Inclusion of environmental effects in steering behaviour modelling using fuzzy logic
Introduction
Local path determination, known as steering, is a spatial behaviour that is based upon the cognitive process for choosing the next step location (Gibson, 2009). This spatial cognition is important, as it enables an individual to perform activities in relation to the environment, especially in steering and navigation tasks. The cognitive process involved includes obtaining sensory information from the surroundings, and interpreting the obtained information to understand the environment in order to implement the next course of action. Psychologists have named the outcome of this cognitive process ‘environmental perception’ (Downs & Stea, 2005). Researchers in various disciplines, such as architecture (Hidayetoglu, Yildirim, & Akalin, 2012), environmental design (Samarasekara, Fukahori, & Kubota, 2011), and computer science (Raubal & Worboys, 1999), have developed a range of frameworks with respect to environmental perception. The process by which the energy from physical objects received by a pedestrian which is then converted into perceptions of physical items such as desks, couches, walls, and buildings, is an open question. This paper provides a method to estimate the environmental effects on the steering behaviours of a pedestrian from the engineering perspective.
Garling and Evans (1991) believed that visual perception is necessary to relate the physical environment with spatial actions. They argued that an individual’s perception from the physical surroundings directs his/her spatial actions. Lynch (1960) showed that pedestrians obtain and perceive information from the immediate environment to implement locomotion. Interaction with the surrounding environment and physical stimuli from objects within the built environment provides an internal motivation for pedestrians to choose the next step position. In a recent study by Lee, Yang, and Lin (2012), the authors pointed out that social, psychological, and physical stimuli are the internal motivational parameters for various movements by pedestrians. In their study, this motivation was represented by a measurement analogue to the Newtonian force.
Gwynne, Galea, Owen, Lawrence, and Filippidis (1999) conducted an extensive research related to different modelling approaches to emergency actions within a built environment. They highlighted four major interacting modelling factors, viz., environment, environmental configuration, behaviour, and procedures. Padgitt and Hund (2012) examined the relationship between the sense of direction and wayfinding efficiency in a complex built environment. In the study of Fajen and Warren (2003), how information exchange with the surrounding environment controls a pedestrian’s locomotion tasks was described. The locomotion task is an action that originates from an imperfect observation of the surrounding space, and it leads to imprecise and vague knowledge of the environment (Raubal & Worboys, 1999). Lynch (1960), a pioneer in path-finding research, described a pedestrian’s uncertain knowledge of the surroundings in his/her mental image of the environment with fuzzy logic-based features.
According to recent studies by Ma, Song, Fang, Lo, and Liao (2010) and Hidayetoglu et al. (2012), environmental design is one of the indoor environmental factors that directly affects a pedestrian’s spatial perception and orientation. These authors identified that further research is required to understand how, and to what extent, environmental design influences the spatial orientation. In this paper, our study concentrates on the prediction of the next step location, whereby influences from the environmental objects are taken into account. A specific gap in research related to walking path prediction is how a pedestrian chooses his/her next step position and speed when he/she is exposed to environmental stimuli during a normal and non-panic situation. As a result, the main objective of this paper is to introduce a modelling approach that considers diverse and subjective nature of the environmental perception by different pedestrians while moving through indoor areas. The following section discusses the related work and justifies the application of fuzzy logic models for pedestrian walking path prediction.
Understanding a pedestrian’s perception of the physical environment is largely based upon empirical studies that postulate how different features of the environment, such as landmarks, routes, and configuration, are integrated to build the environmental knowledge and influence the pedestrian’s perception towards the environment (Golledge et al., 1993, Hidayetoglu et al., 2012). Golledge et al. (1993) observed that information acquired from the surroundings is fuzzy, and is subject to a variety of variables. The relationship between a pedestrian’s perception and his/her displacement was studied by Wineman and Peponis (2010), with the aim to forecast the pedestrian’s movement. In another study, dynamic interactions between a pedestrian and the environment, which incorporate mobile devices to assist in urban wayfinding, was suggested (Li, 2006). Similar to the pedestrian’s steering behaviour, a driver’s steering model was developed using a fuzzy preference relation method (Ridwan, 2004). In another related work, a fuzzy type II model that addresses the driver’s behaviour in high speed signalised intersections was proposed (Hurwitz, Wang, Knodler, Ni, & Moore, 2012).
In a study on crowd simulation (Li, Li, & Liang, 2012), a new approach integrating fuzzy logic with a data-driven method was introduced. The Modified Learning From Example (MLFE) method is applied to extract behavioural rules from state-action data samples extracted from video footage. Tome, Bonzon, Merminod, and Aminian (2008) introduced a fuzzy classifier for Pedestrian Dead Reckoning (PDR) navigation. The classifier combines biomechanical principles with fuzzy logic for recognising a pedestrian’s walking behaviour with 3D replacement. In the proposed method, the stride length was calculated by using a simple inverse pendulum model, and a fuzzy logic classifier was proposed to classify the walking behaviour in the broader range of 3D displacement. In the field of pedestrian evacuation, a Takagi–Sugeno type of fuzzy model was used to represent pedestrian dynamics in crowd evacuation behaviours (Zhu, Liu, & Tang, 2008). The main idea is to convert the observed behaviours of the crowd during evacuation to mathematical models based on fuzzy logic approaches.
On the other hand, fuzzy rule-based modelling has been implemented successfully in robot path planning and navigation problems. In this context, a fuzzy rule-based model for robot path planning around a terrain was developed (Seraji & Howard, 2002). Three navigation behaviours, i.e., seek-goal, obstacle avoidance, and traverse terrain, were defined using a fuzzy logic approach. Similar to the robot path planning approach, in the domain of science, such as psychology, social science and biology, the behaviours of complex systems have been observed by a human expert, which were transformed into a linguistic description of the phenomenon. Tron and Margaliot (2004) addressed the application of a fuzzy logic approach to building a mathematical model based on the linguistic description of the observer.
The aforementioned studies advocate that fuzzy logic is an appropriate and reliable approach to explore and represent the heterogeneous nature of a pedestrian’s perception–reaction during the steering process. Recent research from different fields, such as robotic, artificial intelligence, geospatial, and navigation, put emphasis on trajectory prediction and utilise this information in various applications, which include providing rich context information (Liu & Karimi, 2006) and location-based information services to complete path finding activities for mobile devices (Li, 2006). In this study, our aim is to consider the use of fuzzy logic for prediction of a pedestrian’s walking path within a built environment. More specifically, the proposed model connects spatial configuration to navigation performance, and associates the impacts of the surrounding environment with the walking path. The next section provides an overview of the proposed approach.
In this paper, a fuzzy logic-based approach is proposed to model a pedestrian’s steering behaviours in an indoor environment. We postulate that, during pedestrian–environment interaction, one’s perception is an imprecise and fuzzy concept. As such, the proposed fuzzy logic model accepts as its input the level of stimulation in three future positions within the pedestrian’s field of view. In addition, speed and step-length are two important input variables of the model. The input variables are subject to six membership functions, namely low, medium, and high in both attractive and repulsive forces. The fuzzy inference engine evaluates the inputs and makes a predicted output according to a rule matrix of 216 if–then rules. The output represents the turning angle (in degrees) for the next walking step. To verify and validate the model, an experimental study employing a motion tracking system for real data collection was conducted. The experiment was carried out on a physical layout, and data samples pertaining to the walking trajectories were collected. Then, the fuzzy logic model was used to predict the pedestrians’ walking paths under four different scenarios consisting of constant or variable speeds and step-lengths from heterogeneous pedestrians. Statistical error measurements were used to quantify the performances.
The key contributions of this study are: (1) employing the social force method to quantify the attractive or repulsive environmental stimulation as the steering force; (2) using a fuzzy logic approach to handle vagueness and uncertain information of pedestrians’ perceptions towards the surrounding environment; (3) engaging heterogeneous pedestrians with different speeds and step-lengths in the simulation model.
This paper is structured as follows. Section 2 presents the proposed modelling approach. Section 3 explains the principles of pedestrian–environment interactions. Section 4 discusses the environmental influences on a pedestrian’s steering behaviours, the structure of the proposed fuzzy logic model, and the simulation results. Section 5 details data collection along with statistical analysis of the results. Four different scenarios for model assessment are presented in Section 6. Concluding remarks are given in Section 7.
Section snippets
Modelling, simulation, and validation of the proposed approach
The existing methodologies for modelling and validation of a pedestrian’s steering behaviours comprise computer-based analysis (Helbing, Buzna, Johansson, & Werner, 2005), field observation (Pan, Han, Dauber, & Law, 2007), controlled laboratory experiment (Zacharias, 2001), and questionnaire (Borgers & Timmermans, 1986). Computer-based analysis focuses on computer simulation, while other methodologies require data collection and validation. Computer simulation is a convenient and effective
Pedestrian–environment interactions
There are three levels of interaction with the environment when a pedestrian performs an activity, namely following instinct or skill-based, following experience or rule-based, and bounded rationality or knowledge-based behaviours (Wills, 1998). Skill-based behaviours involve processes that are performed with little or no conscious effort. In this case, pedestrians react to the surrounding situation automatically by instinct. This automatic reaction is considered as a result of learning from
Environmental influences on pedestrian steering behaviour
Determining the walking path of a pedestrian in an indoor environment requires two essential factors, namely (i) environmental influences; (ii) imprecise and subjective perception from environmental stimuli. Influences from the areas within a pedestrian’s field of view are the inputs to the proposed model. Two indicators to show the level of influences from the environment, namely attractive or repulsive interaction, are considered, and three linguistic descriptions are deployed to describe the
Data collection
In practise, microscopic pedestrian data, especially trajectory data samples, are rarely collected due to the lack of demand. Hence, model validation is a major concern faced by many researchers in this area. From the literature review, we notice that most of proposed models lack calibration and validation with real data. Extracting useful and practical information from the collected data samples, such as walking trajectory, poses another challenge. This is because it is a tedious manual
Model validation
Initially, the proposed model was evaluated using a constant walking speed and step-length for all the pedestrians. During data analysis and performance comparison, we realised that the model should consider diverse pedestrians with different speeds and step-lengths; otherwise unrealistic and inaccurate results would be produced. Hence, different walking speeds and step-lengths were subsequently considered. Besides, the simulation time step should be approximately equal to the time required for
Conclusions
The main contribution of this paper is the proposal of a new method for modelling the pedestrian’s steering behaviours by utilising the fuzzy logic model. The effects of the surrounding environment, i.e., both attractive and repulsive forces that influence the pedestrian’s walking path, are taken into consideration in the proposed fuzzy logic model. Depending on the objects in the pedestrian’s vision field, the attractive or repulsive effects are adjusted dynamically to determine the subsequent
Acknowledgments
This research is fully supported by Australian Research Council Linkage Grant LP0776826 and Centre for Intelligent Systems Research (CISR) at Deakin University.
References (51)
- et al.
Discrete choice models of pedestrian walking behavior
Transportation Research Part B: Methodological
(2006) - et al.
Integrating route knowledge in an unfamiliar neighborhood: Along and across route experiments
Journal of Environmental Psychology
(1993) - et al.
A review of the methodologies used in the computer simulation of evacuation from the built environment
Building and Environment
(1999) Using Gaussian membership functions for improving the reliability and robustness of students evaluation systems
Expert Systems with Applications
(2011)- et al.
Counterflow model for agent-based simulation of crowd dynamics
Building and Environment
(2012) - et al.
The effects of color and light on indoor wayfinding and the evaluation of the perceived environment
Journal of Environmental Psychology
(2012) - et al.
Fuzzy sets to describe driver behavior in the dilemma zone of high-speed signalized intersections
Transportation Research Part F: Traffic Psychology and Behaviour
(2012) - et al.
Laying out the occupant flows in public buildings for operating efficiency
Building and Environment
(2012) User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding
Computers, Environment and Urban Systems
(2006)- et al.
Location awareness through trajectory prediction
Computers, Environment and Urban Systems
(2006)
Modeling and simulation of pedestrian traffic flow
Transportation Research Part B: Methodological
Experimental study on microscopic moving characteristics of pedestrians in built corridor based on digital image processing
Building and Environment
How good are these directions? Determining direction quality and wayfinding efficiency
Journal of Environmental Psychology
Fuzzy preference based traffic assignment problem
Transportation Research Part C: Emerging Technologies
Specification, estimation and validation of a pedestrian walking behavior model
Transportation Research Part B-Methodological
Mathematical modeling of observed natural behavior: A fuzzy logic approach
Fuzzy Sets and Systems
Simulation of crowd behavior and movement: Fundamental relations and application
Transportation Research Record
A model of pedestrian route choice and demand for retail facilities within inner-city shopping areas
Geographical Analysis
The influence of restricted viewing conditions on egocentric distance perception: Implications for real and virtual indoor environments
Perception
A note on two problems in connexion with graphs
Numerische Mathematik
Image environment: Cognitive mapping and spatial behavior
Behavioral dynamics of steering, obstacle avoidance, and route selection
Journal of Experimental Psychology: Human Perception and Performance
Environment, cognition, and action: An integrated approach
The wayfinding handbook: Information design for public places
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