Attributes Of Passengers Research Articles

A model for aircraft cabin evacuation considering passenger type

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft's unique characteristics, such as the diversity of passengers and the restricted evacuation environment. It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints. To fill this gap, an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes. Passengers are divided into healthy individual passengers and disabled-healthy group passengers, whose movement mechanisms are quantified. Based on the constructed model, simulation experiments are conducted using the configuration cabin layout of B737-800 as an example. The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers. Moreover, the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers' seats are close to the aisle or window, and the evacuation efficiency is best when their seats are evenly distributed in the cabin. The evacuation time is the shortest when all cabin doors are open, and pedestrians are evacuated the slowest when the central emergency doors are closed. This study provides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

Research on Choice Preference of Parallel Trains for High-Speed Rail Heterogeneous Passengers

The passenger capacity utilization of parallel trains on China’s high-speed railway (HSR) varies greatly, which seriously affects the revenue and passenger service quality of railroad companies. One of the key factors contributing to this issue is the uniformity of fares. To address this, China’s railway enterprises are implementing differential pricing reforms for parallel trains. This paper presents a two-step method that combines latent class analysis (LCA) and random parameter logit (RPL) models to estimate the preferences of diverse passengers for parallel trains and quantify them using willingness to pay. To illustrate this, we conducted revealed preference and stated preference questionnaire surveys at Nanning East Railway Station, China, focusing on the Nanning-Guangzhou HSR route. These surveys gathered data on passengers’ personal and travel attributes. LCA was carried out according to passenger attributes, and passengers were divided into three categories according to the goodness-of-fit measures. Through the questionnaire data, the travel time and departure time period of each category of the passenger were converted into costs, and the absolute value of each influencing factor of passengers for parallel trains was obtained. Considering the limited rational choice behavior of passengers, the perceived value of each influencing factor of passengers on parallel trains was calculated through prospect theory. Finally, an RPL model for parallel train choice of heterogeneous passengers was constructed, and the parameters of each category of the passenger were estimated based on the absolute value and perceived value of the influencing factors.

Parallel and Collaborative Passenger Flow Control of Urban Rail Transit Under Comprehensive Emergency Situation

The artificial cooperative control methods are used to fully utilize the train capacity and reduce the passenger delays by coordinating the number of entering and boarding passengers at each station, but it ignores the potential risk associated with the aggregation attributes of passengers in the station under emergency and the unfairness caused by the imbalance of travel opportunities for different origin–destination (OD) passengers. A new parallel and cooperative control model under comprehensive emergency situation is constructed from the perspective of guaranteeing travel safety, improving travel fairness, and increasing travel efficiency to generate passenger flow control strategies. The model aims to maximize passenger turnovers, minimize passenger delays and reduce the difference in the ratio of stranded passengers at stations. The complex multi-objective integer programming problem involving nonlinear equation constraints is difficult to solve numerically. A dynamic programming model taking train sequence as stage variable is then constructed as well as an NSGA-II algorithm with multiple improved strategies (MIS-NSGA-II) is proposed to solve the model. Numerical experiments on a line of Beijing Rail Transit show that the MIS-NSGA-II outperforms the traditional NSGA-II, and that increasing the overload coefficient of the train and the tolerance coefficient of each area in the station can further alleviate passenger congestion and promote the operation efficiency. Compared with other control strategies mentioned in this study, the proposed control model from a comprehensively perspective, which can ensure the travel safety and promote the travel efficiency while forcefully decongesting the bottleneck areas and improving the fairness of passenger travel.

Fuzzy-theory-based pedestrian dynamics models for studying the waiting passenger distribution at the subway platform

The behavior of waiting area choice of passengers at the subway platform can directly affect the travel efficiency, thereby affecting the service level of the station. Combining fuzzy logic theory and cellular automata model/social force model, a waiting area choice model for passengers at the subway platform is proposed, which can be used to predict the distribution of passengers. The distance from passengers to waiting areas, the number of passengers in the waiting area, and the length of the remaining waiting area are selected as the main influence factors after the analysis of field investigation, which are defined as the input variables of the fuzzy logic system. The choice probability of passengers to each waiting area is defined as the output variable. The waiting area with the highest probability is taken as the destination of passengers, thereby the static field value in the cellular automata model and the desired direction in the social force model can be determined. The total error of the established method can be as low as 10.13%, which shows a relatively good prediction effect. The effects of passenger attributes, stair attributes, and passable area size on the waiting area choice behavior are simulated. The results show that the influence degree of passenger companion behavior on passenger distribution depends much on the proportion of passengers in the company. The body radius and the desired speed of passengers have a great impact on the distribution balance of waiting passengers based on the analysis of the Gini coefficient and random forest method. The interval of multiple stairs should be the same as far as possible. The setting of a passable area is conducive to the flow of passengers during peak hours. The number of passengers is most pronounced in the distribution balance of passengers by comparing five different influence factors. Guidance strategies can be released through the LED display screen on the basis of the predicted distribution information. This paper provides a new idea for modeling the waiting area choice behavior of passengers, which lays a theoretical basis for the design and transformation of platform facilities.

Value Assessment of Airport Billboards Based on Passenger Big Data.

As an important element of the airport ecosystem, airport billboards are playing a crucial role in publicizing the city image and facilitating humanistic airport construction. At the same time, airport billboards have great commercial value and is a popular channel for enterprises to prompt their products and to build their brand image. Currently, most airports in China adopt a simple fixed pricing mechanism for airport billboards. Specifically, for any type of billboard, the advertising price is mainly determined by considering historical prices and the total passenger flow of the entire airport during a whole year. However, this seemingly crude pricing mechanism only considers macro-level data of passenger flow and fails in reflecting the real value of billboards in different locations effectively, since the value of a particular billboard depends not only on its media form, but also on the number of passengers flowing through and whether these passengers are the target customers of the advertising content. Based on big data on airport layout, flight information, and passenger attributes, this paper proposes a time- and location-based value assessment model for airport billboards. Using sample data collected from the Beijing Capital International Airport, the assessment model is adopted to evaluate the value of two real billboards in Terminal T3. Application of this model can reflect the difference in the value of airport billboards located in different spots during various periods. Furthermore, this model provides a solid foundation for airport executives to develop differentiated/dynamic pricing and flexible advertisement scheduling strategies, thereby improving the overall efficiency.

Evaluating machine learning methodologies for identification of cancer driver genes

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

How Many Facilities are Needed? Evaluating Configurations of Subway Security Check Systems via a Hybrid Queueing Model

Subway security check systems (SSCS), which play an important role for smooth operation of subways of metropolises, exhibit a high complexity and randomness due to their multiple queues, mixed services, and heterogeneous passengers. Traditional approaches for evaluating configurations of SSCS either assume a single-queueing model or ignore various attributes of passengers for the sake of simplicity of models, leading to unsatisfactory results. In this paper, we propose a robust framework for evaluating the performance of configurations of SSCS. By considering passenger attributes (e.g., with or without bags) and special services (e.g., special check, additional search), we regard SSCS as two subsystems each of which is modeled as an iterative probabilistic formula, and introduce a hybrid queueing model to evaluate the waiting time of each passenger in SSCS. The parameters of the model are calibrated and validated using the field observation data collected in 24 subway stations of Beijing, which serve as the input of simulation studies. We conduct two simulation studies to evaluate three indices of SSCS: passengers’ waiting time in SSCS, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -systematic time of SSCS, and density flow map, and draw three conclusions. (a) 1X2D, 2X3D, 2X4D configurations are suitable for small, medium and large passenger flows, respectively, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\text{X}n\text{D}$ </tex-math></inline-formula> denotes the configuration of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> X-ray machines and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> detector doors; (b) acceptable efficiency of SSCS is achieved when the ratio between the numbers of X-ray machines and detector doors is in the range of 1:2-1:1, and the efficiency achieves highest at the ratio 1:2; (c) Increasing additional detector doors for no-bag channels can improve the efficiency of SSCS. However, switching existing doors to no-bag channels leads to opposite results. Our findings are beneficial to improving both the capacity and efficiency of SSCS, as well as allocating security check facilities reasonably.

Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference

This study examines the potential of using smart card data in public transit systems to infer attributes of travelers, thereby facilitating a more user-centered public transport service design while reducing the use of expensive and time-consuming travel surveys. This is challenging since travel behaviors vary significantly over the population, space, and time and developing meaningful links between them and traveler attributes are not trivial. To achieve this, we conduct an extensive analysis of spatio-temporal travel behavior patterns using smart card data from the Greater Sydney area (Opal card), and then develop a Hybrid Neural Network to utilize spatial and temporal dependencies in the dataset. In particular, we first empirically analyze passengers’ movements and mobility patterns from both spatial and temporal perspectives and design a set of discriminative features to characterize the patterns. We then propose a deep-learning-based framework to investigate spatial and temporal features in order to infer traveler attributes. The proposed modeling framework consists of two components, i.e., a Product-based Spatial–Temporal Module (PSTM) and an Auto-Encoder-based Compression Module (AECM). PSTM encodes the relationships across a variety of features while AECM derives useful spatial information from a transit stop matrix. The proposed model is tested and evaluated using a large-scale public transport dataset in the Greater Sydney area to infer two attributes of passengers, i.e., the age group and residential area. The experimental results demonstrate the effectiveness of the proposed method against a number of established tools in the literature. The developed techniques can be potentially adapted to other domains where spatio-temporal features are critical, such as commercial/entertainment site selection and urban service planning.

Modelling socioeconomic attributes of public transit passengers

The lack of personal and economic attributes in emerging public transit big data (such as smart card data) is a general issue that needs to be addressed. Passengers in the public transit network are from different socioeconomic classes, and their trip attributes usually depend on their personal and economic attributes. For instance, age as a demographic attribute plays an important role in trip attributes; adolescent passengers travel to school, young professionals travel to work, and old passengers travel to medical facilities more often. Relations between the socioeconomic and trip attributes of the passengers can be examined by developing a Bayesian network that represents the relations between the attributes by directed acyclic graphs, and calculating the joint and conditional probability values in the graph. This study infers the socioeconomic attributes of the public transit passengers from the trip attributes through developing a Bayesian network. Considered socioeconomic attributes are age, gender, and income; considered trip attributes are start time and duration of the trip, stay duration, and available origin and destination land use types. First, potential structures of the Bayesian network are examined by comparing network scores and arc strength test. After learning the network’s parameters, the reasoning is done through both prediction and diagnosis in the network. Also, the most likely combinations of the socioeconomic and trip attributes are discovered. The case study for developing the Bayesian network is a Household Travel Survey dataset from Queensland, Australia, that contains both socioeconomic and trip attributes. Results clearly show how the socioeconomic attributes can be inferred from the trip attributes. Discovered probability distributions can be used to enrich the smart card datasets with the socioeconomic attributes. Moreover, the Bayesian classifier is applied to the dataset to validate the capability of the model in predicting the socioeconomic attributes. In the end, the developed network is implemented on a set of smart card records to discuss the potential applications.

Key drivers of fare evasion in a metro system: Evidence from Athens, Greece

Fare evasion is often encountered in public transport and results in severe revenue loss for operators. Using data from the Athens Metro system, this paper attempts to capture the extent of fare evasion, analyze its causes and identify attributes of passengers who practice fare evasion. Perceived effectiveness of measures that can restrict its practice is also explored. The analysis is based on data collected as part of a personal interview survey of Athens metro users. Ordinal logistic models are developed and used to analyze the factors that affect fare evasion. Socio-demographic and travel characteristics, perception on fare levels, the quality of service, as well as personal attributes of respondents and psychological constructs are used as explanatory variables. Model outcomes reveal that gender, age, travel purpose, trip duration, finances and personal beliefs affect fare evasion. Findings may provide useful insights to policy makers and help towards applying effective strategies and target marketing policies to specific groups of travelers to mitigate fare evasion in metro systems.

Behavioural advertising in the public transit network

Public transit networks play a significant role for urban advertisers because a considerable number of residents in urban areas use public transport for their transportation needs. Automated Fare Collection (AFC) systems provide advertisers with valuable records (smart card data) of the boarding and alighting transactions of passengers in the public transit network. While the demographic attributes of passengers are missed by most AFC systems around the world, the systems can still help to reconstruct the activities and trips of passengers. The availability of smart card data has provided a unique opportunity to create detailed models of passengers' travel behaviour. Hence, it is now possible to develop behavioural advertising techniques in the public transit network based on ongoing activities of passengers. Behavioural advertising in the public transit network considers not only location and time of trips, but also duration and type of passengers' activities. This paper proposes and compares two behavioural advertising models based on the smart card data attributes. The first model, a trip-based one, targets trips of passengers, which means it indicates the maximum number of trips on which an advertisement should be viewed, according to the purpose of each trip. The second model, a passenger-based one, targets passengers by maximizing the number of passengers who will view an advertisement relevant to their trip. Both models are formulated as linear programming models. Both models are run on a case study basis to explicitly present the outcomes of the models and the differences between them. Outcomes of each model determine a set of prime time and locations for advertisements in the public transit network. While the trip-based model targets more relevant trips with simpler computations, the passenger-based model displays advertisements to a greater number of passengers, with more complex computations.

Expanding the Theory of Planned Behaviour to Reveal Urban Residents’ Pro-Environment Travel Behaviour

Exploring the mechanism that influences the choice of urban public travel mode is an important policy research topic that can promote urban residents’ pro-environment travel (PET) behaviour and relieve the pressure on urban traffic and environmental problems. By expanding the theory of planned behaviour by considering the effects of the quality of public transport service and individual behaviour, this paper establishes a mixed PET behaviour model. Grounded theory is used to analyse data obtained from in-depth interviews, with the aim of determining the relationships among different attributes of the quality of public transport service and PET. An empirical examination in the form of a questionnaire was conducted in Changsha, China, to obtain the intensity and mechanism of various factors influencing pro-environment behaviour decision-making. The results reveal three new pieces of information. First, the influence of many psychological variables (except subjective norms) is consistent with the prediction results of the theory of planned behaviour (TPB), and the predictions of the model are accurate. More specifically, intention (0.535) and habit (0.354) are key factors in PET behaviour, while attitude (0.527) has the most significant effect on intention towards PET behaviour. Second, the perceived service quality of public transport has a direct and significant impact on intention towards PET behaviour. Satisfaction with public transport service quality exerts a mediating effect on perceived service quality and PET behaviour. More specifically, operation and management (0.808) and vehicle environment (0.809) have the most important influence on intention towards PET behaviour. Last but not least, the extent of the influence of PET behaviour varies based on travellers’ demographic characteristics. The driving age, income and ownership of private cars show the greatest impact. The perceived service quality of public transport and travellers’ social and economic characteristics all play roles in the psychology of travel decisions, and are associated with PET behaviour on several distinct levels. From the perspectives of passenger psychology, public transport service quality and personal attributes of passengers, this paper provides a scientific basis for decision-making in transportation systems and the formulation of traffic intervention strategies to promote voluntary public reductions in carbon-intensive travel behaviour.

Passengers’ Perceptions on Effectiveness of Public Facilities in Hasanuddin International Airport Terminal Based on Ordered Logit Model

The present study attempts to grasp the effectiveness of the public facilities such as waiting rooms, lounge, and cafe in Hasanuddin International Airport terminal, in Makassar City, Indonesia regarding the preferences of the airport passengers. This study collected the passenger's preferences data through an interview survey approach which using a questionnaire sheet instrument. The inquiry involves individual characteristics and travel attributes of passengers. The study used an ordered logit model in analyzing the passenger's preferences. The analysis results show that there are some significant variables which influence the traveler's preferences in the assessment of the effectiveness of the public facilities in the airport. The relevant variables involve airlines, trip frequency, the time interval between arrival time and boarding time, and the place of waiting for boarding time. The passenger's preferences analyzing described that the public facilities at Hasanuddin International Airport are sufficient enough.

Semi-lagrangian formulation of an extended GSOM Model for Multimodal Transportation Systems

The object of the paper is to introduce a macroscopic multimodal transportation model, based on the GSOM (generic second order modelling) approach. In multimodal transportation systems, there are two flows; the flow of vehicles, and the flow of passengers. These two flows are not independent, since vehicles carry passengers. Hence the idea of the GSOM approach: to describe first the vehicle flow, and to consider the passenger load of vehicles as an attribute of vehicles. The resulting model is treated in a semidiscretized lagrangian way: vehicles are discretized, and passengers are modelled by continuous quantities (passenger load). Nodes in the model recapture the main complexity of the transportation system. They can represent such distinct features intersections (cars), stations (buses, trains), or intermodal poles. The advantage of the model is that it provides a unifying macroscopic view of multimodal transportation systems, can accomodate various vehicle and passenger attributes, and thus should provide a useful tool for the management of such systems.

Research on the Characteristics of Actual Passenger Load for Urban Buses

Vehicle load plays an essential role in the development and evaluation on vehicle emissions, fuel consumption, and other energy-saving technologies. On the basis of 10 representative routes selected deliberately from 265 routes in Fuzhou urban area, real-time on-road 2915 samples of passengers on and off are recorded, including bus model, bus station, passenger attributes, number of passengers on and off, etc. Consequently, a creative mathematical model on actual load rate for urban buses is proposed to discover the real characteristics of actual passenger load in Fuzhou, which is only 20.86% of approved full load. The results of this research attempt to provide a more reliable and reasonable weight for urban buses in the process of evaluation of fuel consumption and emissions.

AIRLINE SERVICE FAILURE AND RECOVERY: THE IMPACT OF RELATIONSHIP FACTORS ON CUSTOMER SATISFACTION

In aviation industries, service failure during the service delivery process is foreseeable and leads to passenger complaints, which therefore presents the perfect opportunity for airlines to improve their service process and quality and examine their internal organization. Concurrently, the quality of the service recovery measures reflects the ability of airlines to respond to and handle traveler complaints. By rectifying service failures, airlines can enhance traveler satisfaction toward airlines services, thereby generating loyal customers who would engage in word-of-mouth marketing. This study aims to do examine the relationship between service failure, service recovery and passenger’s satisfaction with service recovery types, employee prompt handling, and service recovery efficiency. The questionnaires used in this study consisted of three sections: (1) Customers’ perception of the service recovery types; this section entails using passengers’ subjective perceptions to evaluate the service recovery types adopted by the airlines when handling flight delay situations. (2) Customers’ perception of the airlines employee’s prompt handling; the traveler’s subjective perception to evaluate the airlines employees’ direct responses to flight delays. (3) Customers’ perception of the problem-solving efficiency; this section involves using the passengers’ subjective perception to evaluate the overall flight delay recovery progress. The traveler characteristics were divided into ‘passenger attributes’ and ‘traveling attributes’ and their relationships with service recovery types, employee’s prompt handling, and problem solving efficiency were examined. The research results showed that passenger attributes demonstrated no significant differences with the three dimensions (i.e., service recovery types, employee’s prompt handling, and problem solving efficiency). However, concerning traveling attributes, ‘purpose of travel’ and ‘flight delay experience’ demonstrated significant differences with the threedimensions.

Fatality rates for crashes involving heavy vehicles on highways: A random parameter tobit regression approach

ABSTRACTFew studies have analyzed the impacts of freight movements (large truck) on crash fatality rates. This study explores a novel application of a method, namely the random parameters tobit regression model to large truck fatality rates by using the nationwide Fatality Analysis Reporting System. By utilizing random parameter tobit regression the authors examined crash rates (instead of frequencies) in per million truck-miles traveled and ton-miles of freight in the United States as continuous censored variables. The empirical and statistical results illustrate that the random-parameters tobit regression model provides a better understanding of the fatality rates per million truck-miles traveled and ton-miles of freight over the fixed parameter tobit model. Factors related to the crash mechanism, temporal and spatial characteristics, road and environmental attributes, vehicle configuration, drivers and passenger attributes were found to be statistically significant. Some exposure to injury severity related factors also were found to be significant with random parameters that vary across the observations.

Identifying Factors Impacting Customers’ Perceived Waiting Time in High Density Passenger Flow Waiting Areas

The pedestrian waiting area is a kind of important pedestrian traffic facility. According to pedestrians’ perceived waiting time, this paper evaluates the level of service (LOS) of waiting areas with high density pedestrian flow. After a detailed analysis of the passenger survey data collected in the waiting areas of the 2010 world EXPO in Shanghai, the paper introduces a perception factor φ as the evaluation index of waiting time. That aims to solve the problem of no comparability of the passenger’ waiting time in different waiting areas. An analysis is conducted on the difference of the perception factors under different population categories and facility environment. On the basis of this analysis, this paper develops a regression model to analyze how the perception factor is related to the passenger's attributes (age, gender, number of partners, visiting times) and the objective environment (weather, waiting facilities, waiting information available, onsite interactive program, the number of people closely around). Then, the relationship between the occurrence of the passengers’ boredom sense in waiting and the perception factor φ is established. Consequently, it is found that φ =1.2 was a critical point at which the passengers’ boredom sense increases significantly. The data also shows a negative correlation between the perception factor and the LOS.

利用者属性に着目したコミュニティバス利用者満足度と利用頻度向上施策に関する研究

In order to grasp the improvement effect of bus operation, passenger consciousnesses were investigated on the community buses operated in Nisshin City, Nagakute Town, Togo Town, Aichi prefecture. In general, it is thought that the degree of satisfaction relates to the frequency of using the community bus. However, we have not seen the relation directly between the satisfaction and the frequency. The improvement effect of bus operation was grasped under simulated scenarios. As a result, it is clarified that the bus operation to improve the satisfaction is different from the operation to increase the frequency, and effective measures to improve the satisfaction or the frequency depend on the passenger attributes.

Determination of A Bus Service Coverage Area Reflecting Passenger Attributes

The paper presented not only the difference of the marginal walking time by passenger, but also the derivation of the bus service determination model by passenger attributes. The marginal walking distance to bus stop is a basic parameter to estimate bus service coverage area in certain area. And the marginal walking distance could be transformed from the marginal walking time, which is the focus of this paper. The result of analysis revealed that the age and income of passenger are strongly related to the marginal walking time. The model suggested in this paper help for the planner to choose optimal alternative in planning or regulating the spacing of bus routes or bus stops in certain area,.