Passenger Walking Research Articles

A historical data approach to optimal gate assignment problem considering aircraft taxi delay and passenger walking time

ABSTRACT Assigning appropriate gates to incoming flights, is a common challenge faced by airports and airlines. This study focuses on the gate assignment problem, considering taxi delays and passenger walking times with a historical data approach. Firstly, ten years of taxi time data from the Aviation Service Performance Metrics (ASPM) system and FlightAware were extracted. Then, using statistical analyses, key parameters for taxi time estimation were found, and by defining the Hour Modification Factor concluded from history-based data and Mixed-Integer Linear Programming, the new gate assignment scheme considering taxi delay was developed. As a case study, taxi delays and dedicated gate assignment for American and Southwest Airlines in Phoenix airport were performed. The results showed that the new gate assignment significantly reduced overall taxi times for both airlines with that 10% reduction in overall taxi times for American Airlines and a 16% reduction for Southwest Airlines, indicating a more efficient airport operation.

Ventilation strategy for particulate matter control in subway stations

Particulate matter severely affects human health, and its migration is closely related to human walking and ventilation. It is essential to select an appropriate ventilation strategy to control the particulate matter caused by cluster passengers walking. In this study, cluster passengers walk in the narrow passage of a subway station. Large eddy simulation (LES) was used to study the influence of cluster passengers walking on the transmission of particulate matter under ventilation conditions involving upper exhaust and lower exhaust. The risk of passengers exposure was analyzed by comparing the exposure levels of particulate matter at different heights under the effect of human disturbance. The results show that the amount of particulate matter near the legs is greatest during human walking. Clustered passengers disturbance airflow induces particulate matter to rise to the breathing zone, which increases the risk of human exposure. Lower-side exhaust conditions are more conducive to the discharge and control of particulate matter. The disturbance of cluster passengers enhances the continuous transmission of particulate matter in the wake flow. This study is applicable to traffic hubs and other public places where passengers gather, the ventilation of the upper side exhaust has better control effect on particulate matter caused by passengers wake flow.

Dynamic airport gate assignment with improved Shuffled Frog-Leaping Algorithm and triangle membership function

The rapid development of the air transportation industry has increased air traffic, posing challenges to the task of airport gate assignment (AGA) for flights. Most past studies have solved the AGA problem (AGAP) using deterministic models, which are incapable of dealing with uncertainty and dynamic conditions at airports. Thus, this research employs fuzzy theory and proposes a triangular membership function to handle flight uncertainty in the AGAP. In addition, an improved metaheuristic, termed the improved Shuffled Frog-Leaping Algorithm (ISFLA), is proposed to circumvent the computationally intractable problems commonly faced by exact approaches when handling large instances. In this research, the AGAP is first formulated as a stochastic Mixed-Integer Linear Programming (MILP) model, with stochastic flight lateness and earliness considered. The objective of this model is to minimize the total cost, which consists of three sub-costs: passenger walking distances, non-preferred gate (NPG) assignments for planes, and fuzzy idle times of gates. These three sub-costs correspond to the major concerns of passengers, airlines, and airports, respectively. The cooperation between the ISFLA and the triangular membership function demonstrates their capability to effectively handle big AGAP instances. Furthermore, the experimental results show that the ISFLA outperforms the standard SFLA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA).

Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Selecting optimal locations for customized bus stops is crucial for enhancing the adoption rate of customized bus services, reducing operational costs, and, consequently, mitigating traffic congestion. This study leverages ride-hailing data to analyze the distance between passengers and bus stops, as well as the operational costs associated with establishing these stops, to construct a customized bus stop location model. To address the limited local search capability of conventional immune algorithms, we propose a Dual Population Adaptive Immunity Algorithm (DPAIA) to solve the bus stop location problem. Finally, we conduct simulation experiments using passenger travel data from a ride-hailing company in Chengdu to evaluate the proposed customized bus stop location model. Through simulations with Chengdu ride-hailing data, the DPAIA algorithm minimized the weighted cost to CNY 28.95 ten thousand, outperforming all counterparts. Although proposing 9–11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban congestion while boosting operator profits.

Multi-period human evacuation model for passenger ships under walking speed uncertainty

An effective evacuation model can steer passengers from unsafe zones to safety in a time-sensitive manner on passenger ships. The International Maritime Organization highly recommends constructing robust evacuation models rooted in understanding human behavior and ship design. This study addresses the challenge by focusing on walking speed, a key determinant of human behavior, the capacity of transitional points (e.g., exit stairs), and the distance of passengers to the exit stairs for a safe and rapid evacuation. This study presents a multi-period human evacuation model aimed at optimizing the evacuation time of the slowest passenger, identifying the optimal number of stairs, and planning route evacuation under passenger walking speed uncertainty affected by the heeling angle for a deck of a passenger ship.A robust optimization approach is also employed to manage uncertainty by defining uncertainty sets for passengers’ walking speeds. The computation of the price of robustness spans various test problems at various levels of conservatism. This research offers multiple managerial implications for maritime industry leaders, paving the way for more efficient evacuation planning.

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-Offs: A Causal Inference Approach

Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curbside utilization and disturb the mainline traffic flow, evidently leading to significant negative societal externalities. However, there is a lack of an analytical framework that rigorously quantifies and mitigates the congestion effect of PUDOs in the system view, particularly with little data support and involvement of confounding effects. To bridge this research gap, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general regional networks. A causal graph is set to represent the spatiotemporal relationship between PUDOs and traffic speed, and a double and separated machine learning (DSML) method is proposed to quantify how PUDOs affect traffic congestion. Additionally, a rerouting formulation is developed and solved to encourage passenger walking and traffic flow rerouting to achieve system optimization. Numerical experiments are conducted using real-world data in the Manhattan area. On average, 100 additional units of PUDOs in a region could reduce the traffic speed by 3.70 and 4.54 miles/hour (mph) on weekdays and weekends, respectively. Rerouting trips with PUDOs on curb space could respectively reduce the system-wide total travel time (TTT) by 2.44% and 2.12% in Midtown and Central Park on weekdays. A sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework. Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant 52102385], grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grants PolyU/25209221 and PolyU/15206322], a grant from the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) at the Hong Kong Polytechnic University [Grant P0043552], and a grant from Hong Kong Polytechnic University [Grant P0033933]. S. Qian was supported by a National Science Foundation Grant [Grant CMMI-1931827]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0195 .

A data-driven, scenario-based human evacuation model for passenger ships addressing hybrid uncertainty

In a disaster at sea, the safe and timely removal of passengers from the ship is paramount. Here, a human evacuation plan enables passengers to be swiftly displaced from a risk area to one less so. Despite existing research on evacuation planning, there is a need for a more comprehensive model that considers various uncertainties and factors. This paper proposes an optimization evacuation model that balances uncertain variables, including passenger walking speed and travel distance, and deterministic factors like deck layout, door capacity, initial density, and corridor traffic flow. The model also accounts for varying starting locations and two levels of awareness—alert and non-alert. The model utilizes a data-driven technique, i.e., the k-means algorithm, to cluster historical data on speeds and generate scenarios. An adjustment scheme is applied to account for the ship's rolling motion, affecting passenger speeds during the evacuation planning period. Travel distance scenarios are produced to capture the impact of different route choices per passenger. A risk-neutral two-stage programming model is constructed to handle uncertainties. The model is tested on multiple problems for a passenger ship deck in day and night modes, revealing valuable managerial insights for the maritime safety sector.

An Airport Stand Assignment Problem considering the Passenger Boarding Distance

The continued growth in the civil aviation industry leads to more traffic in the airport, resulting in a decline in operational efficiency and the travel experience of passengers. Studying how to improve operational efficiency and keep passenger satisfaction simultaneously is very significant. This study proposes to use total passenger boarding distance instead of total passenger walking distance to quantify passenger satisfaction and then model the airport stand assignment problem considering these two different objectives together with the gated percentage, respectively, and the NSGA-II algorithm is improved for a better solution speed. This study also performs a case study by applying a dataset of an airport in China. The results of the case study prove that using the total passenger boarding distance can help the airport better balance operational efficiency and passenger satisfaction, which can help provide theoretical support for airport management.

A column generation-based algorithm for gate assignment problem with combinational gates

The gate assignment is a complicated real-world problem occurring in airports. The present paper studies the gate assignment problem with combinational gates. The problem is multi-objective by nature, concerning four objectives. Among those objectives, the number of passengers allocated to nearby gates is aimed to be maximized. The remaining objectives are targeted to be minimized, i.e., the total passenger walking distance, the amount of carbon dioxide emitted by the aircraft assigned to remote gates, and the total distance traveled by the shuttle bus. To solve this problem, we formulate an integer programming model. A column generation-based algorithm based on a set-partitioning model is proposed to solve the problem effectively. In addition, the Pareto Local Search algorithm is adopted to obtain non-dominated solutions. Computational experiments show that the effectiveness of our model is verified through tests with instances of different sizes. The column generation-based algorithm can obtain the optimal solution or the approximately optimal solution with a small gap in a reasonable time. The results of the Pareto Local Search algorithm show the interaction between multiple objectives.

Artificial Intelligence Aided Crowd Analytics in Rail Transit Station

Crowd analysis and management is a key area of study for transit agencies seeking to optimize their operations and to facilitate safety risk management activities. Key features of crowd analytics include passenger flow volume, crowd density, and walking speed. This study proposes a generalized artificial intelligence (AI)-based crowd analytics model framework for rail transit stations, by analyzing and visualizing crowd analysis data from video records of high-density crowds. Specifically, we propose a generalized AI-aided methodological framework (AI-Crowd) for calculating flow volume, crowd density, and walking speed. You Only Look Once (YOLO) and Deep SORT are integrated into the model framework to detect and track each individual’s dynamic position. Camera calibration is utilized to transform detected trajectories into a real-world coordinate system. Methods for calculating crowd dynamic metrics are formulated based on the data. To validate the model framework, several video records from a platform scenario at a major rail transit station are used. The model’s pedestrian counting accuracy can reach 95% and the fundamental diagrams of density–speed are shown to be consistent with empirical studies. Further crowd analysis of a stair scenario and a transferring passage scenario using the proposed model framework shows some differentiations in walking behavior. The methodology has further practical applications, such as monitoring social distancing.

Optimisation of a new hybrid transit service with modular autonomous vehicles

This study devises a novel hybrid public-transit service that uses modular autonomous vehicles (MAVs) coupled in one fleet that travels a fixed base route. MAVs can decouple from the fleet at the beginning of selected road segments and recouple to the fleet at the end of these segments. This allows a decoupled MAV to serve passengers who request door-to-door service within the deviated area, while the other passengers complete their trips via regular service. This study develops an optimisation model to determine the deviated segments, the MAVs to be decoupled and the corresponding timetable that minimises the total passenger and operator costs. Finally, a case study is conducted using real data from Dalian, China. The results show that compared with a conventional fixed-route service, the hybrid service significantly reduces the total system cost, especially passenger walking and waiting time costs..

Iterated clustering optimization of the split-delivery vehicle routing problem considering passenger walking distance

The user-oriented, demand-driven nature of customized shuttle bus (CSB) services forces operators to pay more attention to service quality. However, the bus route planning of CSB and other similar systems in established studies rarely took service quality into consideration while minimizing operating costs. This study proposed an open split-delivery weighted vehicle routing problem with iterated clustering (OSDWVRP-IC) to simultaneously optimize the route of bus and passenger walking distance. A max–min ant system (MMAS) algorithm is developed to solve the OSDWVRP-IC. The effectiveness of the proposed model is validated using a range of classical benchmark instance sets. The simulation results indicate that the algorithm proposed in this study sacrifices a very limited in-vehicle distance in exchange for a significant reduction in passenger walking distance.

Impacts of human movement and ventilation mode on the indoor environment, droplet evaporation, and aerosol transmission risk at airport terminals

The dispersion of the coronavirus pandemic has caused immense damage worldwide, and people have begun to ruminate epidemic prevention strategies for public places. Airport terminals with a high number of occupied passengers have become potentially high-risk regions for aerosol transmission of coronavirus disease 2019 (COVID-19). In this study, the Eulerian–Lagrangian approach and realizable k-ε turbulence model were used to numerically simulate airflow organization and aerosol transmission when passengers are moving slowly in a line. During the aerosol transmission period, evaporation was considered a key factor influencing the particle size distribution at the beginning of aerosol transmission from humans. Moreover, passenger movement at the airport terminal was attained by employing dynamic mesh algorithms. Based on the relative direction of passengers and air vents when queuing in the terminal building, we studied three conditions: windward walking, leeward walking, and crosswind walking. The results of this study showed that the walking has an important influence on droplet distribution. Droplet distribution indicates that individuals standing behind patients during queuing movements have a higher risk of infection than those standing in front of them. A significant aerosol accumulation was discovered at 0.5 m behind the patient when passengers moved simultaneously. An aerosol transmission distance of 15 s aligned with the passenger's walking direction could reach up to 9.32 m. Furthermore, although the evaporation time of the large droplets was longer than that of the small droplets, both large and small droplets evaporated rapidly after exhalation. The crosswind influence caused the droplets to travel farther away in a direction perpendicular to human movement, which increased the distance by approximately 1.26 m compared to the absence of the crosswind influence.

An integrated method for assessing passenger evacuation performance in ship fires

In order to assess effectively the passenger evacuation performance in ship fires, a quantitative method combining the computational fluid dynamics (CFD)-derived fire data and AnyLogic evacuation simulation is proposed in this paper. In this developed method, the speed reduction factor, instantaneous untenable conditions and fractional effective dose (FED) model are utilized to consider the effects of smoke visibility, heat and toxic gases in ship fires on the passenger walking speed, activity space and human health conditions. In order to demonstrate this proposed method, passenger evacuation analysis is performed in a hypothetical zone of the ship with obstacles for fast t-squared fires. The results indicate that the relationship between the required safe egress time (RSET) and fire heat release rate (HRR) is non-monotonic. However, the total FED increases with the increase of the fire HRR and the sequence of survivors. Compared with two existing methods, this proposed method is able to assess more rigorously the passenger evacuation performance in ship fires. The existing ASET/RSET method is more conservative, and the existing FED method with the Pathfinder underestimates the FED in ship fires. This work will provide a more accurate method for ship fire safety assessment and performance-based fire protection design.

Optimization of multi-objective airport gate assignment problem: considering fairness between airlines

The Airport Gate Assignment Problem (AGAP) deals with assigning a set of aircraft to the gates at airports with respect to several operational and commercial constraints. In this study, we model the AGAP as a multi-objective optimization problem that minimizes the aircraft taxiing costs and passenger walking distance. An additional consideration is taken regarding the fairness of assignment among different airlines. To solve this problem, we propose the NSGA-II-LNS algorithm which builds upon the NSGA-II framework and incorporates the advantages of Large Neighborhood Search (LNS) for refining the solution quality. Two search operators are designed within LNS tailored to different senses of fairness coefficients. A numerical study at Nanjing Lukou International Airport indicates that the proposed algorithm significantly outperforms the previously published algorithms in terms of both solution convergence and diversity. Moreover, acceptable computing time implies the practical potential of our model.

WALK MODELLING WHEN DESIGNING SMALL BUS TRANSIT NETWORKS

Models for the design of public bus transit networks are usually based on the historically observed origin-demand travel patterns of potential passengers. The option of a passenger choosing to walk is, however, not usually incorporated into the design models for such networks. We argue that the absence of this model feature might lead to significant sub-optimality, especially in the design of small public bus transit networks. A novel approach to including this feature in the bus frequency setting aspect of public bus transit network design models is demonstrated in this paper, in which passenger transit modal choices are modelled on the basis of an existing optimal strategy transit assignment model. The newly proposed bi-objective route design and frequency setting models are applied to a realistic case study involving the establishment of a public bus transit system for students on a university campus; and this model implementation is used to gain insight into passenger route choice behaviour, based on individual preferences between walking or using buses. It is shown in the context of this case study that including the option of a passenger walking in the design models for public bus transit networks can affect the efficacy of the final network, especially if the network is small.

Estimation of transfer walking time distribution in multimodal public transport systems based on smart card data

Transfers are a major contributor to travel time unreliability for journeys in public transport. Thus, connections between services in the public transport network must be reliable. To plan such reliable transfers from e.g. busses to trains, it is crucial to know the necessary walking times from stops to platforms. This paper presents an innovative approach for estimation of walking time distributions from bus stops to train platforms based on a matching of smart card data and automatic vehicle location data. The observed times from bus stop to rail platform turns out to have a large variance, due to two reasons: differences in passenger walking speeds, and passengers who are doing activities during the transfer. To account for these variations a hierarchical Bayesian mixture model is applied, where the time for passengers walking directly and passengers doing activities during the transfer follows separate distributions. The proposed methodology is applied to 129 stations in the Eastern part of Denmark, where the tap-in devices are located at the train platform. Results from two stations with different characteristics are presented in details along with justifications and analyses of model accuracy. The outcome of the model with distributions of the necessary walking times from bus stops to train platforms is important input for timetabling connections, and the data-driven methodology can easily be applied at scale.

Study on Optimization of Passenger Flow at a Metro Station Based on AnyLogic—Case Study of Youfangqiao Station of Nanjing Metro Line 2

In this study, simulation software AnyLogic was used to establish a station simulation model for a metro line. First, a basic model of the environment of the metro station was drawn, and accordingly, reasonable assumptions and simplifications were proposed. Then, a diagram of the passenger walking path was created and the simulation variables and functions for passenger flow management were designed. Considering Youfangqiao Station of Nanjing Metro Line 2 in China as an example, the real passenger flow data of this station were statistically analyzed. To simulate the station passenger flow management, input parameters such as the passenger space diameter, passenger flow generation rate, delay rate of automatic fare collection equipment and security check machine, and the number of gates were considered. Passenger flow management was optimized for the morning and evening peak periods, and reasonable suggestions were proposed based on the optimization results, providing a theoretical basis for the construction planning and pre-evaluation of station operation capacities of urban rail transit systems.

Evaluation of subway passengers transfer hub congestion based on queuing model and TOPSIS method

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

Evacuation Analysis of Ship Passenger Using Agent Based Modelling Simulation

Ship accidents that occur in Indonesian have a high frequency. The ship accidents is dominated by fires of Ro-ro or passenger ships which cause material loss, water pollution and loss of human life. In this paper, an analysis of the ship evacuation system will be carried out. The Agent Based Modelling Simulation (ABMS) was using in this study. Initial data required are in the form of ship general arrangement, passenger walking speed, as well as the composition and distribution of passengers. The distribution of ship passengers in Indonesia for the category of women aged less than 30 years is 12%, women aged 30-50 years are 21%, women aged greater than 50 years are 5%, there are no female passengers with disabilities, while for the category of males aged less than 30 years as much as 19%, males with an age range of 30-50 years as much as 31%, males over 50 years old as much as 12%, and no male passengers were found. a man with a disability. The number of ship passengers is conditioned to exceed the passenger capacity, namely 880 passengers should be conditioned to become 1527 passengers. The evacuation simulation of ship passengers using an agent based modelling simulation is run based on the IMO MSC. 1 / Circ. 1533 using 4 cases during the day and night. In the case of a normal night it takes 38.24 minutes to evacuate all passengers, and in the case of a night the evacuation takes 36.45 minutes. In the normal day case it took 29.49 minutes and in the afternoon case the evacuation took 28.12 minutes.