Abstract
There is a growing global interest in developing and accommodating sustainable modes of travel. Cycling is an important mode of travel which offers powerful solutions to chronic traffic problems of congestion and emissions. The accuracy of cyclist position is considered as an important parameter for the calculation of cyclist speed. Cyclist speed data is required for precise design of traffic control measures, for safety studies, and for sight distance analysis. Different speed measurement techniques and results are found in the literature. This study critically examines peer-reviewed studies which discuss different techniques for measuring cyclist speed. This review covers the accuracy of the measurement techniques and highlights limitations of the reviewed studies. These identified limitations are classified as: limited range of movement directions, selection of observed cyclists, seasonal variation in measurements, completeness of results reporting, reporting of equipment limitations, and measurement validation. The study summarizes previous findings of cyclist speed statistics. This study also reviews the level of automation in speed measurement. Any technique involving a human input during the field data collection or analysis to estimate cyclist speed is classified as a manual. Further categories for semi-automated and automated measurement techniques are established. Meta-analysis was conducted to test whether reported cyclist speed depends on where they were recorded; intersections or road segments. The result of the unpaired t-test showed that there is no significant difference between the means of the cyclists speed at road sections and signalized intersections at the 95% of confidence level. The study emphasizes the growing importance of the use of automated computer vision techniques for speed measurement. The paper contrasts the advantages of computer vision techniques with other measurement techniques.
•The accuracy of cyclist position is an important parameter for the calculation of cyclist speed.•To get the naturalistic cyclist behavior, interfering with other road users without any prior notice is required.•Computer vision analysis with video recording provides an opportunity to closely study the data at all stages.•Video analysis permits continuous tracking of cyclists to calculate a speed profile.