四度优选法:基于多因素行为偏好的城市地标群游览路径规划方法OACHSSCD

Against the backdrop of the deepening people-oriented urbanism paradigm and the rapid emergence of new urban touring scenarios such as CityWalk,urban landmark clusters,as key carriers of urban culture and identity,have become increasingly important in the planning and evaluation of touring routes.Such efforts are critical to upgrading pedestrian systems and enhancing the overall quality of urban touring experiences.Conventional route-planning approaches have largely prioritized single efficiency-based criteria,such as shortest distance,while paying insufficient attention to behavioral preferences and multidimensional experiential qualities.As a result,they are poorly suited to the growing demand for people-oriented urban touring.To address this gap,this study proposes a quantitative route optimization method grounded in multiple behavioral preferences to support the planning,design,and human-centered renewal of urban pedestrian systems.. Using urban landmark clusters as anchor points,this study develops a technical framework termed Four-Metric Optimization,which integrates four dimensions:route(angular)choice potential,(public transport)connectivity,(route interface)functional diversity,and(walking environment)spatial vitality.The framework establishes a complete analytical workflow encompassing indicator measurement,weight analysis,route screening,and robustness testing. Methodologically,a high-precision road network dataset is first constructed on a GIS platform to calculate shortest paths between landmarks and generate a complete set of candidate routes.These routes are then screened according to constraints such as route length,walking time,terrain elevation,network connectivity,etc.A Four-Metric evaluation method is subsequently employed for quantitative scoring,and Monte Carlo simulation is used to conduct sensitivity analysis and robustness testing.Multiple weighting scenarios are further compared,and implementable touring routes are ultimately recommended in combination with route profiling and policy orientations.ns. A sample area along Tianjin Haihe River corridor is selected as the empirical case for application and validation.The results show that route(angular)choice potential is the primary driving factor and exerts the greatest influence on route ranking.(Public transport)connectivity is a highly sensitive factor that strongly moderates touring quality.Functional diversity is moderately sensitive and becomes particularly influential in areas with marked differences in land-use mix.Spatial vitality serves as a relatively stable baseline factor and contributes less to ranking fluctuations.The Monte Carlo simulation further indicates that several routes maintain high probabilities of being ranked first or among the top three,demonstrating strong robustness and qualifying them as globally advantageous routes.Based on these results,the study produces visualized evaluation outputs and optimized landmark touring routes. The findings demonstrate that the Four-Metric Optimization model can effectively capture the multidimensional experiential demands of urban touring and is well suited to the comprehensive evaluation and optimization of touring routes within urban landmark clusters.It provides a reliable analytical basis and practical guidance for the targeted improvement of urban street spaces,while offering an operational decision-support tool for the high-quality development and renewal of people-oriented built environments.In addition,the proposed framework retains substantial potential for further extension,which would support the continued evolution of urban touring route planning from a paradigm of technical rationality toward one of comprehensive experiential optimization.