Bus bunching, a common phenomenon during transit operation, makes negative effects on reliability and service level of the bus system. In most urban centers of our country, many bus lines usually serve in a corridor. Different buses may interact each other in the corridor, which may aggravate the bus bunching. However, previous studies on bus bunching focused on single bus service. In addition, with the popularization of bus data acquisition and the maturity of data processing methods, accuracy of bus bunching research meets more opportunities. Based on related research summary, we choose buses lines with obvious bus bunching, corridor and high-quality data as the object after preliminarily processing and analyzing the big data in Foshan City. First, we analyze the theory of the influence of bus collinearity on bus bunching. Next, with various processing methods, initial data is filtrated, processed and transformed into information and format that can apply to analyze bus bunching directly. We separately compute the average stop time at stops and average speed along the road when different buses meet or not, and then analyze the differences between them to prove the impact when different buses serve in the corridor. This paper expands the traditional research on the influencing factors of single-line bus bunching to the influence of buses on different lines in the common line segment, which enriches the influencing factors of bus bunching. The results have important theoretical significance for enriching and completing existing theory and methods of transit system and practical value for improving service level, intelligent management decision support system of transit. Keywords:bus big data, bus bunching, multi-line collinea

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