As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly ess...As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly essential. This study presents a novel integrated topological-functional(ITF) algorithm for identifying critical nodes, combining topological metrics such as K-shell decomposition, node information entropy, and neighbor overlapping interaction with the functional attributes of passenger flow operations, while also considering the coupling effects between metro and bus networks. Using the Chengdu metro network as a case study, the effectiveness of the algorithm under different conditions is validated.The results indicate significant differences in passenger flow patterns between working and non-working days, leading to varying sets of critical nodes across these scenarios. Moreover, the ITF algorithm demonstrates a marked improvement in the accuracy of critical node identification compared to existing methods. This conclusion is supported by the analysis of changes in the overall network structure and relative global operational efficiency following targeted attacks on the identified critical nodes. The findings provide valuable insight into urban transportation planning, offering theoretical and practical guidance for improving metro network safety and resilience.展开更多
The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous pro...The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.展开更多
This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfac...This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.展开更多
We demonstrate the transmission of directly modulated 10-Gb/s WDM signals over 320 km of negative dispersion fiber (dispersion: -2.5 ps/km/nm @1550 nm) without dispersion compensation. The results indicate that a regi...We demonstrate the transmission of directly modulated 10-Gb/s WDM signals over 320 km of negative dispersion fiber (dispersion: -2.5 ps/km/nm @1550 nm) without dispersion compensation. The results indicate that a regional metro WDM network could be implemented cost-effectively by using the proposed negative dispersion fiber and direct modulated lasers.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 71971150)the Project of Research Center for System Sciences and Enterprise Development (Grant No. Xq16B05)the Fundamental Research Funds for the Central Universities of China (Grant No. SXYPY202313)。
文摘As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly essential. This study presents a novel integrated topological-functional(ITF) algorithm for identifying critical nodes, combining topological metrics such as K-shell decomposition, node information entropy, and neighbor overlapping interaction with the functional attributes of passenger flow operations, while also considering the coupling effects between metro and bus networks. Using the Chengdu metro network as a case study, the effectiveness of the algorithm under different conditions is validated.The results indicate significant differences in passenger flow patterns between working and non-working days, leading to varying sets of critical nodes across these scenarios. Moreover, the ITF algorithm demonstrates a marked improvement in the accuracy of critical node identification compared to existing methods. This conclusion is supported by the analysis of changes in the overall network structure and relative global operational efficiency following targeted attacks on the identified critical nodes. The findings provide valuable insight into urban transportation planning, offering theoretical and practical guidance for improving metro network safety and resilience.
基金sponsored by the National Natural Science Foundation of China(No.72021002)the Fundamental Research Funds for the Central Universities of China.
文摘The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.
文摘This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.
文摘We demonstrate the transmission of directly modulated 10-Gb/s WDM signals over 320 km of negative dispersion fiber (dispersion: -2.5 ps/km/nm @1550 nm) without dispersion compensation. The results indicate that a regional metro WDM network could be implemented cost-effectively by using the proposed negative dispersion fiber and direct modulated lasers.
