Analyzing the Multi‐scale Characteristic for Online Car‐hailing Traffic Volume with Quantum Walk

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%).