Reducing Extreme Commuting by Built Environmental Factors: Insights from Spatial Heterogeneity and Nonlinear Effect

Nowadays, the number of people enduring extreme commuting is increasing, exacerbating traffic problems and harming individual well-being. To quantify the extreme commuting, we propose an extreme commuting severity (ECS) index that combines the number of extreme commuting trips with their specific distances, where a one-way trip with a commuting distance of at least 25 km is regarded as an extreme commuting trip. In Beijing, the ECS index shows substantial spatial variability, with maximum values exceeding 30,000 for origins and 50,000 for destinations, underscoring the severe commuting burden in specific areas. By integrating the geographically weighted random forest (GWRF) with Shapley additive explanations (SHAP), we model both nonlinear effects and spatial heterogeneity in how the built environment shapes extreme commuting. Compared with benchmark models, the proposed GWRF model achieves the highest predictive performance, yielding the largest R2 and the lowest absolute and relative indicators across both generation and attraction scenarios. Notably, the GWRF improves explanatory power over the global model by a substantial margin, highlighting the importance of incorporating spatial heterogeneity. SHAP-based global importance results show that residential density (17.58%) is the most influential factor for ECS, whereas in the attraction scenario, company density exhibits the strongest contribution (20.7%), reflecting the strong pull of major employment clusters. Local importance maps further reveal pronounced spatial differences in effect direction and magnitude. For instance, although housing prices have modest global importance, they display clear spatial heterogeneity: they exert the strongest influence on extreme commuting generation within the Fourth Ring Road and around the North Fifth Ring, whereas in the attraction scenario, their effects concentrate in the southern part of the core area. These findings provide new empirical insights into the mechanisms underlying extreme commuting and highlight the need for spatially differentiated planning strategies.