Walking is the primary mode of reaching metro stations, yet the quality of pedestrian networks around these stations has not been well researched. Considering the objective physical characteristics of pedestrian networks and the subjective assessments of walkers on the routes, this study developed an evaluation model that integrated the Analytic Hierarchy Process and Entropy Weight Method with human–machine adversarial scoring and cosine similarity to validate the reliability. Nineteen indicators concerning four fundamental criteria, including accessibility, convenience, safety, and comfort, were applied with data acquired from eight stations in Tianjin, China. Results reveal that accessibility and safety indicators weigh more than convenience and comfort indicators. The quality of pedestrian networks around the public-service and comprehensive stations scores higher than that around residential stations, while walking environment quality near commercial stations shows significant disparities. These findings highlight the importance of prioritizing accessibility and safety while enhancing convenience and comfort in the renewal of the pedestrian network in Tianjin. The assessment model provides a valuable tool for urban policymakers and planners, enabling the formulation of sound pedestrian-network policies, facilitating higher-quality walking access and egress trips to stations, and encouraging transit-oriented development.

Cycling significantly contributes to improving metro accessibility; however, the quality of bicycle environments surrounding metro stations remains insufficiently studied. This study develops a criteria–indicators assessment framework that incorporates both objective characteristics of bicycle infrastructure and subjective perceptions of bicycle access to metro stations. The framework consists of four primary criteria—accessibility, convenience, safety, and comfort—along with eighteen sub-level indicators. Taking central Tianjin as the study area, the study evaluated the cycling environment quality around eight representative metro stations by employing information entropy and the analytic hierarchy process, with cosine similarity used to compare the outcomes against human–machine adversarial scoring result to ensure analytical robustness. The findings reveal substantial disparities in cycling infrastructure, with safety and accessibility exhibiting higher scores than convenience and comfort. Additionally, cycling environment quality is higher around comprehensive and public-service stations compared to residential stations, while commercial stations exhibit the lowest quality. The study underscores the necessity of expanding protected bike lanes, enhancing route directness, and improving parking and wayfinding facilities to promote cycling as an effective first- and last-mile metro access mode.

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.