening peak [20]; however, research related to the BIXI public bicycle system in Montreal found the opposite to be true [21].”
Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach (2017, methodology): “ Denver B-Cycle, and Nice Ride MN systems. Independent variables such as demographic factors and built environment factors are extracted based on a 400-meter buffer around each station (Rixey, 2013). Faghih-Imani et al. (2014) similarly built linear mixed models to predict the hourly bike-sharing de”
Bike sharing: A review of evidence on impacts and processes of implementation and operation (2015, methodology): “A study using real usage data from BIXI, Montreal, combined with a general statistical modelling technique (Faghih-Imani et al., 2014) identified the following key correlates to bicycle flows: weather conditions, with users more likely to bike-share under good weather conditions; time of day/week: d”