The planning of urban transportation infrastructure and land-related policies has a significant impact on the living conditions of urban residents and socio-economic development, particularly in emerging economies. As urbanization continues to advance, Metropolitan Areas (MAs) have become crucial for achieving industrial coordination, functional complementarity between cities, and integrated regional development. Applying Social Network Analysis (SNA), the gravity model, and Quadratic Assignment Procedure (QAP) analysis, this study investigated the spatial-temporal distribution patterns of High-Speed Railway (HSR) networks and economic networks in MAs in China and the dynamic coupling relationship between these two networks. The findings revealed that, although core cities in the Yangtze River Delta MA in China exert varying degrees of radiation and driving effects on the economic development of surrounding cities, the overall development remains immature with a noticeable disequilibrium phenomenon. The coupling relationship between the HSR networks and the economic networks also differs significantly among different MAs. It is expected that the findings and suggestions of this study will contribute to the improvement of urban planning and governance and facilitate coordinated development between urban transportation infrastructure and the economy in emerging economies.

Text2SQL can help non-professionals connect with databases by turning natural languages into SQL. Although previous researches about Text2SQL have provided some workable solutions, most of them extract values based on column representation. If there are multiple values in the query and these values belong to different columns, the previous approaches based on column representation cannot accurately extract values. In this work, we propose a new neural network architecture based on the pre-trained BERT, called M-SQL. The column-based value extraction is divided into two modules, value extraction and valuecolumn matching. We evaluate M-SQL on a more complicated TableQA dataset, which comes from an AI competition. We rank first in this competition. Experimental results and competition ranking show that our proposed M-SQL achieves state-of-the-art results on TableQA. INDEX TERMS Text2SQL, M-SQL, pre-trained, multi-task learning.

Automatically parsing SQL queries from natural languages can help non-professionals access databases and improve the efficiency of information utilization. It is a long-term research issue and has recently received attention from the relevant communities. Although previous researches have provided some workable solutions, most of them only consider table schemas and natural language questions when parsing SQL queries, and do not use table contents. We observe that table contents can provide more helpful information for some user questions. In this paper, we propose a novel neural network approach, F-SQL, to focus on solving the problem of table content utilization. In particular, we employ the gate mechanism to fuse table schemas and table contents and get the more different representation about table schemas. We test this idea on the WikiSQL and TableQA datasets. Experimental results show that F-SQL achieves new stateof-the-art results on WikiSQL and TableQA.

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Ridesharing services aim to reduce the users' travel cost and optimize the drivers' routes to satisfy passengers' expected maximum matching times in practice request dispatching. Existing works can be roughly classified into two types, i.e., online-based and batch-based methods. The former mainly focuses on responding quickly to the requests, and the latter focuses on meticulously enumerating request combinations to improve service quality. However, online-based methods perform poorly in service quality due to the neglect of the sharing relationship between requests, while batch-based methods fail on efficiency. None of these works can smoothly balance the service quality and matching time cost since the matching window is not sufficiently explored or even neglected. To cope with this problem, we propose a novel framework E-Ride, which comprehensively leverages the matching time window based on the event model. Specifically, an adaptive windowed matching algorithm is proposed to adaptively consider personalized matching time and provide a matching solution with higher service rates at lower latencies. Besides, we maintain the request groups through a mixed graph and further integrate the subsequent arrival requests to optimize the matching results, which can scale to or satisfy online use demands. The extensive experimental results demonstrate the efficiency and effectiveness of our proposed method.