4 results found
Shang W, Han K, Ochieng W, et al., 2016, Agent-based day-to-day traffic network model with information percolation, Transportmetrica A-Transport Science, Vol: 13, Pages: 38-66, ISSN: 2324-9935
This paper explores the impact of travel information sharing on road networks using a two-layer, agent-based, day-to-day traffic network model. The first layer (cyber layer) represents a conceptual communication network where travel information is shared among drivers. The second layer (physical layer) captures the day-to-day evolution in a traffic network where individual drivers seek to minimize their own travel costs by making route choices. A key hypothesis in this model is that instead of having perfect information, the drivers form individual groups, among which travel information is shared and utilized for routing decisions. The formation of groups occurs in the cyber layer according to the notion of percolation, which describes the formation of connected clusters (groups) in a random graph. We apply the novel notion of percolation to capture the disaggregated and distributed nature of travel information sharing. We present a numerical study on the convergence of the transport network, when a range of percolation rates are considered. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis. A sensitivity analysis is also presented which shows a bifurcation phenomenon with regard to certain model parameters.
Shang W, Han K, Ochieng W, 2015, An agent-based day-to-day traffic evolution model using percolation theory, Transportation Research Board 95th Annual Meeting, Publisher: Transportation Research Board
Pien KC, Han K, Shang WL, et al., 2015, Robustness Analysis of the European Air Traffic Network, Transportmetrica A: Transport Science, Vol: 11, Pages: 772-792, ISSN: 2324-9943
The European air traffic network (ATN), consisting of a set of airports and area control cen- tres, is highly complex. The current indicator of its performance, air traffic flow management delays, is insufficient for planning and management purposes. Topological analysis of ATNs of this kind has highlighted betweenness centrality (BC) as an indicator of network robustness, although such an indicator assumes no knowledge of actual traffic flows and the network’s operational characteristics. This paper conducts topological and operational analyses of the European ATN in order to derive a more relevant and appropriate indicator of robustness. By applying a flow maximisation model to the network influenced by a range of capacity reductions at the local level, we propose a new index called the Relative Area Index (RAI). The RAI quantifies the importance of an individual node relevant to the performance of the entire network when it suffers from capacity reduction at a local scale. Air traffic data from three typical busy days in Europe are utilised to show that the RAI is more flexible and capable than BC in capturing the network impact of local capacity degradation. This index can be used to assess network robustness and provide a valuable tool for airspace managers and planners.
Shang W, Pien KC, Han K, et al., 2015, Robustness and Topology Analysis of European Air Traffic Network Using Complex Network Theory, The 94th Transportation Research Board Annual Meeting
This paper explores topological and operational features of the European Air Traffic Network (EATN), which consists of airports and Area Control Centres (ACCs), with the goal of analysing and quantifying network robustness. The EATN is regarded as a directed, weighted and asymmetric graph, where various metrics, including betweenness, are studied and analysed statistically. On the operational side, the EATN is recognized as a flow-bearing and capacitated air transport network with established origins and destinations as well as flight routes. We propose a Relative Area Index (RAI), combined with network flow maximization techniques, to capture and quantify the degradation in the network throughput in the event of local disruptions on different levels-a concept frequently referred to as robustness in the literature. Our findings show that the RAI follows the power law distribution. Moreover, it is not correlated with betweenness centrality, which is a popular index used to infer the robustness of topological networks. We show that topological inferences such as betweenness are insufficient to capture network robustness in the real world, and that a class of new indices that reflect the operational constraints, flow patterns and behavioural assumptions should be developed to assess the robustness of air traffic networks.
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