A simulation model for estimating train and passenger delays in large-scale rail transit networks

A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that the time varying original-destination demand and passenger path choice probability were given. Passengers were assumed not to change their destinations and travel paths after delay occurs. CapaciW constraints of train and queue rules of alighting and boarding were taken into account. By using the time-driven simulation, the states of passengers, trains and other facilities in the network were updated every time step. The proposed methodology was also tested in a real network, for demonstration. The results reveal that short train delay does not necessarily result in passenger delays, while, on the contrary, some passengers may get benefits from the short delay. However, large initial train delay may result in not only knock-on train and passenger delays along the same line, but also the passenger delays across the entire rail transit network.

Analysis of Transit Network Design Using GIS and Honeybee Algorithm in the City of Sanandaj, Iran

Public transit planning is a user-oriented problem, respectful of financial issues and involves different stakeholders such as the general public, the transportation provider and the local government. One of the main components of public transit planning is the transit network design (TND) problem. This research is an attempt to perform transit network design and analysis in the city of Sanandaj, Iran using the capabilities of GIS and Honeybee algorithm. Objectives of this study are formulating a multi-objective model of the TND problem, developing a GIS-based procedure for solving the TND problem and examination of the solutions using artificial metaheuristic methods such as honeybee algorithm. The transit network design approach in this research, aims to reduce the walking distance, the total travel distance and the total number of stops needed for a suitable transit service in Sanandaj, Iran. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modelling functionalities and using the abilities of the artificial intelligence in modelling and assessment of the transit network.

Transit Network Design Using GIS and Ant Colony Optimization in Sanandaj

The public transit system in Sanandaj has been under review and modification for the last several years. The goal is to reduce the traffic congestion and the share of private car usage in the city and increase the very low share of the public transit. The bus routes in Sanandaj are not connected. There is no connected transit network with the ability to transfer between the routes in locations outside of the downtown terminal. The routes mostly connect the downtown core directly to the peripheries without providing travel options for passengers between peripheries. Although there has been some improvement in the transit system, lack of service in many populated districts of Sanandaj and town nearby makes the transit system unpopular and unreliable. This research is an attempt to provide solutions for the transit network design (TND) problem in Sanandaj using the capabilities of GIS and artificial intelligence methods. GIS offers several tools that enable the decision-makers to investigate the spatial correlations between different features. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modeling functionalities. The visual ability of GIS is used to generate TNDs. Many studies focus on artificial intelligence as the main method to generate the TNDs, but the focus of this research is to combine GIS and artificial intelligence capabilities in order to generate a multi-objective GIS-based procedure to construct different bus network designs and explore and evaluate them to find the suitable transit network alternative.

Transit Network Design:a Hybrid Enhanced Artificial Bee Colony Approach and a Case Study

A bus network design problem in a suburban area of Hong Kong is studied.The objective is to minimize the weighted sum of the number of transfers and the total travel time of passengers by restructuring bus routes and determining new frequencies.A mixed integer optimization model is developed and was solved by a Hybrid Enhanced Artificial Bee Colony algorithm(HEABC).A case study was conducted to investigate the effects of different design parameters,including the total number of bus routes available,the maximum route duration within the study area and the maximum allowable number of bus routes that originated from each terminal.The model and results are useful for improving bus service policies.

Importance Analysis of Urban Rail Transit Network Station Based on Passenger

Current urban rail transit has become a major mode of transportation, and passenger is an important factor of urban rail transport, so this article is based on passenger and the degree of the road network structure, calculating the point intensity of stations of urban rail transit, and then reaching a station importance by integrating many point intensities in a survey cycle time, and getting the station importance of urban rail transit network through concrete examples.

Resilience assessment framework toward interdependent bus-rail transit network:Structure,critical components,and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks(PTNs)is vital for ensuring the resilience and robustness of transportation systems.However,previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs,neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs.Therefore,this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism,structural and functional characteristics of bus-rail transit networks(BRTNs).In this framework,a network performance metric is proposed by considering the passengers’travel behaviors under various disturbances.Additionally,stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork.The proposed framework is validated via a case study of a BRTN in Beijing,China.Results indicate that the rail transit network(RTN)plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN.Furthermore,the coupling interactions between the RTN and bus transit network(BTN)exhibit distinct characteristics under infrastructure component disruption and functional disruption.These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

Control strategy for wayside supercapacitor energy storage system in railway transit network

This paper discusses the control strategy for energy management in railway transit network with wayside(substation)supercapacitor(SC)energy storage system(ESS).Firstly,the structure of the wayside energy storage system is introduced.Secondly,the model of energy storage system is built and the control strategy is described.Thirdly,in order to estimate the required energy storage system,a useful method is proposed to predict the instantaneous regenerative energy magnitude which is delivered to each substation.Finally,the ESS configuration for each substation is determined.A simplified mathematical model of the whole metro network is established and the main features of the control strategy are developed.Numerical simulations show the efficacy of suggested control strategy and the energy saving obtained for railway transit network.

Effects of Reduced Frequency on Network Configuration and Synchronization Transition

Synchronization of networked phase oscillators depends essentially on the correlation between the topological structure of the graph and the dynamical property of the elements. We propose the concept of ＇reduced frequency＇, a measure which can quantify natural frequencies of each pair of oscillators. Then we introduce an evolving network whose linking rules are controlled by its own dynamical property. The simulation results indicate that when the linking probability positively correlates with the reduced frequency, the network undergoes a first-order phase transition. Meanwhile, we discuss the circumstance under which an explosive synchronization can be ignited. The numerical results show that the peculiar butterfly shape correlation between frequencies and degrees of the nodes contributes to an explosive synchronization transition.

Characteristics of phase transitions via intervention in random networks

We present a percolation process in which the classical Erdts-Rtnyi （ER） random evolutionary network is intervened by the product rule （PR） from some moment to. The parameter to is continuously tunable over the real interval [0, 1]. This model becomes the random network under the Achlioptas process at to = 0 and the ER network at to = 1. For the percolation process at to≤1, we introduce a relatively slow-growing point, after which the largest cluster begins growing faster than that in the ER model. A weakly discontinuous transition is generated in the percolation process at to ≤ 0.5. We take the relatively slow-growing point as the lower pseudotransition point and the maximum gap point of the order parameter as the upper pseudotransition point. The critical point can be approximately predicted by each fitting function of the two points about to. This contributes to understanding the rapid mergence of the large clusters at the critical point. The numerical simulations indicate that the lower pseudotransition point and the upper pseudotransition point are equal in the thermodynamic limit. When to 〉 0.5, the percolation processes generate a continuous transition. The scaling analyses of several quantities are presented, including the relatively slow-growing point, the duration of the relatively slow-growing process, as well as the relatively maximum strength between the percolation percolation at to 〈 1 and the ER network about different to. The presented mechanism can be viewed as a two-stage percolation process that has many potential applications in the growth processes of real networks.

Two Network Structure Indicators for Conventional Public Transit

The significance of network structure indicators for the planning and management of conventional public transit is widely acknowledged.In order to improve and enrich the conventional public transit assessment system,two network structure indicators are proposed.Firstly,according to the obvious defects lying in the traditional no-linear coefficient,the realistic no-linear coefficient γRNL,a modified no-linear coefficient indicator,is put forward,which takes into account the effects of barriers in a city.Secondly,to cover the gap of an indicator which can reflect the coverage homogeneity of a transit network,the length dimension LDis proposed on the basis of Fractal Theory.Finally,a case study is applied to verify the validity and practicability of the two indicators in problem diagnosis using regression analysis.The results validate that γRNLcan evaluate the detour of bus lines more reasonably than the previous no-linear coefficient because it reflects the layout of bus lines,and LDcan represent the rate of change of the network density,adding a new member to the scheme of network structure indicators for public transit.

Plasticity-induced characteristic changes of pattern dynamics and the related phase transitions in small-world neuronal networks

Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions to some rhythmic synchronous firing states play significant roles on diverse brain functions and disfunctions, such as encoding rhythmical external stimuli, epileptic seizure, etc. However, in previous studies, phase transitions in neuronal networks are almost driven by network parameters （e.g., external stimuli）, and there has been no investigation about the transitions between typical activity states of neuronal networks in a self-organized way by applying plastic connection weights. In this paper, we discuss phase transitions in electrically coupled and lattice-based small-world neuronal networks （LBSW networks） under spike-timing-dependent plasticity （STDP）. By applying STDP on all electrical synapses, various known and novel phase transitions could emerge in LBSW networks, particularly, the phenomenon of self-organized phase transitions （SOPTs）： repeated transitions between synchronous and asynchronous firing states. We further explore the mechanics generating SOPTs on the basis of synaptic weight dynamics.

Phase Transition in Recovery Process of Complex Networks

The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks： external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally, a largest degree node protection strategy is proposed for improving the robustness of networks.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

Effects of channel noise on synchronization transitions in delayed scale-free network of stochastic Hodgkin–Huxley neurons

We numerically study the effect of the channel noise on the spiking synchronization of a scale-free Hodgkin-Huxley neuron network with time delays. It is found that the time delay can induce synchronization transitions at an intermediate and proper channel noise intensity, and the synchronization transitions become strongest when the channel noise intensity is optimal. The neurons can also exhibit synchronization transitions as the channel noise intensity is varied, and this phenomenon is enhanced at around the time delays that can induce the synchronization transitions. It is also found that the synchronization transitions induced by the channel noise are dependent on the coupling strength and the network average degree, and there is an optimal coupling strength or network average degree with which the synchronization transitions become strongest. These results show that by inducing synchronization transitions, the channel noise has a big regulation effect on the synchronization of the neuronal network. These findings could find potential implications for the information transmission in neural systems.

Phase Transitions of Majority-Vote Model on Modular Networks

We investigate the phase transitions behavior of the majority-vote model with noise on a topology that consists of two coupled random networks. A parameter p is used to measure the degree of modularity, defined as the ratio of intermodular to intramodular connectivity. For the networks of strong modularity （small p）, as the level of noise f increases, the system undergoes successively two transitions at two distinct critical noises, fc1 and fc2. The first transition is a discontinuous jump from a coexistence state of parallel and antiparallel order to a state that only parallel order survives, and the second one is continuous that separates the ordered state from a disordered state. As the network modularity worsens, fc1 becomes smaller and fc1 does not change, such that the antiparallel ordered state will vanish if p is larger than a critical value of pc. We propose a mean-field theory to explain the simulation results.

A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS

In this paper, an artificial neural network model is adopted to study the glass transition temperature of polymers. In our artificial neural networks, the input nodes are the characteristic ratio C-infinity, the average molecular weight M-e between entanglement points and the molecular weight M-mon of repeating unit. The output node is the glass transition temperature T-g, and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting the outcome for polymers for which it is not trained. The maximum relative error for predicting of the glass transition temperature is 3.47%, and the overall average error is only 2.27%. Artificial neural networks may provide some new ideas to investigate other properties of the polymers.

Study on the phase transition of the fractal scale-free networks

Based on the Ising spin, the phase transition on fractal scale-free networks with tree-like skeletons is studied, where the loops are generated by local links. The degree distribution of the tree-like skeleton satisfies the power-law form P（k）～ k^-δ.It is found that when δ≥3, the renormalized scale-free network will have the same degree distribution as the original network. For a special case of δ = 4.5, a ferromagnetic to paramagnetic transition is found and the critical temperature is determined by the box-covering renormalization method. By keeping the structure of the fractal scale-free network constant, the numerical relationship between the critical temperature and the network size is found, which is the form of power law.

2023年世界城市轨道交通运营统计与分析综述

参考国际上较为通用的统计标准,将城市轨道交通分为地铁、轻轨和有轨电车三大类,对世界城轨交通运营现状进行统计。分析表明:截至2023年底,全球有79个国家和地区的563座城市开通了城市轨道交通系统,总里程超过43400.40 km,其中地铁、轻轨、有轨电车分别占50.07%、10.69%和39.24%;中国(含港澳台)累计有66座城市开通运营轨道交通,运营里程达11900.29 km,其中中国内地运营里程11232.65 km。2022年,全球59个国家的183座城市地铁累计运送乘客586.52亿人次,平均负荷强度0.81万人次/(km·d),其中中国(含港澳台)地铁年客流量为212.51亿人次。我国城市轨道交通持续稳步发展,线网规模和客流规模继续居全球第一。统计国务院52号文件发布后的中国轨道交通的线网规模数据,预测低运量城市轨道交通系统将在中国内地有较好的发展前景,特别是中西部地区,以促进城市绿色、可持续发展。同时,根据疫情前后全球主要国家(地区)和城市的客流数据,预计中国内地客流量将在2024—2025年恢复至疫情前水平。

基于数据融合的城市轨道交通大客流预警研究

为了降低因节假日或突发时间导致的客流拥堵,促进客运部门快速应对客流波动,提出了一种基于数据融合的城市轨道交通大客流预警方法。通过对客流参数预测信息进行融合,获得符合实际客流瓶颈点的参数信息,以完成轨道交通大客流预警。提出的方法利用动态贝叶斯网络,实现了历史数据与运输服务信息的融合。以城市假期期间五条线路每日客流量为例,通过对比实验验证了提出方法的有效性与准确性。

考虑站点耦合关系的大型城市轨道交通网络级联失效分析

为了解析站点分布密集的大型城市轨道交通网络级联失效传播机理,提出改进的耦合映射格子模型.该模型结合大型城市轨道交通网络站点分布密集特征,量化耦合站点间客流演化及耦合站点抵抗力.为了衡量级联失效在大型城市轨道交通网络中的传播过程与结果,提出失效站点数量比与网络效率比指标.以上海市城市轨道交通网络为例,验证该模型的可行性.结果表明:当节点度高或进站客流量大的站点失效后,耦合站点间的级联失效规模是邻接站点间的约2倍,且由于耦合站点间的级联失效的影响,介数中心性大的站点失效所引起的级联失效传播速度最快.在考虑耦合站点间级联失效时,客流对大型城市轨道交通网络级联失效的作用力是拓扑结构的约9倍.研究结论可以为城市轨道交通网络的规划设计与事故管控提供科学的建议.