Everyday Cycling in Urban Environments: Understanding Behaviors and Constraints in Space-Time - Computational Approaches for Urban Environments - page 194

Tabl e 8. 3 All trips: gender and cycling with off/on/near network - corridor space characteristics

Off/on/near network - corridor space characteristics (weighted
distance traveled in km/%)

Outside buffers
(off network) km

10-20 m buffer
(near network) km All trips 10 m buffer (on network) km Tot al (km )

Female 695 (32.5 %) 1,262 (59.1 %) 179 (3.3 %) 2,136

Male 1,167 (34.6 %) 1,900 (56.3 %) 305 (5.5 %) 3,372

Tot al 1,862 (33.8 %) 3,162 (57.4 %) 484 (8.8 %) 5,508

Fig. 8.7 All trips in space and time: cycling with off/on/near network - corridor space character-

istics

(i.e., on/off/near regions). The percentage differences between male and female
bike trips falling within the on regions (34.5 22.9 D 8.6%) are almost the same
as the percentage of bike trips falling within the off region (21.2 12.6 D 11.6%)
suggesting some similarities in cycling behaviors based on gender. The addition of
the trade-off region, the near region (5.5 3.3 D 2.2)%, estimates to the on estimates,
making it about 10.8 %, even increases the degree of similarities between on and off
intra-differences. Figure 8.7 shows the temporal dimension of all the cycle trips per
the on/off/near regions and suggests that longer trips occurred in November. It also
shows the frequency of GPS logged points per day pointing out low cycling uptake
on Sundays (i.e., dates shown on scale like October 16, 23, etc.). Depending on how
the STC 3D environment is oriented, different conclusions could be drawn making