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In today's world, creating safe, comfortable, and beautiful slow communities has become an urgent research topic for addressing energy crises, environmental pollution, and traffic congestion. This study explored the relationship between the built environment and residents' leisure-time physical activities in slow communities in Shanghai. This study uses the analytic hierarchy process and entropy weight method to construct a new evaluation model to explore the sustainability mechanism. The results of the study are as follows. First, women participated in walking, jogging, and bicycling activities at a higher rate than men. Second, various leisure-time physical activities have different requirements for the built environment. Third, the built environment of slow communities in Shanghai shows a "pyramid" type of spatial stratification phenomenon. This study contributes to a new evaluation system and optimization model for promoting leisure-time physical activities, providing theoretical and methodological guidance for constructing livable slow communities in developing countries and promoting slow living. [ABSTRACT FROM AUTHOR]

The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Public transport plays an essential role in helping people escape from the congested traffic in large and crowded cities. Evaluating customer satisfaction is the key to the continuous quality improvement of public transport services. This work proposes a new public transport customer satisfaction evaluation approach based on an extended thermodynamic method with q-rung orthopair fuzzy sets. First, we use the q-ROFSs to handle the ambiguity and uncertainty of customer satisfaction evaluation information for public transport. Then, we extend the thermodynamic method to determine the customer satisfaction levels of public transport lines. Moreover, the stepwise weight assessment ratio analysis method is utilized to specify the weights of evaluation criteria as it is simple and time-saving. The effectiveness of the proposed approach is illustrated with a customer satisfaction evaluation of the rail transit network in Shanghai, China. Results showed that Line 7 has the highest customer satisfaction and Line 2 has the lowest customer satisfaction among the considered rail transit lines. Besides, nine evaluation criteria need to be optimized to improve the customer satisfaction level of Line 2 and the most critical one is train crowding. [ABSTRACT FROM AUTHOR]

Along with the rapid development of the transportation industry, the problems of the energy crisis and transport emissions have become increasingly serious. The success of traffic emission reduction is related to the realization of global low-carbon goals. Placing priority on public transport is the internationally recognized traffic development model. This paper takes Shanghai, China, as an example to examine the optimal public transport structure. Five factors were selected from personal and public perspectives, including travel costs, crowding degree, occupied area, traffic emissions, and operating subsidies. The objective functions of these factors were transformed into satisfaction functions, and a multi-objective programming model was used to solve for the optimal proportions of the ground bus and rail transit, and the carbon emission reduction potential was analyzed in different scenarios. The study showed that the actual proportion of rail transit in Shanghai was slightly lower than the optimal value, and accompanied by low satisfaction with each factor relative to the optimal value. It was difficult to achieve the traffic emission reduction targets by only reducing satisfaction with other factors except carbon emissions assuming a fixed proportion of public transport. As the proportion of total travel represented by public transport increased, rail transit became the main mode of public transport and the usage trend was more obvious, but the structure of public transport gradually reached a relatively stable state after a certain level of development. Compared to reducing carbon emissions by changing satisfaction with other factors, it was easier to achieve traffic emission reduction targets by increasing the proportion of public transport. To increase the proportion of public transport travel and achieve the goal of traffic reduction in the future, further improvements are needed in the quality of public transport system services, public transport priority development must be differentiated, and the profitability of the public transport industry itself must be enhanced. [ABSTRACT FROM AUTHOR]