Exploring the Relationship Between the Built Environment and Spatiotemporal Heterogeneity of Urban Traffic Congestion During Tourism Peaks: A Case Study of Harbin, China

Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We apply a Multiscale Geographically Weighted Regression (MGWR) model to geospatial data across four typical peak periods and benchmark the results against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The MGWR model demonstrates superior capability in capturing spatial non-stationarity and multiscale effects. The results reveal strong spatiotemporal heterogeneity in the effects of built environment factors on congestion. Intersection density demonstrates a stronger mitigating effect during weekday evening peaks. Catering facilities significantly exacerbate congestion in tourist hotspots. Tourism-related facilities such as hotels and attractions intensify congestion during weekend peaks. Parking availability shows dual impacts, with peripheral parking reducing pressure and central clustering worsening congestion. Our geospatially disaggregated results provide empirical evidence for location-sensitive and temporally adaptive traffic management and urban design strategies. This study highlights the value of MGWR-based spatial modeling in supporting geoinformation-driven urban mobility planning.