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Register a new userEffectiveness of Rapid Rail Transit System in Beijing
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The effectiveness of rapid rail transit system is analyzed using tools of complex network for the first time. We evaluated the effectiveness of the system in Beijing quantitatively from different perspectives, including descriptive statistics analysis, bridging property, centrality property, ability of connecting different part of the system and ability of disease spreading. The results showed that the public transport of Beijing does benefit from the rapid rail transit lines, but there is still room to improve. The paper concluded with some policy suggestions regarding how to promote the system. This study offered significant insight that can help understand the public transportation better. The methodology can be easily applied to analyze other urban public systems, such as electricity grid, water system, to develop more livable cities.
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We study the effectiveness of using multiple phases for maximizing the extent of information diffusion through a social network, and present insights while considering various aspects. In particular, we focus on the independent cascade model with the possibility of adaptively selecting seed nodes in multiple phases based on the observed diffusion in preceding phases, and conduct a detailed simulation study on real-world network datasets and various values of seeding budgets. We first present a negative result that more phases do not guarantee a better spread, however the adaptability advantage of more phases generally leads to a better spread in practice, as observed on real-world datasets. We study how diffusing in multiple phases affects the mean and standard deviation of the distribution representing the extent of diffusion. We then study how the number of phases impacts the effectiveness of multiphase diffusion, how the diffusion progresses phase-by-phase, and what is an optimal way to split the total seeding budget across phases. Our experiments suggest a significant gain when we move from single phase to two phases, and an appreciable gain when we further move to three phases, but the marginal gain thereafter is usually not very significant. Our main conclusion is that, given the number of phases, an optimal way to split the budget across phases is such that the number of nodes influenced in each phase is almost the same.
The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.
Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk.
We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks.
This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system. We employ a macroscopic train operation sub-model developed by Seo et al. (2017a,b) to express the interaction between dynamics of passengers and trains in a simplified manner while maintaining their essential physical relations. We derive the equilibrium conditions of the proposed model and discuss the existence of equilibrium. The characteristics of the equilibrium are then examined through numerical examples under different passenger demand settings. As an application of the proposed model, we finally analyze a simple time-dependent timetable optimization problem with equilibrium constraints and show that there exists a capacity increasing paradox in which a higher dispatch frequency can increase the equilibrium cost. Further insights into the design of the timetable and its influence on passengers equilibrium travel costs are also obtained.
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