Fig. 1. The method for estimating potential bicycle travel demand based on mobile phone location data

Fig. 2. The principle of stop identification

Fig. 3. The interpolation of move trajectory segment with same start and end point by the furthest point

Fig. 4. Shanghai's administrative districts

Fig. 5. The density distribution of the base stations in the research dataset in 2012

Fig. 6. The probability distribution of the coverage radius of the base stations in the research dataset in 2012

Fig. 7. The mobile phone location data sampling time interval distribution

Fig. 8. The distribution of the Shanghai public transportation stations in Shanghai in 2017

Fig. 9. The spatial distribution for mobile phone user travel OD extracted in the research dataset in 2012

Fig. 10. Thespatial distribution for potential cycling and parking demand in Shanghai

Fig. 11. The spatial distribution for potential cycling and parking demand in Shanghai during some periods of time

Fig. 12. The temporal characteristics of potential bicycle travel demand in Shanghai in 2012

Fig. 13. Thetemporal characteristics of potential bicycle travel demand in some areas of Shanghai in 2012

Fig. 14. The temporal characteristics of public transportation transfer travel demand in Shanghai

Fig. 15. The spatial distribution of the top 10 public transportation stations with the highest transfer travel demand

Fig. 16. The temporal characteristics of public transportation transfer travel demand for partial public transportation stations