The multi‐scale characteristic of online car‐hailing traffic volume can reflect the implied distribution pattern, which is crucial for traffic management and even urban planning. Nevertheless, the spatio‐temporal heterogeneity of online car‐hailing traffic volume makes it challenging to analyze its multi‐scale characteristics effectively. Here, a method named multi‐scale characteristic analysis for online car‐hailing traffic volume with quantum walk (MCATV‐QW) is proposed. MCATV‐QW adopts quantum walks to generate multi‐scale probability patterns that online car‐hailing appears at different locations over time. Then stepwise regression is applied to screen the generated multi‐scale probability patterns, to further analyze the multi‐scale characteristic. We validate MCATV‐QW with online car‐hailing traffic volume in the northeast of Chengdu, China. MCATV‐QW not only achieves better simulation performance, but also reveals the distribution pattern that the influence degree of multi‐scale probability patterns weakens from southwest to northeast of study area. MCATV‐QW also reflects the traffic spatial pattern that is dominated by gradual traffic (48%), with both abrupt (26%) and uniform traffic (26%).
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