In this study, the route choices of cyclists in an urban environment were related to nine variables on street design and dynamics: tree shade, car parking, traffic speed, traffic volume, mixed or segregated traffic, dedicated bicycle facility, number of car lanes, number of restaurants, and number of retail stores. Cycling itineraries were collected with a tracker equipped with GPS and responses from an intercept survey ( n = 212). Distance-equivalent routes were obtained with a geographical information system. All street segments used by cyclists were investigated to obtain original relevant traffic and physical data. Street segments were coded for the nine variables and entered into a multiple linear regression model in which cyclist volume from the revealed preference survey was the dependent variable. Tree shade, traffic speed, mixed traffic, and the presence of restaurants were found to be significant or highly significant, while car parking and traffic volume were nearly significant. The model explained 21% of the variance in route choice in this sample of four kinds of cycling trips in Shenzhen, China. Examination of the distance-equivalent alternative routes revealed that the number of turns was significantly higher, while tree shade and mixed traffic were not significant in choice.