Jie Song

Studying urban amenities is crucial for understanding their impact on quality of life, social equity, and sustainable city development. However, the valuation of urban amenities on a holistic level is understudied. This paper builds upon the framework of isobenefit lines, where residents supposedly receive the same level of combined benefits from all urban amenities. Specifically, our proposed approach takes into account the spatial heterogeneity of amenities and the proxy data of property transaction prices in Singapore to estimate their underlying monetary values. These monetary values were quantified using geographically weighted regression (GWR) that can adequately address spatial autocorrelation issues. Our results show that GWR outperforms the global linear regression model by approximately 150% increase in the R-square value, demonstrating a much better goodness of fit when dealing with spatial data sets. Additionally, the signs of the average coefficients of GWR are largely consistent with those of the global model, while the signs and magnitude of the GWR coefficients vary spatially. The spatial variations of modeling performance tend to intensify from older to younger towns. The results reveal that the central regions of Singapore are among the top spots that receive the highest levels of composite benefits. It is also observed that a spatial structure with multiple centers with high benefit scores emerges in the younger towns located in the peripheral rings of the city. This observation demonstrates the efforts of local authorities to promote a city with several regional centers of diverse functions. As the first study that applies the concept of isobenefit lines in a real urban setting, we demonstrate that the developed framework can be a useful addition to the existing toolbox of urban and infrastructure planners.

During the peak of the COVID-19 outbreak in Wuhan, there were distinct variations in bus control intensity among cities, influenced by factors such as population size and economic scale. In response to these differences, this study collected and compiled bus control strategies from more than 200 cities across the country. Daily data on the existing confirmed cases (confirmed cases per million population) from January 24, 2020, to March 23, 2020, were gathered as research samples. Statistical analysis using the Kruskal-Wallis One-Way ANOVA (k-sample) test was performed in SPSS, followed by pairwise comparisons of the samples. The study involved comparing epidemic data between cities with high control intensity (implementing complete shutdown) and those with lower control intensity (implementing partial shutdown or non- shutdown). The results of the analysis indicated that there were no significant differences in the existing confirmed COVID-19 cases between many pairs of cities. This finding suggests that the control intensity of bus strategy in these cities may be unreasonable. Pairwise comparison heatmaps also revealed a certain pattern, with the decline of the GDP volume, the proportion of the city pairs with no significant difference in COVID-19 diagnosis statistics continues to increase under the same population size, which furthermore proves that bus control strategies of some cities during COVID-19 is unnecessarily tight, especially in economically underdeveloped areas, which need to adapt to local conditions, develop reasonable control strategies based on actual travel needs. Research on public transportation strategies for COVID-19 can offer valuable insights for response strategies, prompting improvements and strengthening of healthcare infrastructure to better address future threats posed by similar infectious diseases.

To achieve efficient and accurate identification of ship encounter behavior information in massive Automatic Identification System (AIS) data, understand and analyze the navigation process of encounter ships, and identify potential safety risk points within ports and waterways. This article takes the AIS data of the main channel area in the Qingdao sea area as the research object, uses Douglas Pucker algorithm and differential algorithm to extract the behavioral feature points of ships overtaking in the main channel, quantitatively analyzes the relationship between behavior change points and overtaking points, understands the behavioral characteristics of ships overtaking process, and provides a clearer and comprehensive understanding of the overtaking process of ships.

Precise bus stops and trajectories are crucial for public transport. However, acquiring accurate information on bus trajectories and stop locations faces the situation of point drift of the global positioning system (GPS), low sampling frequency, and the absence of the latest bus information. Thus, this study developed a method, based on Dumpster Shafer evidence theory and K-Shortest Paths, to update the latest bus stops and routes and automatically remove abandoned bus stops by taking advantage of historical GPS data. To speed up computation, we divided the road network into meshes, dynamically generating a local road network. The effectiveness of the method is validated on Qingdao bus data, and the experiment demonstrates that the method is robust in pinpointing the bus stops and the trajectories of different bus routes.

Currently, historical data and on-site surveys—particularly in the context of China—are heavily relied upon to determine the best time to maintain expressway road markings. This study aims to determine what influences the service life of thermoplastic road markings on expressways in Shandong Province, China, while considering both those motorways’ unique characteristics and the local environment. Additionally, a scientific evaluation of the road markings’ retroreflective coefficient’s decay pattern will be undertaken. We collected the retroreflective data for twelve consecutive months regarding the thermoplastic road markings on five expressways and potential influencing factors such as age of marking and annual average daily traffic. The service life of the markings was forecast using a multiple linear regression. Dominance analysis was used to quantitatively analyze each explanatory factor’s impact on the service life of the markings, and statistically significant variables were also found. Using LightGBM, a machine learning technique, a nonparametric prediction model was also created based on examining the relevance of influencing elements. The modeling results show that LightGBM generates an R² of 0.942, implying that it offers better interpretability and higher accuracy than the regression-based approach. Additionally, LightGBM outperforms MLR according to final validation accuracies, with a score of 95.02% or more than 8% that of MLR. The results are useful for expressway marking upkeep and for driving safety.

This study developed an analytical framework that aims at understanding the evolutionary processes of a micro-mobility system (for example, bike-sharing), which offers insights into the transforming nature of a city transport system. Firstly, the framework applied a Gaussian Mixture Model to examine the long-term fluctuations of travel demands. Secondly, it investigated the growth trajectories of service points via exponential and logistic growth models. Cumulative connections with other points represented the growth of a service location. An eigendecomposition approach was used to uncover the hidden structures behind the growth curves. This framework was applied in the docked bike-sharing program in New York City, USA. The results show that there existed periodic patterns of travel demands in the long term. The majority of stations grew rapidly after they began to operate. However, the temporal signatures of stations’ growth displayed some variations across different locations. This proposed workflow can be employed in other cities with similar context to better investigate how micro-mobility systems evolve.