Urban transportation network planning method considering traffic accessibility

To address the challenges of limited line coverage, protracted planning durations, and elevated construction expenses inherent in traditional urban transportation network planning techniques, an urban transportation network planning method considering traffic accessibility is proposed. This method leverages multiple sensors to establish an urban traffic data collection framework, enabling the acquisition of pertinent traffic information. Data preprocessing steps, including clustering and missing data imputation, are executed, followed by the application of the Kalman filtering method to forecast urban traffic flow. Traffic accessibility is computed based on the spatial interaction model's relative accessibility, and this metric is integrated with traffic flow, fairness indicators for benefit allocation, the Gini coefficient for urban road resources, social cost sharing indicators, and urban transportation construction costs to formulate an urban transportation network planning model. The experimental outcomes reveal that the proposed approach achieves a maximum coverage of 80.1%, with a maximum planning time of 156.47 seconds and a total construction cost of 5.7076 million yuan. By comparing the literature results, its superiority is demonstrated.