As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.

Analysis of the robustness and vulnerability of metro networks has great implications for public transport planning and emergency management, particularly considering passengers’ dynamic behaviors. This paper presents an improved coupled map lattices (CMLs) model based on graph attention networks (GAT) to study the cascading failure process of metro networks. The proposed model is applied to the Shanghai metro network using the automated fare collection (AFC) data, and the passengers’ dynamic behaviors are simulated by GAT. The quantitative cascading failure analysis shows that Shanghai metro network is robust to random attacks, but fragile to intentional attacks. Moreover, there is an approximately normal distribution between instant cascading failure speed and time step and the perturbation in a station which leads to steady state is approximately a constant. The result shows that a station surrounded by other densely distributed stations can trigger cascading failure faster and the cascading failure triggered by low-level accidents will spread in a short time and disappear quickly. This study provides an effective reference for dynamic safety evaluation and emergency management in metro networks.

The trip graph, which can model the residents’ taxi demands, consists of vertices used to indicate trips and edges used to indicate the follow-up relationships between trips. A trip $$v_j$$v j is a tight follow-up trip of another trip $$v_i$$v i , if a taxi can arrive at the departure location of $$v_j$$v j within a time threshold $$\delta$$δ after it finishes $$v_i$$v i . However, for big cities, there are a large amount of trips every day and it is time consuming to construct a trip graph. In this paper, we propose efficient algorithms to construct trip graphs for big cities. When constructing a trip graph, the most expensive step is to connect the vertices if the tight follow-up relationships exist. To find out the tight follow-up trips fast, we design an index considering both spatial and temporal constraints. To designate appropriate search areas, we propose efficient methods to determine the distance-based search areas and the traffic-based search areas. We conduct experiments on real datasets, i.e., the taxi trajectories of Shanghai in 2015. The experimental results show that our algorithm can construct the trip graph about 40 times faster than the straightforward method. We also demonstrate the usages of the trip graph in green transportation applications, i.e., the minimum fleet analysis and the minimum total “idle” mileage analysis.

Good connectivity and accessibility help to enhance the competitiveness of regions and countries. This research provides a detailed analysis of the connectivity and accessibility of the Chinese railway network. The studied period starts in 1949 and ends in 2017. The research scope covers the railway system of the entire country (except Taiwan, Hong Kong, and Macao). Instead of focusing on main cities as research objects, this paper provides more detailed insights by using counties as the basic research units. The analysis shows that the achieved connectivity has been increasing continuously over the study period. Four accessibility indicators (temporal location indicator, weighted average travel time, daily accessibility, and potential indicator) provide comprehensive and complementary results, indicating that the most accessible cities and units are located in the southeastern part of the Hu line. In addition, higher economic level, or higher population density, is correlated with higher accessibility. Furthermore, the current network exhibits an unbalanced spatial distribution pattern, with an underdeveloped west. All the indicators show that the accessibility of the northwest and southwest regions is the lowest. Based on these conclusions, regional policy-making suggestions can be made to guide a rational railway network expansion and facilitate the equality and sustainable economic development of regions. The future railway system development is suggested to focus more on enhancing inner and inter-region communication in the west of China and attach importance to poverty-stricken counties in support of balanced regional growth and development. The railway development of the eastern regions needs to focus on optimizing the structure of the network as well as reasonably organizing railway routes.