The use of gait parameters to evaluate pedestrian behavior at scramble phase signalized intersections

Pedestrian scramble phasing is usually implemented to reduce pedestrian‐vehicle conflicts and therefore increase the safety of the intersection. However, to adequately determine the benefits of scramble phasing, it is necessary to understand how pedestrians react to such an unconventional design. This study investigates changes in pedestrian crossing behavior following the implementation of a scramble phase by examining the spatiotemporal gait parameters (step length and step frequency). This detailed microscopic‐level analysis provides insight into changes in pedestrian walking mechanisms as well as the effect of various pedestrian and intersection characteristics. The study uses video data collected at a scramble phase signalized intersection in Oakland, California. Gait parameters were found to be influenced by pedestrian gender, age, group size, crosswalk length, and pedestrian signal indications. Both average step length and walking speed were significantly higher for diagonally crossing pedestrians compared with pedestrians crossing on the conventional crosswalks. Pedestrians were found to have the tendency to increase their step length more than their step frequency to increase walking speed. It was also found that, compared with men, women generally increase their walking speed by increasing their step frequency more than step length. However, when in non‐compliance with signal indications, women increase their walking speed by increasing their step length more than step frequency. It was also found that older pedestrians do not significantly change their walking behavior when in non‐compliance with signal indications. Copyright © 2014 John Wiley & Sons, Ltd.     

Waiting zones for realistic modelling of pedestrian dynamics: A case study using two major German railway stations as examples

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.     

Validation of an agent-based microscopic pedestrian simulation model in a crowded pedestrian walking environment

This study validates a recently developed agent-based pedestrian micro-simulation model in a crowded walking environment. The model is applied to simulate pedestrian movements at a major street in the downtown Vancouver area. The street was closed for traffic to allow people attending a social event to leave the area safely. The calibration of model parameters is conducted using a Genetic Algorithm that minimizes the error between simulated and actual trajectories, acquired by means of computer vision. Validation results confirm the accuracy of the simulated trajectories, as the average error between the actual and simulated trajectories is found to be 0.28?m, and the average error in walking speed is just 0.06 m/s. Furthermore, results show that the model is capable of reproducing the actual behavior of pedestrians during different interactions with high accuracy (more than 94% for most interactions).     

A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities

This paper presents a hybrid simulation-assignment modeling framework for studying crowd dynamics in large-scale pedestrian facilities. The proposed modeling framework judiciously manages the trade-off between ability to accurately capture congestion phenomena resulting from the pedestrians’ collective behavior and scalability to model large facilities. We present a novel modeling framework that integrates a dynamic simulation-assignment logic with a hybrid (two-layer or bi-resolution) representation of the facility. The top layer consists of a network representation of the facility, which enables modeling the pedestrians’ route planning decisions while performing their activities. The bottom layer consists of a high resolution Cellular Automata (CA) system for all open spaces, which enables modeling the pedestrians’ local maneuvers and movement decisions at a high level of detail. The model is applied to simulate the crowd dynamics in the ground floor of Al-Haram Al-Sharif Mosque in the City of Mecca, Saudi Arabia during the pilgrimage season. The analysis illustrates the model’s capability in accurately representing the observed congestion phenomena in the facility.     

Specification and calibration of a microscopic model for pedestrian dynamic simulation at signalized intersections: A hybrid approach

Macroscopic pedestrian models for bidirectional flow analysis encounter limitations in describing microscopic dynamics at crosswalks. Pedestrian behavior at crosswalks is typically characterized by the evasive effect with conflicting pedestrians and vehicles and the following effect with leading pedestrians. This study proposes a hybrid approach (i.e., route search and social force-based approach) for modeling of pedestrian movement at signalized crosswalks. The key influential factors, i.e., leading pedestrians, conflict with opposite pedestrians, collision avoidance with vehicles, and compromise with traffic lights, are considered. Aerial video data collected at one intersection in Beijing, China were recorded and extracted. A new calibration approach based on a genetic algorithm is proposed that enables optimization of the relative error of pedestrian trajectory in two dimensions, i.e., moving distance and angle. Model validation is conducted by comparison with the observed trajectories in five typical cases of pedestrian crossing with or without conflict between pedestrians and vehicles. The characteristics of pedestrian flow, speed, acceleration, pedestrian-vehicle conflict, and the lane formation phenomenon were compared with those from two competitive models, thus demonstrating the advantage of the proposed model.     

A mesoscopic model for large-scale simulation of pedestrian dynamics

A mesoscopic pedestrian model is proposed, considering pedestrians as individuals and describing their movement by means of aggregate density-flow relationships. The model builds on a stochastic process, describing transition rates among adjacent sites on a lattice. Each lattice can contain several pedestrians. The approach is minimal and fast to simulate, and, by construction, capable of capturing population heterogeneity as well as variability in walking behaviour and en-route path choice. The model is more efficient than microscopic models, and potentially more accurate than macroscopic ones. We calibrate and validate the model using real data and carry out several numerical experiments to present its key properties and possible applications for simulation of large-scale scenarios.     

Assessing the usage and level-of-service of pedestrian facilities in train stations: A Swiss case study

A framework for assessing the usage and level-of-service of rail access facilities is presented. It consists of two parts. A dynamic demand estimator allows to obtain time-dependent pedestrian origin–destination demand within walking facilities. Using that demand, a traffic assignment model describes the propagation of pedestrians through the station, providing an estimate of prevalent traffic conditions in terms of flow, walking times, speed and density. The corresponding level-of-service of the facilities can be directly obtained. The framework is discussed at the example of Lausanne railway station. For this train station, a rich set of data sources including travel surveys, pedestrian counts and trajectories has been collected in collaboration with the Swiss Federal Railways. Results show a good performance of the framework. To underline its practical applicability, a six-step planning guideline is presented that can be used to design and optimize rail access facilities for new or existing train stations. In the long term, the framework may also be used for crowd management, involving real-time monitoring and control of pedestrian flows.     

Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation

Although various theories have been adopted to develop reliable pedestrian walking models, a limited effort has been made to calibrate them rigorously based on individual trajectories. Most researchers have validated their models by comparing observed and estimated traffic flow parameters such as speed, density, and flow rate, or replaced the validation by visual confirmation of some well-known phenomena such as channelization and platooning. The present study adopted maximum likelihood estimation to calibrate a social-force model based on the observed walking trajectories of pedestrians. The model was assumed to be made up of five components (i.e., inertia, desired direction, leader–follower relationship, collision avoidance, and random error), and their corresponding coefficients represented relative sensitivity. The model also included coefficients for individual-specific characteristics and for a distance-decay relationship between a pedestrian and his/her leaders or colliders. The calibration results varied with the two density levels adopted in the present study. In the case of high density, significant coefficient estimates were found with respect to both the leader–follower relationship and collision avoidance. Collision avoidance did not affect the pedestrian’s walking behavior for the low-density case due to channelization. The distance limit was confirmed, within which a pedestrian is affected by neighbors. At the low-density level, by comparison with women, men were found to more actively follow leaders, and pedestrians walking in a party were found to be less sensitive to the motion of leaders at the high-density level.     

Application of social force model to pedestrian behavior analysis at signalized crosswalk

Limited pedestrian behavior models shed light on the case at signalized crosswalk, where pedestrian behavior is characterized by group or individual evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, and response to signal control and crosswalk boundary. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis at signalized intersection. The social force theory has been employed and adjusted for this purpose. The parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Last, the model performance was confirmed in light of individual trajectory comparison between estimation and observation, passing position distribution at several cross-sections, collision avoidance behavior with conflicting vehicles, and lane-formation phenomenon. The simulation results also concluded that the model enables to visually represent pedestrian crossing behavior as in the real world.     

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.     

Modifying pedestrian behaviour

The paper reports on research using a new type of pedestrian waiting countdown timer to influence pedestrian behaviour at signalised pedestrian crossings in Dublin. The aim was to evaluate the impact of the timers on pedestrian crossing behaviour and in particular to see if it had any impact on the number of illegal crossings (during red man––do not walk signal). The timers inform the pedestrian how many seconds they have to wait until the green man appears. Two surveys were used to evaluate the impact: an attitude survey to evaluate the perception of the users and a video survey to estimate quantitatively the impacts on pedestrian behaviour but also to evaluate the awareness levels of the pedestrians towards the countdown timers. Some of the results include observance of more compliant behaviour by females and that pedestrians tend to overestimate their waiting time. Before the timers were installed 65% of pedestrians started to cross during the green man and amber phases but this rose to 76% after the timers were installed.     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

Performance comparison between pedestrian push-button and pre-timed pedestrian crossings at midblock: a Korean case study

Walking has been highlighted as an independent transportation mode as well as an access/egress mode to/from public transit to encourage the use of more sustainable transport systems. However, walking does not seem to have priority over other transportation modes, especially in areas where various modes of movement are in conflict. The pedestrian push-button system seems to be a solution to distribute the right of way. The focus of this study is on the performance issue of the pedestrian push-button. Specifically, this study deals with issues related to mid-block crossings and attempts to answer two questions: whose waiting time is longer at pre-timed and push-button crossings, pedestrians, or vehicles? and which system – pre-timed or push-button – is better in terms of total waiting time? According to our simulation analyses, if the pedestrian flow rate is less than 120, 85, and 70 ped/h for two-, three-, and four-lane roads, respectively, the push-button system is recommended.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

Microscopic modeling of large‐scale pedestrian–vehicle conflicts in the city of Madinah,Saudi Arabia

This paper presents a micro‐simulation modeling framework for evaluating pedestrian–vehicle conflicts in crowded crossing areas. The framework adopts a simulation approach that models vehicles and pedestrians at the microscopic level while satisfying two sets of constraints: (1) flow constraints and (2) non‐collision constraints. Pedestrians move across two‐directional cells as opposed to one‐dimensional lanes as in the case of vehicles; therefore, extra caution is considered when modeling the shared space between vehicles and pedestrians. The framework is used to assess large‐scale pedestrian–vehicle conflicts in a highly congested ring road in the City of Madinah that carries 20 000 vehicles/hour and crossed by 140 000 pedestrians/hour after a major congregational prayer. The quantitative and visual results of the simulation exhibits serious conflicts between pedestrians and vehicles, resulting in considerable delays for pedestrians crossing the road (9 minutes average delay) and slow traffic conditions (average speed <10 km/hour). The model is then used to evaluate the following three mitigating strategies: (1) pedestrian‐only phase; (2) grade separation; and (3) pedestrian mall. A matrix of operational measures of effectiveness for network‐wide performance (e.g., average travel time, average speed) and for pedestrian‐specific performance (e.g., mean speed, mean density, mean delay, mean moving time) is used to assess the effectiveness of the proposed strategies. Copyright © 2012 John Wiley & Sons, Ltd.     

A macroscopic loading model for time-varying pedestrian flows in public walking areas

A macroscopic loading model applicable to time-dependent and congested pedestrian flows in public walking areas is proposed. Building on the continuum theory of pedestrian flows and the cell transmission model for car traffic, an isotropic framework is developed that can describe the simultaneous and potentially conflicting propagation of multiple pedestrian groups. The model is formulated at the aggregate level and thus computationally cheap, which is advantageous for studying large-scale problems. A detailed analysis of several basic flow patterns including counter- and cross flows, as well as two generic scenarios involving a corner- and a bottleneck flow is carried out. Various behavioral patterns ranging from disciplined queueing to impatient jostling can be realistically reproduced. Following a systematic model calibration, two case studies involving a Swiss railway station and a Dutch bottleneck flow experiment are presented. A comparison with the social force model and pedestrian tracking data shows a good performance of the proposed model with respect to predictions of travel time and density.     

Collection, spillback, and dissipation in pedestrian evacuation: A network-based method

We present a method of predicting pedestrian route choice behavior and physical congestion during the evacuation of indoor areas with internal obstacles. Under the proposed method, a network is first constructed by discretizing the space into regular hexagonal cells and giving these cells potentials before a modified cell transmission model is employed to predict the evolution of pedestrian flow in the network over time and space. Several properties of this cell transmission model are explored. The method can be used to predict the evolution of pedestrian flow over time and space in indoor areas with internal obstacles and to investigate the collection, spillback, and dissipation behavior of pedestrians passing through a bottleneck. The cell transmission model is further extended to imitate the movements of multiple flows of pedestrians with different destinations. An algorithm based on generalized cell potential is also developed to assign the pedestrian flow.     

Walking-distance introduced queueing model for pedestrian queueing system: Theoretical analysis and experimental verification

We have introduced the effect of delay in walking from the head of a queue to the service windows in the queueing model and obtain a suitable type of queueing system under various conditions by both computational simulation and theoretical analysis. When there are multiple service windows, the queueing theory indicates that mean waiting time in a fork-type queueing system (Fork), which collects pedestrians into a single queue, is smaller than that in a parallel-type queueing system (Parallel), i.e., queues for each service window. However, in our walking-distance introduced queueing model, we have examined that mean waiting time in Parallel becomes smaller when both the arrival probability of pedestrians and the effect of walking distance are large. Moreover, enhanced Forks, which shorten waiting time by reducing the effect of walking distance, are considered, and parts of our results are also verified by real queueing experiments.     

Potential-based dynamic pedestrian flow assignment

This study proposes a potential-based dynamic pedestrian flow assignment model to optimize the evacuation time needed for all pedestrians to leave an indoor or outdoor area with internal obstacles and multiple exits, e.g., railway station, air terminal, plaza, and park. In the model, the dynamic loading of pedestrian flows on a two-dimensional space is formulated by a cell transmission model, the movement of crowds is driven by space potential, and the optimization of evacuation time is solved by a proportional swapping process. In this way, the proposed model can be applied to not only efficiently optimize the evacuation process of a crowd with large scale but also recognize local congestion dynamics during crowd evacuation. Finally, a set of numerical examples are presented to show the proposed model’s effectiveness for optimizing crowd evacuation process and its application to design a class of variable guide sign systems.     

A pedestrian flow model considering the impact of local density: Voronoi diagram based heuristics approach

Local density, which is an indicator for comfortable moving of a pedestrian, is rarely considered in traditional force based and heuristics based pedestrian flow models. However, comfortable moving is surely a demand of pedestrian in normal situations. Recently, Voronoi diagram had been successfully adopted to obtain the local density of a pedestrian in empirical studies. In this paper, Voronoi diagram is introduced into the heuristics based pedestrian flow model. It provides not only local density but also other information for determining moving velocity and direction. Those information include personal space, safe distance, neighbors, and three elementary characteristics directions. Several typical scenarios are set up to verify the proposed model. The simulation results show that the velocity-density relations and capacities of bottleneck are consistent with the empirical data, and many self-organization phenomena, i.e., arching phenomenon and lane formation, are also reproduced. The pedestrians are likely to be homogeneously distributed when they are sensitive to local density, otherwise pedestrians are non-uniformly distributed and the stop-and-go waves are likely to be reproduced. Such results indicate that the Voronoi diagram is a promising tool in modeling pedestrian dynamics.