Weighted complex network analysis of travel routes on the Singapore public transportation system

Soh, Harold; Lim, Sonja; Zhang, Tianyou; Fu, Xiuju; Lee, Gary Kee Khoon; Hung, Terence; Pan, Di; Prakasam, Silvester; Wong, Limsoon

doi:10.1016/j.physa.2010.08.015

Cited by 172 publications

(103 citation statements)

References 21 publications

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“…Due to the page limitations, we show the number of arriving and departing passengers at each station of Line 2 in Figure 12. It is obvious that the passenger demands are significantly heterogeneous for different stations and different hours, which is corroborated by previous studies [56,57]. In the numerical experiments, we consider the time window from 10:00 to 12:00 in off-peak hours, during which a total of 16 trains are operated.…”

Section: A Real-world Case Studysupporting

confidence: 59%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

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Section: A Real-world Case Studysupporting

confidence: 59%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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“…Similar work was carried out by [18], who studied the Singapore rail and bus networks using both typological and dynamical (weighted) analyses. They found that using dynamical analysis (weighted) contributes to the network analysis.…”

Section: Methodsmentioning

confidence: 85%

“…1 Network development (state). Note The TA network alternatives are presented on top of original graph from [6][7][8] the findings of [18] in the Singapore network showing different results when using weighted measures and analyzing public transportation systems. In this paper, we used line speed to represent the difference in accessibility between metro and LRT.…”

Section: Discussionmentioning

confidence: 99%

“…The research suggests that larger cities are more connected but have less capacity per capita and longer commute times. Soh et al [18] analyzed the Singapore public transportation network structure using traditional typological and dynamical (weighted) analysis. They showed that the dynamical analysis (weighted) reveals a more complex picture of the network than the topological network analysis.…”

Section: Network Performance Measuresmentioning

confidence: 99%

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Network Analysis of the Tel Aviv Mass Transit Plan

2018

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This paper analyzes the strategic transit network plan for the Tel Aviv metropolitan area, using graph theory and other recently developed transit network measures. The different transit modes included in the strategic plan are emphasized by adding weights to distinguish metro lines from light rail lines (LRT). This approach can help compare the combined metro or LRT alternatives of the new Tel Aviv plan to the metro-only alternatives as well as measure the performance relative to the metro systems in other cities around the world. The analysis of the alternative plans in Tel Aviv showed that when metro and LRT lines were treated as homogeneous modes, in which all were considered as metro, the alternatives resembled medium developed metro systems, such as in Barcelona and Washington DC. In contrast, when the distinguished weights were included, the combined metro/LRT alternatives resembled less developed systems, such as in Lyon and Lisbon, and only the metro-only alterative score remained high. The results also showed that the alternatives have regional coverage, and the alternatives with more LRT lines score lower in coverage. The network structure analysis showed that the metro-oriented networks score higher in both directness and connectivity. When using the weighted measures, the existing plan (LRT-only) scores low on both directness and connectivity. The analysis of the results emphasizes the need for more metro lines in the Tel Aviv metropolitan area. The results also suggest that the analysis of the complex mass transit networks based on graph theory should consider differences in line technology reflected in the line speed and coverage.

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“…Bus networks are the most popular kind of intraurban public transportation networks in many cities, and previous research has strived to understand the statistical mechanics of these networks [1,8]. Exploratory research has led to the discovery of three coordinated properties that characterize the bus networks as complex: (1) the degree distribution of nodes follows a power law or an exponential law, (2) the relationship between the degree of nodes and their relative influence is positive and linear, and (3) bus networks exhibit a high clustering coefficient, suggesting that such networks are 'small-worlds' [1,8,[12][13][14]. Complex networks exhibit an unequal degree distribution, and this distribution forms the basis for dividing the network into smaller clusters.…”

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confidence: 99%

Analysis of community properties and node properties to understand the structure of the bus transport network

Sun

Mburu

Wang

2016

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• We analyze the community structure of the bus network and identify the important nodes in the network.• The intra-urban bus network has a multi-community structure.• The geographic characteristics of communities somewhat reflect the socio-economic division in the city.• The majority of the important nodes (hubs) are clustered in the city center, implying the majority of bus lines are likely to go through the city center. a r t i c l e i n f o b s t r a c tAkin to most infrastructures, intraurban bus networks are large and highly complex. Understanding the composition of such networks requires an intricate decomposition of the network into modules, taking into account the manner in which network links are distributed among the nodes. There exists for each set of highly interlinked nodes little connectivity with the next set of highly interlinked nodes. This inherent property of nodes makes community detection a popular approach for analyzing the structure of complex networks. In this study, we attempt to understand the structure of the intraurban bus network of Ireland's capital city, Dublin in a two-step approach. We first analyze the modular structure of the network by identifying potential communities. Secondly, we assess the prominence of each network node by examining the module-based topological properties of the nodes. Results of this empirical study reveal a clear pattern of independent communities, indicating thus, an implicit multi-community structure of the intraurban bus network. Examination of the geographic characteristics of the identified communities shows a degree of socio-economic divisions of the Dublin city. Furthermore, a large majority of the important nodes (vital transportation hubs) are located at the city center, implying that most of the bus lines in Dublin city tend to intersect the city's core.

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Weighted complex network analysis of travel routes on the Singapore public transportation system

Cited by 172 publications

References 21 publications

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Network Analysis of the Tel Aviv Mass Transit Plan

Analysis of community properties and node properties to understand the structure of the bus transport network

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