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Female Sex Workers (FSWs) are a high-risk group that not only are susceptible to, but also may contribute to the spread of, HIV/AIDS. How to effectively intervene the FSW population for HIV/AIDS prevention has been a public concern. In this paper, relied on a set of interview data of FSWs collected from a city in Yunnan Province, China, we first construct a social network based on if FSWs have been acquaint. We find that the network has the characteristics of “small world” with a small average shortest path and a big clustering coefficient. The network also shows a “small community” phenomenon with a low average density. In other words, individuals are close to each other in community, and their external connection is sparse. The degree of the FSW social network obeys an exponential distribution, indicating that few numbers of nodes have a large centrality (i.e. the important individuals). In addition, those nodes that is more passive in HIV/AIDS prevention have a higher level of clustering, but a lower similarity with their neighbors. Finally, we use individuals’ responses to the AIDS KAP questionnaire to predict one’s position (or status) in the social network, as the social network structure is invisible, observable individual responses to the questionnaire is easier to obtain. We then propose an efficient method to choose the nodes based on individuals’ responses to AIDS KAP questionnaire for intervention and network clogging. We found that, compared with a random node selection method, our method is more effective.

Bicycle-sharing is an eco-friendly transportation operating model in the context of “Internet Plus” and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents’ bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents’ demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future.

Due to the urban industrial transformation and upgrading in China, the relationship between residential and employment spaces in large cities has changed significantly, resulting in an increasingly prominent job–housing separation phenomenon. Based on the taxi GPS trajectory data, this paper firstly analyzes the time and space distribution of job–housing–travel and the commuting characteristics of residents. Then, we construct the evaluation index systems of regional jobs–housing–taxi demand and regional social development level for Beijing. After calculating the weight of each index by using an entropy weight method, we analyze the coupling relationship between regional taxi demand and social development by building the coupling coordination degree model (CCDM). Conclusions are as follows: Firstly, it shows imbalance of space distribution of taxi orders in different administrative and functional areas. The order quantity gradually decreases from the main urban area to the edge, with the most in the living area, accounting for 46.23% of the total orders. Secondly, the daily variation trend of taxi orders in different functional areas is significantly different. During the morning rush hour, taxi orders flow out of residential areas and into office areas; however, during the evening rush hour, they flow out of the office and into the living area. For example, the residence area has a higher proportion of getting on passengers during the morning rush hour, which is 1.46 times that of the getting off ones. Thirdly, a low coupling coordination degree is observed between the regional social development level and the job–housing–taxi demand in Beijing, with a gradually decreasing from the main urban area to the outside, which is mainly caused by the imbalance between supply and demand. Therefore, it is suggested that the relevant government departments should formulate specific control schemes to traffic congestion according to local commuting features, and promote job–housing balance. Meanwhile, the taxi resources are supposed to be allocated reasonably by the striving to information symmetry, to promote the coordination and sustainability between the regional job–housing–taxi demand and social development level.

With the rapid development of the urban economy and transportation, the taxi market has presented a series of problems regarding unreasonable resource allocation, demand–supply imbalance, and driver income imbalance. Since the distribution of high-quality customers (hereafter HQC) affects drivers’ incomes and resource allocation in the taxi market, it is of great significance to study the HQC distribution to address these imbalance problems and understand the imbalance of the taxi market. In this paper, we calculate the profit margin of each order and construct a high-quality customer evaluation model from taxi GPS trajectory data. In analyzing the spatial–temporal distribution of HQC, our results indicate that HQC present a regional aggregation phenomenon in the spatial dimension, and HQC are mainly distributed in the main urban areas. The distribution of HQC is imbalanced in various administrative districts and functional zones. The daily change of orders in each administrative district is the same, but the temporal distribution of HQC is imbalanced. The temporal distribution of HQC in each functional zone is imbalanced, showing different daily change trend. According to these results, we suggest that the pricing of taxis and online ride-hailing services be coordinated based on the spatial–temporal distribution characteristics of HQC and that resources be rationally allocated to promote the sustainable and healthy development of urban transportation.

With the development of social economy, a series of problems such as an imbalance of supply and demand, unreasonable resource allocation and low operation efficiency appear in the urban taxi market. Based on the GPS trajectory data of taxis, this paper calculates and analyzes the operation efficiency of taxis in terms of time and space dimensions in Chengdu, Sichuan Province by using the capacity utilization rate. The results show that, firstly, the weekly variation trend of taxi capacity utilization rate is obviously periodic and its daily variation trend is unbalanced, which is reflected in the morning peak, afternoon peak, evening peak and night peak. The daily variation trend of taxi capacity utilization rate is significantly different between workdays and weekends, and the capacity utilization rate on weekends is much lower. Secondly, there is a spatial imbalance in the distribution of taxi capacity utilization rate. The capacity utilization rate of the main urban district is relatively high, and that of the other urban districts negatively correlates with the economic level and population density. The supply and demand of taxis are balanced within the various functional zones but unbalanced between different functional zones. It is suggested that the relevant departments shall reasonably allocate taxi resources according to the imbalance of taxi capacity utilization rate in time and space to improve the overall operation efficiency, and integrate market mechanism with government regulations to promote the sustainable development of urban taxis.