Using Multivariate Statistical Methods to Analyze High-Quality Bicycle Path Service Systems: A Case Study of Popular Bicycle Paths in Taiwan

Abstract

Taiwan has promoted bicycle tourism for nearly 20 years, and the bicycle paths it has constructed throughout the island are diverse in design. In the present study, an evaluation scale for bicycle path sightseeing potential was devised with a focus on the overall service quality of the paths; 30 popular bicycle paths were analyzed using a field survey, with expert consultation on quantitative indicators, and a qualitative analysis entailing interviews with people regarding the bicycle paths. A multivariate statistical analysis was performed on the quality of the service systems for these paths. The results revealed that the quality of these service systems is influenced by four principal components, namely, landscape attractiveness, image management, bicycle-specific paths, and accessibility, for a total explanatory power of 76.21%; the individual explanatory power of these components was 25.89%, 21.49%, 16.81%, and 12.03%, respectively. Bicycle path conditions, service maintenance, and cleanliness and bicycle specificity are required for future high-quality bicycle paths; diverse bicycle rental services and bicycle types, entrance visibility, and ecological introduction boards along paths are value-added factors to bicycle path quality.

A Survey of Technologies and Recent Developments for Sustainable Smart Cycling

“…It concluded that most participants were looking for characteristics such as "fast", "safe", "simple" and "attractive" for the routes they would take. Similar conclusions might be inferred from [45]. Thus, it is reasonable to say that there are some guiding rules that have been considered when implementing such solutions, ultimately supporting the adoption of bikes for transportation.…”

The Munich Bikeability Index: A Practical Approach for Measuring Urban Bikeability

“…In general, planning and implementing bicycle infrastructure in urban environments is challenging [20] and may make use of complex optimization techniques [21]. Other approaches have focused on evaluating a present bicycle infrastructure by observing cyclists [22] and relating extracted patterns [23,24], such as route preferences [25], to the underlying road network structure [26].…”

Bicycle Traffic Model for Sustainable Urban Mobility Planning

“…Bicycle traffic models allow the resolution of problems related, among other things, to demand and supply [116,122], route choice [123], lane change, and queueing behaviour [124][125][126]. The demand for cycling, the choice of bicycle routes, and the choice of cycling modes were found to be influenced by many factors, including the built environment [127][128][129][130]; socioeconomic [131][132][133][134], psychological (habits, attitudes, norms, stress), and physical characteristics [135][136][137][138][139]; policies that promote cycling [46,59,84,138,[140][141][142]; infrastructure for cyclists [39,[143][144][145]; cost; effort; distance travelled; travel time; road safety; climate and weather; and travel motivation [146][147][148][149][150]. The frequency of commute to work, the time to cycle, and the length of the journey are important features of active commute behaviour.…”

“…From the opinions of the respondents presented in Section 4.2, it also appears that the pavement indicated by cyclists as the most attractive is a smooth, even surface (asphalt and, to a lesser extent, smooth concrete blocks). Many previous studies indicate that the type of surface is a factor that determines the choice of route by a cyclist [22,129,130,163,215,216]; however, it is difficult to find direct research results on cyclist preferences with regard to this issue [217,218]. One of the few works that unambiguously states that the quality of the surface is an important decision factor for cyclists when choosing a cycling route is the study by Landis et al [219].…”