Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

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.

A panel data model to predict airline passenger volume

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans.This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume.First,potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects.Second,the principal component analysis(PCA)is applied to identify key factors that impact the airline passenger volume of city pairs.Then the panel data model is estimated using 120 sets of data,which are a collection of observations for multiple subjects at multiple instances.Finally,the airline data from Chongqing to Shanghai,from 2003 to 2012,was used as a test case to verify the validity of the prediction model.Results show that railway and highway transportation assumed a certain proportion of passenger volumes,and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume.According to the validity test results,the prediction accuracies of the model for 10 sets of data are all greater than 90%.The model performs better than a multivariate regression model,thus assisting airport operators decide which routes to adjust and which new routes to introduce.

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

Prediction of Railway Passenger Traffic Volume

The current situation of the railway passenger traffic (RPT) and the traffic marketing is analyzed. The grey model theory is adopted to establish a prediction model for the railway passenger traffic volume (RPTV).The RPTV from 2001 to 2005 is predicted with the proposed model, and a few suggestions are put forward.

Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.

Revenue management method and critical techniques of railway passenger transport

Purpose–Revenue management(RM)is a significant technique to improve revenue with limited resources.With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China,it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.Design/methodology/approach–This paper proposes the theory and framework of generalized RM of railway passenger transport(RMRPT),and the thoughts and methods of the main techniques in RMRPT,involving demand forecasting,line planning,inventory control,pricing strategies and information systems,are all studied and elaborated.The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT.The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.Findings–The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.Originality/value–The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

以乘用车标签开发浅析任务驱动的共用件知识管理模型应用

为了满足汽车行业技术革新和市场需求,以提升乘用车共用件的开发效率和资源利用为目标,探索知识分析管理模型在乘用车标签领域的应用。通过构建知识复用库和验证模型,提出了一种基于任务驱动的共用件知识管理新策略,知识分析管理模型为乘用车共用件的高效开发提供了一种创新方法。结果表明,该模型能有效促进知识共享、缩短开发周期、降低成本、提升企业竞争力。

基于区块链的民航旅客隐私信息保护方案

目前的民航信息系统必须获取机票和旅客明文信息,才能进行出行验证,存在很高的旅客隐私信息泄露风险。针对此问题,提出一种基于区块链的民航旅客隐私信息保护方案。首先利用网络身份标识和数字证书技术实现区块链账户的实名制注册,确保信息的真实性;其次采用零知识证明和环签名叠加构造安全的信息认证协议,实现旅客隐私信息完全匿名验证,确保真实信息匿名可验证性;进一步对存储在区块链上的敏感信息利用K-匿名技术脱敏,加强信息的安全存储。实验结果和性能分析表明,该方案能够提供安全有效的民航旅客隐私信息保护,且必要的信息验证总耗时满足效率需求。

Evacuated tube transport technologies (ET3)^(tm):a maximum value global transportation network for passengers and cargo

Evacuated Tube Transport Technologies （ET3） offers the potential for more than an order of magnitude improvement in transportation efficiency, speed, cost, and effectiveness. An ET3 network may be optimized to sustainably displace most global transportation by car, ship, truck, train, and jet aircraft. To do this, ET3 standards should adhere to certain key principals： maximum value through efficiency, reliability, and simplicity; equal consideration for passenger and cargo loads; optimum size; high speed/high frequency operation; demand oriented; random accessibility; scalability; high granularity; automated control; full speed passive switching; open standards of implementation; and maximum use of existing capacities, materials, and processes.

Metro passenger flow control with station-to-station cooperation based on stop-skipping and boarding limiting

Metro passenger flow control problem is studied under given total inbound demand in this work,which considers passenger demand control and train capacity supply.Relevant connotations are analyzed and a mathematical model is developed.The decision variables are boarding limiting and stop-skipping strategies and the objective is the maximal passenger profit.And a passenger original station choice model based on utility theory is built to modify the inbound passenger distribution among stations.Algorithm of metro passenger flow control scheme is designed,where two key technologies of stopping-station choice and headway adjustment are given and boarding limiting and train stopping-station scheme are optimized.Finally,a real case of Beijing metro is taken for example to verify validity.The results show that in the three scenarios with different ratios of normal trains to stop-skipping trains,the total limited passenger volume is the smallest and the systematic profit is the largest in scenario 3.

Spatial Interaction and Network Structure Evolvement of Cities in Terms of China's Rail Passenger Flows

Cities separated in space are connected together by spatial interaction (SI) between them. But the studies focusing on the SI are relatively few in China mainly because of the scarcity of data. This paper deals with the SI in terms of rail passenger flows, which is an important aspect of the network structure of urban agglomeration. By using a data set consisting of rail O-D (origin-destination) passenger flows among nearly 200 cities, intercity rail distance O-D matrixes, and some other indices, it is found that the attenuating tendency of rail passenger is obvious. And by the analysis on dominant flows and spatial structure of flows, we find that passenger flows have a trend of polarizing to hubs while the linkages between hubs upgrade. However, the gravity model reveals an overall picture of convergence process over time which is not in our expectation of integration process in the framework of globalization and economic integration. Some driven factors for the re-organization process of the structure of urban agglomeration, such as technique advance, globalization, etc. are discussed further based on the results we obtained.

NUMERICAL ANALYSIS OF NONLINEAR STABILITY FOR RAILWAY PASSENGER CARS

The Newton Raphson iteration and QR algorithm are combined to search the Hpf bifurcation point of the vehicle running on straight track and on large radius curved tracks. Limit cycles that are bifurcated from the equilibrium points and the saddle node bifurcation point are computed through employing a variable step Runge Kutta method and the Poincaré map. Finally, numerical simulations are carried out for the stability of a high speed passenger car operating on straight and large radius curved tracks. The influences of the radius of curvature and the superelevation of the track on the stability of the vehicle system are investigated.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

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.

CO2 emission of urban passenger transportation in China from 2000 to 2014

This study reviewed the urban passenger transportation(UPT)development of seven typical cities in China from 2000 to 2014,estimated the UPT CO2emission,analyzed the structure,and discussed the main factors of UPT CO,emission.Results showed that increases of GDP,population,and UPT scale of the cities have speeded up.The most significant development of UPT is that the growth of private vehicles is greatly faster than that of public transportation.The total and per-capita UPT CO2 emissions both increased.The share of private vehicles emission to total UPT CO2emission has increased,with the share in range of 65%-88%in 2014,exponentially leading to the increases of total and per-capita UPT CO2 emission.Although UPT CO2 emission structure with more share of public transportation would slow down the UPT CO2emission increase,private vehicle CO2 emission is recognized as the dominated driving factor.Contributions of driving factors,such as GDP,population,private vehicle CO2 emissions,to UPT CO2 emission are different among the cities.Private vehicle CO2 emission.is the dominated factor for UPT CO2emission in Beijing and Taiyuan.Besides private vehicle CO2emission,GDP also plays an important role in UPT CO2emissions of Chengdu,Shanghai,Guangzhou,and Urumqi.Contributions of private vehicle CO2 emission and GDP to UPT CO2 emission are almost same in Xi'an.

Modeling and simulation of high-speed passenger train movements in the rail line

In this paper, we propose a new formula of the real-time minimum safety headway based on the relative velocity of consecutive trains and present a dynamic model of high-speed passenger train movements in the rail line based on the proposed formula of the minimum safety headway. Moreover, we provide the control strategies of the high-speed passenger train operations based on the proposed formula of the real-time minimum safety headway and the dynamic model of highspeed passenger train movements. The simulation results demonstrate that the proposed control strategies of the passenger train operations can greatly reduce the delay propagation in the high-speed rail line when a random delay occurs.

Research on a Multi-grid Model for Passenger Evacuation in Ships

In order to enhance the authenticity and accuracy of passenger evacuation simulation in ships, a new multi-grid model was proposed on the basis of a traditional cellular automata model. In the new model finer lattices were used, interaction of force among pedestrians or between pedestrians and constructions was considered, and static floor fields in a multi-level exit environment were simplified into cabin and exit static floor fields. Compared with the traditional cellular automata model, the multi-grid model enhanced the continuity of the passengers'track and the precision of the boundary qualifications. The functions of the dislocation distribution of passengers as well as partial overlap of tracks due to congestion were realized. Furthermore, taking the typical cabin environment as an example, the two models were used to analyze passenger evacuation under the same conditions. It was found that the laws of passenger evacuation simulated by the two models are similar, while the simulation's authenticity and accuracy are enhanced by the multi-grid model.

Research on Railway Passenger Flow Prediction Method Based on GA Improved BP Neural Network

This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability.

Explicit–Implicit Co-Simulation Techniques for Dynamic Responses of a Passenger Car on Arbitrary Road Surfaces

To study the durability of a passenger car, this work investigates numerical simulation techniques. The investigations are based on an explicit implicit approach in which substructure techniques are used to reduce the simulation time, allowing full vehicle dynamic analyses to be performed on a timescale that is dif cult or impossible with the conventional nite element model (FEM). The model used here includes all necessary nonlinearities in order to maintain accuracy. All key components of the car structure are modeled with deformable materials. Tire road interactions are modeled in the explicit package with contact-impact interfaces with arbitrary frictional and geometric properties. Key parameters of the responses of the car driven on six different kinds of test road surfaces are examined and compared with experimental values. It can be concluded that the explicit implicit co-simulation techniques used here are ef cient and accurate enough for engineering purposes. This paper also discusses the limitations of the proposed method and outlines possible improvements for future work.

基于麻雀搜索算法和长短期记忆神经网络的轨道交通站点客流预测

准确的短时客流预测可以为城市轨道交通的良好运营提供保障,但轨道交通的短时客流具有非线性和高随机性等特点,为了提高对短时客流的预测精度,提出将ISSA算法和LSTM模型进行组合,构建城市轨道交通短时客流预测模型.针对SSA算法收敛速度慢,容易陷入局部最优解的问题,引入黄金莱维飞行策略,通过动态调整探索者移动步长的方法,使得它在未知范围内搜索时,能够覆盖更大的范围,提高SSA算法全局搜索的能力.通过使用ISSA算法对LSTM模型的隐含层、学习率和迭代次数的神经元个数进行优化,构建ISSA-LSTM组合预测模型,用于城市轨道交通短时客流的预测.将该模型与BP、LSTM和SSA-LSTM等3种短时客流预测模型进行对比,结果表明:在针对工作日和非工作日客流的预测中,ISSA-LSTM模型预测误差最小,具有较好的预测效果.