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Metro network connectivity is crucial for ensuring reliable operation of metro systems. Despite the rich literature on the connectivity analysis of transportation network, very little attention has been paid to passengers’ heterogeneous cognition toward congestion and connectivity incorporating subjective judgment. In this paper, we develop a data-driven framework to analyze metro network connectivity evolution involving individual cognition by characterizing it as a transit percolation process. The concept of individual tolerance index of congestion and a measure named network friendliness are proposed. By comparing individual tolerance index and friendliness of metro network, metro network connectivity with regard to different passengers can be depicted quantitatively. The evolution of network connectivity can be monitored both as individual tolerance changes and as time goes on. We also demonstrate how global transit breaks down when the identified bottlenecks are congested from the perspective of the passengers’ cognition. The proposed method is validated using a real-world case of the Shenzhen Metro in China. Results show that the proposed method is effective in capturing the dynamic evolution of the Shenzhen metro network connectivity and enable effective identification of transit bottlenecks. The network connectivity and friendliness are found to be significantly increased through a small improvement of the bottlenecks pinpointed.

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method. [ABSTRACT FROM AUTHOR]

The bottleneck (BN) of a production system is a machine with the strongest effect on the system's throughput. In this paper, a method for BN identification in serial lines with rework and Bernoulli machines is developed. The method can be applied using either calculated or measured data on blockages and starvations of the machines. For the case of calculated data, a technique for evaluating performance measures of Bernoulli lines with rework is developed. Along with these quantitative contributions, the paper provides three qualitative results. First, it shows that Bernoulli lines with rework do not observe the property of reversibility. Second, it demonstrates that downstream machines may have a larger effect on the throughput than upstream ones. Third, it demonstrates that BNs may be shifting not only because of changes in machine and buffer parameters but also due to changes in quality of parts produced. [ABSTRACT FROM AUTHOR]

In closed production lines, each part is placed on a carrier at the input of the first machine and is removed from the carrier at the output of the last machine. The first machine is starved if no carriers are available, and the last machine is blocked if the empty carrier buffer is full. The number of carriers in the system is S and the capacity of the empty carrier buffer is No. Under the assumption that the machines obey the Bernoulli reliability model, this paper provides methods for determining if a pair (No, S) impedes the open line performance and, if it does, develops techniques for improvement with respect to S and No. In addition, bottlenecks in closed lines are discussed, and an approach to selecting the smallest No and S, which result in no impediment, is described. Note to Practitioners--In closed production lines, parts are transported throughout the system on pallets. Clearly, this could impede the system performance since the first operation may be starved for pallets and the last may be blocked by full empty pallets buffer. Therefore, it is important to establish conditions on the number of pallets and the capacity of the empty pallets buffer, under which the impediment does not take place. This paper provides such conditions under the assumption that the operations obey a simple (Bernoulli) reliability model. In addition, this paper provides a method for identification of bottlenecks in closed lines. Future work will extend these results to systems with exponential and non-Markovian reliability models. [ABSTRACT FROM AUTHOR]