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.

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.

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.

Impacts of automated passenger cars on the capacity of a freeway basic section: applicability in the determination of vehicle adjustment factors in mixed traffic

The highway capacity manual(HCM)provides a formula to calculate the heavy vehicle adjustment factor(fHV)as a function of passenger car equivalent factors for the heavy vehicle(ET).However,a significant drawback is that the methodology was established solely based on human-driven passenger cars(HDPC)and human-driven heavy vehicles(HDHV).Due to automated passenger cars(APCs),a new adjustment factor(fAV)might be expected.This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on fHV and fAV values.The simulation determined observed adjustment factors at different percentages of HDHVs and APCs(fobserved).The HCM formula was used to calculate(fHCM).Modifications to the HCM formula are proposed,and vehicle adjustment factors due to HDHVs and APCs were calculated(fproposed).Results showed that,in the presence of APCs,while fobserved and fHCM were statistically significantly different,fobserved and fproposed were statistically equal.Hence,this study recommends using the proposed formula when determining vehicle adjustment factors(fproposed)due to HDHVs and APCs in the traffic stream.

Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between trains.Accurate estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity estimation.In the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train dispatching.The averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions.Then,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line.The results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances.Further,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,respectively.Based on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time.However,planned time has positive influences,whereas arrival delay has negative influences.

Abnormal Flight Passenger Recovery Algorithm Based on Itinerary Acceptance

Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, adverse climatic conditions, problems in air traffic control and mechanical failures. In order to reduce losses, it has become a major problem for airlines to use optimization algorithm to study the recovery of abnormal flights. By upgrading the passenger recovery engine, the purpose of this paper is to provide the optimal recovery scheme for passengers, so as to reduce the risk of transferring overseas flights, and thus reduce the economic loss of airlines. In this paper, the optimization model and algorithm based on network flow, combined with actual business requirements, comprehensively consider multiple optimization objectives to quickly generate passenger recovery solutions, and at the same time achieve the optimal income of airlines and the acceptance rate of passenger recovery, so as to balance the two. The practicability and effectiveness of the proposed model and algorithm are proved by some concrete examples.

Last train timetable optimization for metro network to maximize the passenger accessibility over the end-of-service period

Purpose-This study aims to improve the passenger accessibility of passenger demands in the end-ofoperation period.Design/methodology/approach-A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway,the minimum headway and the latest end-of-operation time.The objective of the model is to maximize the number of reachable passengers in the end-of-operation period.A solution method based on a preset train service is proposed,which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.Findings-The results of the case study of Wuhan Metro show that the solution method can obtain highquality solutions in a shorter time;and the shorter the time interval of passenger flow data,the more obvious the advantage of solution speed;after optimization,the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.Originality/value-Existing research results only consider the passengers who take the last train.Compared with previous research,considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination.Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network,but due to the decrease in passenger demand,postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

Technical Feasibility Study of Passenger Rail Service along the West Route between Las Vegas and Los Angeles

The study evaluates the feasibility of running passenger train service from Las Vegas, NV on the Union Pacific Railroad (UPRR), to Barstow, on the Burlington Northern Santa Fe (BNSF) track, to Mojave on UPRR track again, and to Lancaster connecting Metrolink to their destinations in Southern California. In this study, the railroad infrastructure was inventoried and issues related to running the passenger service were identified. Passenger train operation was evaluated based on the Rail Traffic Controller (RTC) simulation model. The performance measures of passenger trains including travel time, overall delay and average speed are analyzed. The uncertainty in freight flow and its impact on providing the passenger service is addressed by conducting a sensitivity analysis. The conclusion is that the existing railroad infrastructure is sufficient to provide a passenger train service from Las Vegas to Los Angeles. From an operational perspective, the passenger train is not expected to influence freight trains’ performance on the existing railroads. When freight train flows are increased to 50%, the influence of passenger train service on the freight operation is still minimal. This study recommends restoring a platform at the Las Vegas Station. At the Mojave Station, special care should be given on running the passenger trains where there is no direct railroad connection from BNSF to UPRR. Platforms and walkways require construction at the Lancaster Station for transferring passengers between the Metrolink trains and X-Train. Transferring the passenger train at this station involves stopping the train on mainline and coordinating the operations between different railroads.

Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.

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.

Estimation of Passenger Car Equivalents for Basic Freeway Sections at Different Traffic Conditions

Passenger car equivalent (PCE) is an important factor which is used to convert traffic volumes containing proportions of heavy good vehicles (HGVs) to a unify measure containing only passenger cars units (PCU). This paper uses large data base of real traffic raw data extracted from loop detector before being aggregated to estimate PCEs. These detectors are located on the M25 and the M42 motorway sites in the United Kingdom. The selected sites represent basic freeway segments as they are far away from the influence of entrance (on ramp) and exit (off ramp) sections. The data are filtered properly so as to estimate passenger car equivalents (PCEs) using lagging headway method for close following situations at different speed ranges. The results suggest that for the same location, the equivalency factors are varies significantly based traffic speed. However, it is proved that such variation with traffic speed is influenced by the differences in lengths between HGVs and cars. Regression models have also been developed linking the PCEs with traffic speed.

Research on Practice of Excellent Railway Passenger Station Construction and High-quality Development

Taking the construction of excellent passenger stations in Beijing-Tianjin-AHebei region as the research object,the paper discusses the implementation of the new concept of building up“well-connected fully-integrated,envioment-friendlyy passenger-oriented,economically-eficient,culturally-rich intelligent and convenient”railway passenger stations It then strives to analyze the connotation of an excellent railway passenger station,the construction and management methods,and the innovation of the working mechanism in the new era.It offers thoughts and methods for promoting the highquality development in the practice of construction for innovative development of excellent passenger stations.

Technical Research and Innovative Practice of Excellent Passenger Stations Design

The key points and strategies of the design of railway passenger stations in the new era are described.The distinctive features,design innovation points and design elements of high-quality projects of large transport hubs are analyzed through dissecting two cases of completed projects,ie Xiong'an Station and Beijing Chaoyang Station.In the light of the general requirements for passenger station construction of China State Railway Group Co.Lid the paper elaborates the development and application trends of railway passenger stations ofthe new erain terms of environmental protection,energy saving,economic efficiency,culture,convenient circulation and intelligence.In addition,new concepts,new technologies and new materials are actively developed and key technologies of the design of iconic excellent passenger stations of the new era are explored and summarized.

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.

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.

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.

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.

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.