The metropolitan area is a spatial phenomenon that emerges after the regionalization of large cities reaches a certain stage. As urbanization deepens in our country, the connection between urban and rural areas becomes closer, making the concept of metropolitan area increasingly important. Under the context of metropolitan area's integrated development, the conventional single-structured and flat public transportation network has struggled to adapt to new trends and changes in travel patterns among metropolitan residents. The appeal of public transportation has waned, leading to a continuous decline in passenger flow and exacerbating other issues.

Firstly, in response to the confusion surrounding the concept of metropolitan areas and varying scopes of definition, a systematic review was conducted on literature pertaining to metropolitan areas in the United States, Japan, and China. This led to a redefinition of the concept of metropolitan areas and establishment of a framework for delimit metropolitan area. Based on spatial correlation, Landscan population density data analysis is utilized to determine the central city of the metropolitan area. Using a time-space binning method with large-scale mobile phone signaling data, residents' travel data is extracted and users' occupation and residence are identified while considering production and life connections as well as time-space constraints. The proposed method is utilized to delimit the Shanghai metropolitan area. The findings indicate that the Shanghai metropolitan area corresponds with its administrative boundaries, which holds significant implications for formulating and implementing development policies as well as serving as a cognitive premise for planning the bus network in this region.

Secondly, the complex network theory is applied to analyze the characteristics of the traffic network in Shanghai metropolitan area from both supply and demand perspectives, aiming to provide insights and guidance for optimizing the bus network in this region.. Specifically, at the demand level, we employed SVD to extract stable travel demand from mobile phone signaling data and construct a comprehensive travel network. Through the analysis of travel node centrality, it is evident that residents' travel activities exhibit distinct characteristics of decentralized agglomeration, clearly demonstrating the distributed polycentric structure of Shanghai's metropolitan area dominated by five new cities. Through analyzing the spatial distribution characteristics of the network, it is discovered that residents' travel exhibits diversified and multi-level features across multiple distance scales. Short-distance travel displays dispersed spatial distribution, while long-distance travel demonstrates obvious centrality and hierarchy. The hierarchical nature of travel demand highlights the necessity for constructing a multi-level public transportation network. At the supply level, bus station and route data are collected from online network maps to construct weighted bus station networks and bus route networks based on Space L and Space R spatial network models. On the one hand, the topological characteristics of the two bus networks are analyzed from a macroscopic perspective, and the overall connectivity in the regional network is investigated. The findings indicate that while the bus network exhibits evident local clustering features, its global connectivity is not strong. On the other hand, the significance of nodes within a network is gauged by multiple centrality indices. The findings indicate that various stations and lines in the network serve distinct functions. A lack of connecting nodes within the network hinders travelers' mobility throughout it.

Finally, a hub-and-spoke multi-level metropolitan bus network optimization layout method is proposed based on the characteristics of the traffic network. The study focuses on three key elements of the network layout: division of spatial structure in the metropolitan area, evaluation and classification of site importance, and identification of hub sites. Firstly, the community detection algorithm is utilized to partition the spatial configuration of the metropolitan area based on travel demand distribution. Secondly, a multi-dimensional evaluation system for bus stations is established, which comprehensively considers the characteristics of network structure, spatial location and activity demand of stations. The weighted TOPSIS algorithm is utilized to calculate the importance of traffic network stations, and clustering algorithm is applied to classify the stations based on their importance. Subsequently, a hub node identification method is established by integrating the significance of stations and central elements, followed by an empirical test on the layout of hub nodes and network optimization for inter -regional bus networks in metropolitan areas. The analysis of optimization results indicates that the inter-regional network can enhance connectivity and accessibility, improve station connection capacity, and increase station utilization efficiency without expanding the network scale. Simultaneously, by simplifying the network structure and shortening line length, it is possible to further reduce waiting times while enhancing bus service reliability and appeal through increased departure frequency and scheduling flexibility.

From a data-driven perspective, this paper utilizes big data to delimit metropolitan area, analyzes the distribution characteristics and rules of residents' travel demand within said area, and optimizes its transit service network based on the concept of networked travel. This represents an active exploration into optimizing theory and methods for metropolitan transit networks.

Key Words: metropolitan area, Characteristics of travel demand, characteristics of bus networks, hub stations identification, bus service network optimization