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Modelling the pedestrian’s willingness to walk on the subway platform: A novel approach to analyze in-vehicle crowd congestion
... For example, collecting and organizing data on crowd behaviour, travel preferences, and modes of transportation, and using mathematical models and additional tools for analysis. This will assist urban planners in gaining a better understanding of the operational mechanisms of travel systems and in formulating more effective travel planning schemes [36][37][38][39]. Consequently, it will improve the travel efficiency of the city and enhance the convenience for tourists. ...
The rapid development of cities has led to increasingly problems in the road network structure of urban streets. Combining emerging big data technology with traditional street network analysis methods has become a new way to tackle it. Guilin is a famous international tourist city, and the “Two Rivers and Four Lakes” scenic area is an iconic symbol of Guilin’s scenery. Its streets connect various tourist spots. This study focused on the street’s layout of the “Two Rivers and Four Lakes” scenic area, and used a combination of spatial syntax and POI big data to analyse their spatial structure. The research results indicated that: (1) there was a positive correlation between the global integration value of the street and the POI value; (2) by combining functional density indicators with global integration analysis, streets that significantly deviate from the overall trend can be identified, and classified according to their characteristics to reveal the reasons for their contradictions; (3) we needed to propose three plans for optimizing the proportion of high street, enhancing street functions, and “improving street space” for different types of streets to ultimately realize the purpose of sustainable development of streets and cities.
A R T I C L E I N F O Keywords: Energy-efficient timetabling Path choice behavior Tree knapsack problem Urban rail transit A B S T R A C T Urban rail transit (URT) is the backbone transport mode in metropolitan areas to accommodate large travel demands. The high energy consumption of URT becomes a hotspot problem due to the ever-increasing operation mileages and pressing agendas of carbon neutralization. The high model complexity and inconsistency in the objectives of minimizing passenger travel time and operational energy consumption are the main challenges for energy-efficient timetabling for a URT network with multiple interlinked lines. This study proposes a general model framework of timetabling and passenger path choice in a URT network to minimize energy consumption under passenger travel time constraints. To obtain satisfactory energy-efficient nonuniform timetables, we suggest a novel model reformulation as a tree knapsack problem to determine train running times by a pseudo-polynomial dynamic programming algorithm in the first stage. Furthermore, a heuristic sequencing method is developed to determine nonuniform headways and dwell times in the second stage. The suggested model framework and solution algorithm are tested using a real-world URT network, and the results show that energy consumption can be considerably reduced given certain travel time increments.
Urban geometry plays a critical role in determining paths for pedestrian flow in urban areas. To improve the urban planning processes and to enhance quality of life for end-users in urban spaces, a better understanding of the factors influencing pedestrian movement is required by decision-makers within the urban design and planning industry. The aim of this study is to present a novel means to assess pedestrian routing in urban environments. As a unique contribution to knowledge and practice, this study: (a) enhances the body of knowledge by developing a conceptual model to assess and classify pedestrian movement behaviours, utilising machine learning algorithms and location data in conjunction with spatial attributes, and (b) extends previous research by revealing spatial visibility as a driver for pedestrian movement in urban environments. The importance of the findings lies in the perspective of revealing novel insights concerning individual preferences and behaviours of end-users and the utilisation of urban spaces. The approaches developed can be utilised for observations in large-scale contexts, as an addition to traditional methods. Application of the model in a high pedestrian traffic-dense retail urban area in London reveals clear and consistent relationships amongst spatial visibility, individuals' motivation, and knowledge of the area. Key behaviours established in the study area are grouped into two activity categories: (i) Utilitarian walking (with motivation-expert and novice striders) and (ii) Leisure walking (no motivation-expert and novice strollers). The approach offers an insightful and automated means to understand pedestrian flow in urban contexts and informs wider wayfinding, walkability, and transportation knowledge.
This study proposes a probabilistic framework to infer passengers’ responses to unplanned urban rail service disruptions using smart card data in tap-in-only public transit systems. We first identify 19 possible response behaviors that passengers may have based on their decision-making times and locations (i.e, the stage of their trips when an incident happened), including transferring to a bus line, canceling trips, waiting, delaying departure time, etc. A probabilistic model is proposed to estimate the mean and variance of the number of passengers in each of the 19 behavior groups using passengers’ smart card transactions. The 19 behavioral responses can be categorized from two aspects. From the behavioral aspect, they can be grouped into 5 aggregated response behaviors including using bus, using rail (changing or not changing route), not using public transit, and not being affected. The inference of the 19 behaviors can be classified into four cases based on the information used (historical trips vs. subsequent trips) and the context of the observed transactions (direct incident-related vs. indirect incident-related). The public transit system (bus and urban rail) of the Chicago Transit Authority (CTA) is used as a case study based on a real-world rail disruption incident. The model is applied with both synthetic data and real-world data. Results with synthetic data show that the proposed approach can estimate passengers’ behavior well. The mean absolute percentage error (MAPE) for the estimated expected number of passengers in each behavior group is 20.5%, which outperforms the rule-based benchmark method (60.3%). The estimation results with real-world data are consistent with the incident’s context. An indirect model validation method using demand change information and incident log data demonstrates the reasonableness of the results.
As travel demand grows in many cities around the world, overcrowding in public transport systems has become a major issue and has many negative effects for both users and operators. Measures to address on-board congestion span from large-scale strategic investments (e.g. increasing infrastructure capacity), through tactical planning (e.g. stopping pattern) to real-time operational measures (e.g. information provision, gate and escalator control). Thus there is a need to evaluate the impact of these measures prior to their implementation. To this end, this study aims at capturing the effective capacity utilization of the train, by considering passengers' distribution among individual train cars into an agent-based simulation model. The developed model is validated and applied to a case study for the Stockholm metro network. The findings suggest that an increase in peak hour demand leads to a more even passenger distribution among individual train cars, which partially counteracts the increased disutility caused by the higher passenger volumes. Interestingly, the closure of the most popular entrance point at one of the stations leads to lower train crowding unevenness at the downstream stops and consequently reduces passengers' experienced discomfort. We find that the user cost is significantly underestimated when passenger distribution among cars is not accounted for.
We propose a methodology based on multiple automated data sources for evaluating the effects of station layout, arriving traveler flows, and platform and on-board crowding on the distribution of boarding passengers between individual cars of metro trains. The methodology is applied to a case study for a sequence of stations in the Stockholm metro network. While train car loads at the analyzed stations are generally skewed towards the leading cars, results indicate that a crowded front car of the arriving train is associated with increasing boarding shares of the middle car. Moreover, higher platform crowding is found to have a positive effect on the boarding share of the middle cars. These findings suggest that passengers opt for less crowded train cars in crowded situations, trading-off walking and in-vehicle crowding while waiting and riding. We find that the boarding car distribution is also affected by the locations of platform access points and the distribution of entering traveler flows. These insights may be used by transit planners and operators to increase the understanding of how passengers behave under varying crowding conditions, identify the factors that affect travelers' choice of metro car and eventually reduce experienced on-board crowding and increase the capacity utilization of the trains through investments in infrastructure or operational interventions.
Metro capacity should be further improved with growing demand for metros. For metro lines with high-frequency services, the train dwell time is a determinant of the number of trains per hour. Uneven passenger distribution on the platform may cause a longer dwell time, thereby reducing the capacity. This study analyses the mechanism of the passenger distribution on the platform with regard to which cars of a train passengers choose to board. A model has been developed, considering the origin-destination and waiting time of passengers and the platform layouts of the stations where passengers board and alight. The model was calibrated using loadweigh data, which provides the weight of passengers on each car of a train, on the Hammersmith & City line of the London Underground. The largest difference between the actual and modelled average number of boarders on a car is less than 2 passengers, which suggests the model performs well. The results show, at both stations studied, 44% of passengers in the morning peak chose boarding cars based on the layouts of the stations where they alighted. The results would be useful for metro operation planners and station staff to take measures to manage passenger distribution on the platform.
To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual’s behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physicists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek, 2000; Helbing et al., 2002). This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. A problem, however, is that the testing results of the model were not explained in consistency with the psychological findings, resulting in misunderstanding of the model by psychologists. This paper will bridge the gap between psychological studies and physical explanation about this model. We reinterpret this physics-based model from a psychological perspective, clarifying that the model is consistent with psychological studies on stress, including time-related stress and interpersonal stress. Based on the conception of stress, we renew the model at both micro-and-macro level, referring to multi-agent simulation in a microscopic sense and fluid-based analysis in a macroscopic sense. The cognition and behavior of individual agents are critically modeled as response to environmental stimuli. Existing simulation results such as faster-is-slower effect will be reinterpreted by Yerkes–Dodson law, and herding and grouping effect are further discussed by integrating attraction into the social force. In brief the social-force model exhibits a bridge between the physics laws and psychological principles regarding crowd motion, and this paper will renew and reinterpret the model on the foundations of psychological studies.
Abstract: The problems such as design of a metro station, an analysis of the station or line performance characteristics, simulation of passenger alighting and boarding process, simulation of metro line operation, etc. involve the need to get much better knowledge about the behavior of the passengers on the station platform in terms of their distribution among train doors in relation with the station design elements. Despite this, there have been only very limited number of researchers that tackled this problem. In this article, a model of passenger distribution among metro train doors has been developed. The model has been based on the data collected from the Bloor metro station in Toronto, Canada. Metro trains were video-taped during two-hour study period and data were obtained for the number of alighting and boarding passengers per each door and per each train. The results of the statistical analysis have shown that multinomial probability distribution appears to be a good model to describe passenger boarding and alighting process. It also has been shown that there has been clear dependence of the passenger distribution among train doors on the position of the platform entrance and exit points.
We propose a revised social force model to simulate the pedestrian counter flow through a bottleneck. Spatial and temporal separation rules are involved in this model so as to reproduce these self-organizing movement patterns of pedestrians, including oscillatory flow and three classes of lane formations. Moreover, by scenario simulations, we show that, by reasonably adjusting the parameters in these separation rules, the pedestrian efficiency of passing through the bottleneck can be improved. The study is helpful for designing and planning pedestrian facilities involving pedestrian counter flow through the bottleneck and for evaluating the pedestrian efficiency of passing through a bottleneck.
Knowing passenger numbers is important for the planning and operation of the urban rail systems. Manual and electronic counting systems (typically infrared or video) are expensive and therefore entail small sample sizes. They usually count boarding and alighting passengers, which means that errors in estimates of total numbers of passengers propagate along train runs. Counting errors in manual and electronic counting systems are typically flow-dependent, making uncertainty a function of volume. This paper presents a new counting technique that exploits the weighing systems installed in most modern trains to control braking. This technique makes passenger counting cheaper and ensures a complete sample. The paper compares numbers estimated by this technique with manual counts and counts from an infrared system in trains in urban Copenhagen. It shows that the weighing system provides more accurate passenger counts than the infrared equipment. The method has been validated on a large data set and is now in full operation in the urban Copenhagen rail system.
Crowding on metro trains is an important measure of passenger satisfaction and also provides a criterion for determining service frequency and the number of cars necessary for a train set. Particularly in metropolitan areas during morning peak hours, many studies have revealed a considerable difference in the crowding of specific cars on a single train. To accommodate the impact of this phenomenon in calculating metro capacity, a loading diversity factor has been adopted in many transportation studies. However, the underlying causes behind the uneven nature of carriage loading have rarely been examined in a systematic manner. In particular, there has been no trial to explain the nature of choice within a framework for individual passengers. Under the assumption that the uneven selection might stem from each passenger’s intrinsic preference for a specific car, the present study established a nested logit model to investigate the potential factors affecting the choice of a specific car on a train. Passengers were interviewed as they boarded from the platforms of line 7 of the Seoul Metro during the morning peak hours. Results show that the motivation to minimize the walking distance at destination stations turned out to be the most decisive in determining a passenger’s choice for a specific car of a train.
This work presents a behavioural model based on discrete choice models (DCMs) in order to simulate decision maker behaviour during an emergency situation. The proposed model is a probabilistic exit choice model that allows understanding the heterogeneity of the different decision maker's tastes. A stated preference survey was designed and realized with a sample of decision makers in order to achieve this objective. Thus, the obtained data was used to model the optimal mixed logit model. The use of DCMs allows to know the behaviour of different decision maker types during an evacuation process. The case study highlights the importance of the influence of other decision makers on the decision-making process. This work could be used as the starting point in the development of behavioural models which could be implemented in the current tools for the simulation of emergency evacuation.
This paper examines the integrated optimization of train stopping plan and seat allocation scheme in railway systems, where equilibrium passenger travel choice under elastic demand is considered. The integrated optimization problem is formulated as a non-concave and non-linear mixed-integer mathematical model, where the objective is to maximize the system net benefit considering ticket revenue, consumer surplus, and cost associated with train stoppings. The integrated optimization model can be reformulated into a mixed-integer linear programming (MILP) model based on a series of linearization, relaxation, and outer-approximation techniques, which can then be solved by commercial MILP solvers (e.g., GUROBI). We also compare the integrated optimization approach with that when the train stopping plan and seat allocation are optimized separately and identify the potential benefits. Numerical studies have been conducted on a small-scale example, the Zhengzhou-Xi’an and Shanghai-Beijing high-speed railway corridors to illustrate the proposed model and solution approach.
In some megacities, commuter metro lines that connect suburbs and downtown areas always suffer from serious non-equilibrium passenger congestion at stations during rush hours, bringing huge operation risks. To balance the non-equilibrium passenger congestion on overcrowded metro lines, this study aims to jointly optimize the train timetable and stop plan from the perspective of transportation safety in which some effective skip-stop patterns are adopted to allocate necessary train capacities to overcrowded stations with consideration to time-varying and elastic demand. We formulate the problem of interest as an integer linear programming model where the total passenger gathering risk, passenger waiting time, passenger riding time, and loss of ticket income are all included in the objective function. An efficient iterated local search (ILS) algorithm is designed for the convenience of solving large-scale models. Numerical experiments for a series of small-scale and real-world case studies are conducted to test the performance of the proposed methods. The computational results show that the designed algorithm can obtain high-quality train operation schemes in a much shorter time, which can greatly improve transportation safety by balancing the number of stranded passengers at any station.
In this study, we proposed a generic Bayesian optimization (BO) framework to solve congestion pricing problems. In the BO framework, the Gaussian process (GP) serves as a surrogate model to approximate the highly nonlinear and expensive-to-evaluate objective functions. This study reveals that GP exhibits an instability phenomenon, which inherently limits the accuracy of BO. We investigate the sources and influences of instability from the perspective of error analysis, and then propose an improved GP (IGP) model to address the instability issue. The associated improvements are twofold: matrix inversion and matrix multiplication. A tailored preconditioner is developed to reduce the matrix inversion errors. To address multiplication errors, a tailored dot product algorithm in conjunction with a GP reformulation scheme is proposed. To validate the proposed models and methods, a link-based second-best congestion pricing problem is considered as an example. The results indicate that, in comparison to benchmark approaches (the sensitivity analysis method and genetic algorithm), the proposed BO framework shows higher computational efficiency and solution accuracy. With modifications on GP, the instability phenomenon is substantially mitigated in several instances, hence enhancing the accuracy of the BO framework.
Urban rail transit networks face frequent delays and oversaturation resulting from unexpected disturbances and high demands during rush hours, which inevitably leads to unstable train operations, poor service experiences, and potential accidents on crowded platforms. This paper explores the train regulation and passenger flow control strategies for large-scale urban rail transit networks. Specifically, we develop a mixed-integer nonlinear programming (MINLP) formulation to improve the punctuality and regularity in train operations, reduce the passenger waiting time, and alleviate the passenger flow burden of platforms, where the dynamic coupling interactions among network-wide passengers, trains and stations are systematically considered. To reduce the computational complexity and satisfy real-time requirements, we particularly devise a real-time optimization approach based on iterative nonlinear programming (INP) combined with Lagrangian relaxation (LR) under the rolling horizon (RH) framework, which effectively disposes of the intractable nonconvexity and constructs the desirable properties of decomposability and parallelism. The original problem is transformed into a sequence of line-level subproblems that can be solved quickly, so as to circumvent the computational burden. Moreover, to verify the effectiveness of our approach, we apply it to address numerical examples on a simplified network and the real-world Beijing Subway network. The realistic case studies illustrate that the proposed approach can reduce the train deviation from the original timetable, the passenger waiting time, and the passenger accumulation risk on the platform, by around 84.92%, 30.34% and 61.42% in comparison with the common rule-based strategy. Additionally, the computation time for the realistic large-scale experiment is acceptable for a real-time implementation.
This paper proposes a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme, i.e., both plug-in fast charging and battery-swapping charging modes are incorporated in a single charging station. To accommodate the uncertain battery energy consumption during bus operation, a two-stage stochastic program is formulated, where the first stage decision determines the battery inventory level of each station and the second stage determines the charging mode and designs when, where, and how long each bus should be charged. Future uncertainties associated with energy consumption are captured by a set of possible discrete scenarios from historical data. A progressive hedging algorithm is developed to decompose the two-stage stochastic program into sub-problems. A case study is conducted to verify the proposed models and solution algorithms.
Boarding and alighting process is the most complicated behaviour at urban railway stations, and significantly affects the service level and evacuation capacities of stations. This paper quantitatively discusses the individual and group behavioural characteristics of both boarding and alighting passengers. The time headways are extracted from surveillance videos. Based on the data analysis results, an improved cellular automata model is proposed to describe the microscopic dynamics of the boarding and alighting process. In this model, three categories of floor fields are defined to describe passenger behaviour. The number of alighting passengers is included to quantitatively describe the boarding and alighting pattern. The time headway is also used to define the following behaviour with leaders. The model is validated and calibrated from several aspects by real data. The proposed model comprehensively reveals the boarding and alighting dynamics, and it can potentially be used to make crowd management in subway stations.
Evacuation path of pedestrians at subway station can directly affect the evacuation efficiency, and then affect the service level of the station. A Fisk stochastic user equilibrium model considering the congestion factor is established to assist evacuation path planning, which could avoid the bottleneck area and dangerous situation in advance. By comparing the field statistical data with simulation data of the number of pedestrians at the entrance gate of subway station, it is verified that the social force model and the minimum cost model can basically reproduce the movement law and path selection behavior of pedestrians at the subway station. Simulation experiment is carried out to compare the evacuation efficiency at the subway station under the stochastic user equilibrium model and the minimum cost model. The results indicate that the number of pedestrians at each gate is relatively balanced which can avoid overcrowding under the stochastic user equilibrium model. The total evacuation time can be reduced by 15%, and the individual evacuation time can be saved about 0.57 min. By comparing the 3D overall pedestrian trajectory and the 2D local pedestrian trajectory, it is found that the stochastic user equilibrium model is of great significance for evacuation path optimization at the subway station. The quantitative relationship between the number of pedestrians and evacuation time at the subway station is given through regression analysis under stochastic user equilibrium model and minimum cost model. The suspension of escalators can obviously result in congestion at the gate and increase the total evacuation time. When pedestrian density at the station is low, closing a certain exit can significantly improve the pedestrian traffic efficiency at the gate and greatly reduce the total evacuation time. The stochastic user equilibrium evacuation path planning model constructed in this paper provides a guidance strategy for pedestrian evacuation, which could improve the efficiency and safety of subway evacuation system.
With its high infection rate, COVID-19 has swept the globe and brought great challenges to social life and economies. As an essential form of public transportation, the Beijing subway plays an important role in transportation systems. In traditional subway organizations, all one-sided doors of a train carriage are employed for passengers’ alighting and boarding. A higher risk of COVID-19 infections may be attributed to inevitable bidirectional conflicts at doors with higher passenger volumes. Moreover, quantitative analyses for this problem and corresponding solutions are, limited in recent studies. In this research, conflicts at carriage doors are analyzed using a cellular automaton (CA) based model. Four schemes to separate alighting and passenger boarding into separate doors are investigated. The performances of different schemes with various alighting and boarding passenger ratios are simulated with the software package Legion Studio. Both macroscopic and microscopic parameters to characterize passenger conflicts are obtained for analysis. The separation of alighting and boarding passenger flows yields the desired reduction in bidirectional conflicts, which further limits the probability of infectious disease exposure. This is an important reference to improve current practices and provide specific measurements of passenger organization under abnormal situations.
With the growing urban population and its rapid growth of mobility needs, metro systems often suffer from congestion in peak hours in many mega-cities over the world. This incurs severe travel delays for commuters and safety risks for metro operators. Hence, passenger flow management and control becomes an essential way to reduce station congestion during high-peak hours. This paper investigates the passenger flow control problem with the objective of increasing the number of boarding passengers. Considering the scenario that the destination of each passenger entering the station is unknown, a flow control problem with dynamic and station-based constraints is proposed to dynamically determine the number of passengers boarding each train at each station. Compared with existing flow control strategies, this model can improve the equity for boarding passengers of different OD pairs. The station-based flow control problem is formulated as a complicated nonlinear nonconvex quadratic programming model. To solve the intractable nonlinear programming model, we reformulate it into the dynamic programming formation and develop two efficient heuristic algorithms to solve it. We carry out two sets of numerical experiments, including the small-scale case with synthetic data and the real-world case with the operation data of Beijing metro system, to evaluate the performance of our model and algorithms. Several performance indicators, e.g. average waiting time and Gini coefficient, are presented to verify the efficiency and fairness of proposed model. The numerical results applied to Beijing urban subway network indicate that our approach can reduce the passengers’ waiting time and the line-level Gini coefficient by 5.21% and 23.52% compared with the benchmark flow control strategy with maximum loading and station-based constraints.
Existing methods to improve the operational efficiency and service level of urban rail transit (URT) systems focus on forecasting passenger flow in macro networks and optimizing metro vehicle operation strategies. Among the factors affecting passenger comfort in vehicles, the passenger arrival distribution is the most direct but is not sufficiently taken into account. This study provides a riding guidance method for passengers on the URT platform to improve the uniformity of the passenger distribution among carriages. First, a passenger arrival distribution model is proposed on the basis of the influences of the queue length at the door, the number of people in the carriage and the travel distance on the passengers’ choice of carriage. Second, taking the maximum spatial comfort degree of every passenger as an objective, a passenger riding guidance model on the URT platform is proposed. Hefei Metro Line 2 is taken as an example for the numerical case study, and the results show that the guidance method can balance the passenger flow distribution and improve the spatial comfort level of passengers when the passenger flow is approximately 65 to 135 persons per 5 min.
With the extensive construction of rail tunnels, the subway has gradually become the most widely used means of public transport. As important transportation hubs, the evacuation capacity of subway stations is especially significant. An accurate prediction model can efficiently reduce the evacuation time, and the social force (SF) model is one of the most widely used evacuation models. Almost all past research based on the SF model has focused on a single model for pedestrians. However, in this research, based on different force models, a crowd is divided into three categories that have different kinds of movement based on different force models. The first category of pedestrians, namely luggage-laden pedestrians, is characterized by a lower velocity and large radii. The second category of pedestrians is ordinary pedestrians. The final category of pedestrians comprises panic pedestrians, who choose an angle at which to overtake the pedestrians in front of them. Simulations are performed based on different proportions of these three categories of pedestrians, and it is found that with the increase of the proportions of luggage-laden and panic pedestrians, the evacuation time is also increased; this phenomenon could not be observed with the original SF model. The performance of the modified SF model is then tested in a passage with a bottleneck, and it is found that the interaction between panic pedestrians and luggage-laden pedestrians greatly increases the difficulty of evacuation. The number of luggage-laden pedestrians is found to be the controllable factor that leads to an increase of the evacuation time at the evacuation bottlenecks. Therefore, solutions like warning signs at evacuation bottlenecks should be implemented.
Passenger alighting and boarding time is an important factor for crowded metros with high-frequency operations. Some operators leave the train doors open until the last passenger boards or passengers voluntarily stop boarding, while others regulate the boarding process. To evaluate whether it is effective to regulate the passenger while boarding or not, this research explores how the density inside the train would influence the passenger boarding rate by conducting a laboratory experiment. The results showed that the passenger boarding rate increased as the density increased, up to approximately 2.5–3.0 passengers/m². Beyond that point, our results suggested that the boarding rate may have decreased, but the evidence was not conclusive. It is deduced that, under the experimented density (up to around 5 passengers/m²), the density does not have any apparent negative effects on the boarding rate, which implies that there is no strong evidence to recommend passenger boarding regulation.
Urban metro system is one of the most sustainable travel modes that affords lots of travel demands in the large cities. The boarding and alighting process is a special form of the bi-directional pedestrian flow through bottleneck, which displays complicated nonlinear dynamics. Quantitatively investigating the influence of human behavior on the boarding and alighting process is the challenging problem. To further analyze the crowd dynamics, the surveillance videos at platforms of several urban metro stations in Beijing were recorded, and the time interval of each passenger passing the train door was extracted. According to the extracted individual movement data, the time headway between each two adjacent passengers and the burst size were extracted and analyzed by statistics approaches. The concept of order degree was proposed to describe the activity pattern of a boarding and alighting process. The relationships between these factors including burst size, order degree, and time gap were explored by quantitatively analyzing the individual data. The probability density function of the time headway follows the positively skewed distribution, and the complementary cumulative distribution function shows the property of the power-law distribution. The relationship between burst size and time headway could be divided into three phases. By finding the time gap between the first alighting passenger and the first boarding passenger, the passenger activity under different time pressure was investigated. The obtained individual passenger behavior characteristics could be potentially applied to estimate and design the dwell time and improve the system sustainability.
Concentrated boarding describes the phenomenon when rail passengers congregate in certain areas of the platform and board the train carriages that stop near these areas. This influences the distribution of passengers throughout the carriages, which can negatively affect passenger comfort, safety at the platform-train interface, efficiency of the rail network, and the reputation of rail travel as a whole. This project aimed to determine whether concentrated boarding occurs in stations in the UK in order to understand its relevance for future rolling stock, infrastructure design and its associated manufacturing research. Video recording technology was used to observe the movements of passengers in Oxford Station and data was collected for nine individual trains. By reviewing the recordings, the number of passengers boarding through each door of the trains was determined, and the boarding distribution along the length of the platform was plotted. Several reasons for noted trends are offered, and potential solutions proposed. The use of real time information could be invaluable to minimise concentrated boarding, as it would allow passengers to make informed decisions as to where they could board trains to have a better journey experience. These findings indicate the relevance of a human-centred design process, particularly the user research stages, in the process of defining priorities for manufacturing and engineering.
Pedestrian simulation approach has been widely used to reveal the human behavior and evaluate the performance of crowd evacuation. In the existing pedestrian simulation models, the social force model is capable of predicting many collective phenomena. Detour behavior occurs in many cases, and the important behavior is a dominate factor of the crowd evacuation efficiency. However, limited attention has been attracted for analyzing and modeling the characteristics of detour behavior. In this paper, a modified social force model integrated by Voronoi diagram is proposed to calculate the detour direction and preferred velocity. Besides, with the consideration of locations and velocities of neighbor pedestrians, a Logit-based choice model is built to describe the detour direction choice. The proposed model is applied to analyze pedestrian dynamics in a corridor scenario with either unidirectional or bidirectional flow, and a building scenario in real-world. Simulation results show that the modified social force model including detour behavior could reduce the frequency of collision and deadlock, increase the average speed of the crowd, and predict more practical crowd dynamics with detour behavior. This model can also be potentially applied to understand the pedestrian dynamics and design emergent management strategies for crowd evacuations.
In the subway platform, not all passengers distribute randomly but gather in the waiting areas, especially when a train is coming. During emergency evacuations, passengers’ initial distribution may play a significant role in affecting the escape efficiency. In this paper, a passenger distribution modelling method is proposed to predict such waiting area choice processes based on ant colony optimization (ACO) algorithm, which is really a complicated job due to many influence factors. The model considers the distance to the target waiting area, the length of queues, the physical length of waiting areas and the train schedule as four main influence factors. Specially, a modification of the passenger’s impatience factor in the famous social force model (SFM), better reflecting the change of psychological states with an arrival of a train, is presented. The field data collected at the Xuanwumen subway platform is utilized for the model calibration and validation. The ultimate simulation results demonstrate that passenger distributions based on ACO algorithm basically can reflect the field distribution and also the dynamic characteristics of waiting area choice processes. Impacts of passenger distributions on evacuation dynamics under fires are further studied based on the software FDS+Evac. The results indicate that passenger distribution does has little impact on evacuation efficiency when fires are not very large, while the evacuation will be affected significantly by passenger distributions once fires are large enough. This further indicates the necessity of studying the passenger distribution at the subway platform especially under emergencies.
This paper proposes a new nonlinear distance-based transit fare structure, which is measured by a function of the Euclidean distance between the origin and destination stations, termed as Origin-Destination (OD)-based fare. The novel fare structure encourages passengers to freely choose the most efficient trip plan. An optimization model is formulated based on a three-party game (involving the transport authority, transit company, and passenger) to determine the optimal fare function and frequency. An artificial bee colony algorithm is adopted to solve the model. Finally, a numerical example is provided to verify the proposed method.
This paper addresses an innovative concept, termed as queuing passengers’ willingness to board (WTB) the transit vehicles. In the peak hours, some queuing passengers cannot board a crowded bus/train, but when the same vehicle arrives at the next stop, some other passengers could still get on. This phenomenon reflects that passengers at different queuing locations have heterogeneous level of ambitions to board. A methodological framework is proposed for the quantitative investigation of WTB. First, a general model is proposed, together with a new least square method (LSM) for the calibration. Then, a parametric model is developed, which is also calibrated by the LSM. To refine the calibration method and deal with the biasness of survey data, a weighted least square method is further developed. Based on real survey data, the calibration results clearly support the existence of WTB, which can be used to estimate the capacity of transit vehicles. This paper also sheds some lights on the practical applications of the quantitative WTB.
Microscopic simulation of pedestrian traffic is an important and increasingly popular method to evaluate the performance of existing or proposed infrastructure. The social force model is a common model in simulations, describing the dynamics of pedestrian crowds given the goals of the simulated pedestrians encoded as their preferred velocities. The main focus of the literature has so far been how to choose the preferred velocities to produce realistic dynamic route choices for pedestrians moving through congested infrastructure. However, limited attention has been given the problem of choosing the preferred velocity to produce other behaviors, such as waiting, commonly occurring at, e.g., public transport interchange stations. We hypothesize that: (1) the inclusion of waiting pedestrians in a simulated scenario will significantly affect the level of service for passing pedestrians, and (2) the details of the waiting model affect the predicted level of service, that is, it is important to choose an appropriate model of waiting. We show that the treatment of waiting pedestrians have a significant impact on simulations of pedestrian traffic. We do this by introducing a series of extensions to the social force model to produce waiting behavior, and provide predictions of the model extensions that highlight their differences. We also present a sensitivity analysis and provide sufficient criteria for stability.
This paper investigates crowding effect on the path choice of metro passengers. We show people reroute not only to avoid the delay from crowding but also to evade crowding itself. More specifically, a logit model fits best when it uses the transit delay from crowding as well as the passenger load of a connection in addition to the conventional explanatory variables. Also, we demonstrate that crowding decreases the overall welfare of metro passengers. The model is tested on the real path choice data acquired by the recent algorithm by Hong et al. (2015) known to detect the real path choice from Smart Card data in more than 90% of the cases.
The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it).
The value of a pedestrian stream simulation depends on its ability to reproduce natural behaviour of pedestrians in different situations. Most models assume that pedestrians are single-minded and constantly move towards their destinations. However, our observations at two major German railway stations made during field experiments and our analysis of video recordings at one of these stations revealed that in virtually every setting a significant proportion of pedestrians do not walk continuously. Instead, they occasionally change their route in order to visit certain locations and stand there for a period of time. By waiting, they often block walking pedestrians and thereby influence the overall dynamics.
In this paper, we evaluate the impact of waiting pedestrians and propose a model for waiting pedestrians based on cellular automata. The model is able to reproduce the observed pedestrian behaviour. We illustrate the model with simulations of several real life scenarios for a major German railway station and show that during rush hour standing pedestrians may prolong walking time by up to nearly 20%. We also demonstrate how the developed model can be used for the analysis of infrastructures, and prediction of problematic areas in public spaces.
Currently, pedestrian simulation models are used to predict where, when and why hazardous high density crowd movements arise. However, it is questionable whether models developed for low density situations can be used to simulate high density crowd movements. The objective of this paper is to assess the existent pedestrian simulation models with respect to known crowd phenomena in order to ascertain whether these models can indeed be used for the simulation of high density crowds and to indicate any gaps in the field of pedestrian simulation modeling research.This paper provides a broad, but not exhaustive overview of the crowd motion simulation models of the last decades. It is argued that any model used for crowd simulation should be able to simulate most of the phenomena indicated in this paper. In the paper cellular automata, social force models, velocity-based models, continuum models, hybrid models, behavioral models and network models are discussed. The comparison shows that the models can roughly be divided into slow but highly precise microscopic modeling attempts and very fast but behaviorally questionable macroscopic modeling attempts. Both sets of models have their use, which is highly dependent on the application the model has originally been developed for. Yet, for practical applications, that need both precision and speed, the current pedestrian simulation models are inadequate.
A model for traffic flow is developed by treating the traffic stream as a continuous fluid. Fluid dynamic principles are then used to derive relations between speed, density, and flow.
We propose a discrete choice framework for pedestrian dynamics, modelling short term behavior of individuals as a response to the presence of other pedestrians. We use a dynamic and individual-based spatial discretization, representing the physical space. We develop a model predicting where the next step of a walking pedestrian will be, at a given point in time. The use of the discrete choice framework is justified by its flexibility, the capacity to deal with individuals and the compatibility with agent-based simulation. The model is calibrated using data from actual pedestrian movements, manually taken from video sequences. We present two different formulations: a cross-nested logit and a mixed nested logit. In order to verify the quality of the calibrated model, we have designed and developed a pedestrians simulator.