Pedestrian Behavior Research Articles

Cell phone distraction behavior of young pedestrians: evidence from China

Cell phone use while walking escalates pedestrian safety risks, particularly among students, given their heavy reliance on these devices. Psychological factors wield significant influence on individual behavior, particularly among adolescents who navigate a blend of rational and social-reactive pathways in decision-making. Therefore, this paper employs the Prototype Willingness Model (PWM) to scrutinize psychological determinants of students’ cell phone use while walking, drawing on data from 368 valid samples. Structural equation modeling (SEM) dissects the impact of psychological constructs on this behavior, highlighting the significance of both the social response and reasoning pathways within PWM. The study comprehensively analyzes direct, indirect, and overall effects of exogenous variables on behavioral intention and behavior.

Psychological impatience in pedestrian evacuation: modelling, simulations and experiments

The study of psychological impatience has provided new insights into pedestrian behaviour and evacuation dynamics. However, previous works have mostly focused on investigating the impatience of the individual pedestrian, and there is a lack of research on the effect of impatience on different groups of pedestrians. This paper introduces an evacuation model considering both the self-growth and contagion of impatience for different groups of pedestrians. We apply the model to the scenario of two rooms separated by a corridor and conduct a series of parameter analyses of the rates of impatience self-growth and contagion. We first analyse the case that two groups of pedestrians have the same impatience self-growth rate. The results show that impatience contagion has a crucial effect on evacuation efficiency. In the no-contagion scenario, both too high and too low an impatience level lead to lower evacuation efficiency. In the contagion scenario, an increasing impatience level leads to lower efficiency. We then analyse the case that two pedestrian groups have different impatience self-growth rates. The results indicate that pedestrian groups with different self-growth rates have different evacuation efficiencies, where the pedestrian group with a higher impatience self-growth rate has a lower evacuation efficiency. A higher contagion rate results in a greater difference in evacuation efficiency between the pedestrian groups with different self-growth rates. Increasing the main exit width reduces the evacuation efficiency gap. Furthermore, we conducted a series of experiments under different impatience treatments to verify the model and found that the numerical results of our model agreed with the experimental results. The findings demonstrate that the proposed impatience model has the potential to accurately predict evacuation processes.

Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis

Predicting human trajectories is a challenging task due to the complexity of pedestrian behavior, which is influenced by external factors such as the scene’s topology and interactions with other pedestrians. A special challenge arises from the dependence of the behavior on the density of the scene. In the literature, deep learning algorithms show the best performance in predicting pedestrian trajectories, but so far just for situations with low densities. In this study, we aim to investigate the suitability of these algorithms for high-density scenarios by evaluating them on different error metrics and comparing their accuracy to that of knowledge-based models that have been used since long time in the literature. The findings indicate that deep learning algorithms provide improved trajectory prediction accuracy in the distance metrics for all tested densities. Nevertheless, we observe a significant number of collisions in the predictions, especially in high-density scenarios. This issue arises partly due to the absence of a collision avoidance mechanism within the algorithms and partly because the distance-based collision metric is inadequate for dense situations. To address these limitations, we propose the introduction of a novel continuous collision metric based on pedestrians’ time-to-collision. Subsequently, we outline how this metric can be utilized to enhance the training of the algorithms.

Research on mechanism and simulation for avoiding behavior of individual pedestrian

The research on the mechanism of avoiding behaviors and microscopic simulations in the scenario of individual pedestrian are carried out. Controlled experiments on avoidance of obstacle are conducted to analyze the microscopic characteristics of pedestrian behaviors and provide experimental evidence. Common features concerning movement parameters, such as velocity and spatial position, are summarized to construct the common scenario for avoidance for further quantitative analysis of avoiding behaviors. Walking power-consumption is introduced to describe the influences of distance and body-rotation on route choice. Further, walking comfortability and walking efficiency are proposed based on walking power-consumption to describe the long-term and short-term utilities of decision making respectively. Preference for comfortability and efficiency is discussed based on spatial distance to the target and inertia coefficient, which is introduced to describe the laziness of pedestrian. Then the process of decision making is modeled as a dynamic process of pedestrian balancing long-term and short-term benefits, based on which a microscopic simulation model is established to simulate the avoiding behaviors of pedestrian and reproduce the microscopic characteristics. The results show that, the phenomenon of delayed-avoidance exists in the avoidance of obstacle, which means that pedestrians make no instant reactions when encountering with an obstacle, but keep the current velocity and walk for some distance. Also, walking efficiency is found to be decreasing when the current velocity deviates from the target. Furthermore, moving behaviors and route choices of pedestrian are largely relevant to the level of inertia and walking preference. According to different levels of inertia, the moving microscopic characteristics and walking patterns of individual pedestrian can be divided into three types of moving strategies in avoidance. Specifically, pedestrian of lower inertia will focus more on walking efficiency, and are more active in reaction to the obstacle. Pedestrian in higher level of inertia tends to be short-sighted and lazier in route choice. Besides, the inertia of pedestrian is found to make pedestrian focus on short-term and immediate rather than long-term and global benefits in route choice.

Research Advanced in Pedestrian Detection Based on Deep Learning

In the field of computer vision, pedestrian detection is a significant research area that is frequently applied in areas like intelligent driving and video surveillance. The reliability and accuracy of subsequent tasks like pedestrian tracking and behavior analysis are directly impacted by the accuracy and effectiveness of pedestrian detection. However, there are still a number of difficulties with pedestrian detection tasks because of the variety of factors, including the shape, scale, and posture of pedestrians, as well as background interference and changes in lighting. Based on research on pertinent domestic and international literature, this article examines the key techniques for identifying pedestrians. Introduce frequently used datasets for pedestrian detection first. The model can then be split into two categories based on its training process: one-stage detection algorithms and two-stage detection algorithms. For each stage type, this study offers a thorough introduction to representative algorithms. A summary of its current state of development and future prospects was then given.

Exploration of the impact of junction characteristics on pedestrian red light violation

The design of the geometry and traffic controls at signalised junctions is often focused on the level of service offered to motor vehicles and rarely considers the level of service offered to pedestrians. This results in pedestrian adopting illegal and unsafe behaviours – e.g. red light violation. This study aimed to identify the elements of signalised junction design that are critical in pedestrian safety by analysing how they affect pedestrian behaviour. Both traffic engineering design and associated traffic conditions were investigated. Over 6500 observations had been made at 10 signalised junctions in Montreal, Canada. The 10 junctions were selected to cover a variety of environments, road users and junction designs. Results show that the presence of a countdown display has the most significant and positive impact on pedestrian behaviour. Results also suggest that pedestrians cross on the red light when they feel confident about their ability to judge whether they can use the available traffic gaps to cross safely the street. This study concludes that an adequate junction design is likely to limit risky pedestrian behaviours. Therefore, designers need to consider the factors affecting the behaviour of pedestrians to design junctions that are safe convenient and safe for them.

Accident Analysis and Identification of Black Spots on Road Section from Sanga to Dhulikhel in Araniko Highway

Road accidents are major causes of injuries and fatalities worldwide and a significant public health concern. This paper includes a comprehensive analysis of road accidents on the road section from Sanga to Dhulikhel of Kavrepalanchwok district to identify trends of accidents, causes contributing to accidents and identifying the accident black spots and ranking accident black spots based on the Weighted Severity Index and Accident Severity Index. The study uses a data-driven approach using accident data obtained from the district police office, statistical analysis and weighted Severity Index and Accident Severity Index to observe road accident patterns. This study focuses on accident analysis to find out the spatial distribution of accidents, highlight accident peak times, accident black spots and their ranking. The various factors contributing to road accidents related to driver behaviour, vehicle condition, pedestrian behaviour, and environmental condition were explored and statistical analysis are done to determine accident likelihood and severity which are very helpful to identify accident-prone areas. There is a noteworthy pattern in the accident records in the past four fiscal years. Overall, fatal accidents have decreased over the years but serious accidents have increased. The number of minor incidents has also shown some variation. Males had more accidents than females. It is clear that the male percentage significantly outnumber the female comprising 81.9%, while females make up only 10.1%. It was observed that the 26 to 56 age group is a more vulnerable. The relatively low percentages of accidents occurred during morning and late evening. The accident frequency significantly rises from the late morning through the early afternoon with peak frequency in the late afternoon. It was observed that light vehicles account for 25%, tipper and other vehicles account for 19%, two wheeler accounts for 20% and vehicle and pedestrian accounts for 36% of the total accidents recorded. This study suggests evidence-based recommendations for accident prevention, infrastructure improvement, public awareness programs and law enforcement strategies to reduce road accidents.

Assessing pedestrian behaviour: evaluation of a survey of Portuguese residents

The aim of this study was to evaluate the pedestrian behaviour of a sample of Portuguese residents. For this, a questionnaire was published online and disseminated through e-mails and social media to Portuguese residents. It was answered by 502 people (318 women and 184 men) between 15 and 64 years of age. Principal component analysis differentiated four axes into factors: (1) ‘transgression’, which included violations of the rules of the road and legal errors; (2) ‘lapses’, referring to errors caused by lack of attention; (3) ‘aggressive behaviours’; and (4) ‘positive behaviours’ in relation to other road users. From the original, a shorter version with 20 items, referring to each factor's four largest factor loads, was developed, and its internal reliability was tested and approved. The effects of demographic variables were investigated alongside how demographic variables could impact the ‘transgression’ item, as observed from the discussion of the results. This study helps recognise risk behaviours and adapt user-preventive actions.

Assessment of Pedestrians Crossing Behaviour at Mid-Blocks Under Mixed Traffic Conditions in Urban Contexts

Pedestrians are an integral part of urban transportation infrastructure systems, which is also sustainable mode of transportation system. But they are particularly at risk in isolated mid-block sites that have different levels of traffic. The purpose of this study is to investigate, analyze, and simulate pedestrian behavior at Uncontrolled Midblock Crossings in Hyderabad (Hitech city to Habsiguda) under Mixed Traffic Conditions. Particularly, the Mid-Block Crossing Behavior and Pedestrian Crossing Speed. A field survey needs to be conducted using during the project by using any kind of survey techniques, such as Video-graphic technique, manually. Different locations are to be identified, and to be chosen in accordance to Pedestrian traffic and Pedestrians Crossing at Midblock. The Survey should be carried out as per the standards and done during the peak hours. The overall Pedestrian Speed should be determined by using the pedestrian field survey. An Interview method Survey was done to determine the exact Age of Pedestrians at the Study Locations. The data that is required for analysis of pedestrian behaviour at the Mid-Block Crossings are included with Speed of vehicles, Crossing Speeds of Pedestrians, individual characteristics (Gender, Age etc.) as well as the type of Crossing Behaviour. The Pedestrian Crossing Speeds and the Cumulative Speeds were determined and the variations in accordance to Age and Gender were obtained for the selected locations. And further it can be extended to predict the Gap size of the locations by using the log Normal Regression Model and A Binary Logit Model can be examined to find the effects of behavioural aspects of parameters on the Gender (Male and Female) coded as a discrete variable.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

Pedestrian behaviour analysis at intersection in Vijayawada for road user safety and infrastructure design

Pedestrian behaviour analysis is crucial for improving safety, urban planning, public health, and transportation infrastructure planning to establish a pedestrian-friendly zone and enhance people’s overall quality of life in urban contexts. Micro-level infrastructure improvements, such as better lighting, crosswalks, and sidewalks, are essential for communities to be sustainable and walkable. Safe and practical infrastructure encourages active mobility and reduces dependency on energy-intensive modes of transportation. Giving pedestrian safety as a priority in urban construction enhances the environmental significance of sustainable cities. Safe pedestrian environments encourage walking and other non-motorized modes of transportation, which lessens the pollution and emissions that come from driving. In the majority of developing countries, such as India, rapid urbanization, an increase in the number of vehicles, and a lack of adherence to traffic regulations by both automobiles and pedestrians contribute to a significant problem at junctions. The objective of this paper is to investigate how pedestrians behave at three junctions in Vijayawada say the Moghalrajpuram intersection, high school road junction, and kanuru junction (Andhra Pradesh, India). Approximately 69% of people in the study area prefer to cross in a perpendicular manner. At Kanuru, oblique crossing speeds range from 1.88 to 3.08 m/sec, whereas perpendicular crossing speeds range from 1.88 to 2.09 m/sec. Oblique crossing speeds at the High School Road intersection range from 3.35 to 3.49 m/sec, and perpendicular crossing speeds range from 2.00 to 2.45 m/sec. Oblique crossing speeds at Moghalrajpuram junction range from 1.05 to 1.61 m/sec, while perpendicular crossing speeds range from 1.76 to 2.50 m/sec for all the genders and age groups. At every intersection, pedestrian flow variables like crossing patterns, crossing speeds in relation to demographics like age and gender, total pedestrian counts, waiting times, and directions are analysed.

Risk assessment of pedestrian red-light violation behavior using surrogate safety measures: Influence of human, road, vehicle, and environmental factors

Pedestrian red-light violation is one of the crucial causes of pedestrian crashes at urban intersections, which cause considerable injuries and casualties to this vulnerable road group of road users. The objective of this study is to evaluate the risk of pedestrian-vehicle collisions by clustering the pedestrians' red-light violations using surrogate safety measures. The present study utilized surveillance camera footage to collect data on pedestrians' red-light violations at two urban intersections in Babol City. Based on critical thresholds of post-encroachment time (PET), Time to Collision (TTC), and Gap Time (GT), three different risk levels of red-light violations were identified through the use of a K-means algorithm. Moreover, structural equation models were developed for each of the risk levels considering variables that are associated with four major components: human, environment, road, and vehicle. Lastly, policy insights into amending pedestrian behavior and promoting traffic safety culture were proposed, with an overarching emphasis on the human factor, due to its identified greater influence on the propensity for red-light violations.

Experimental study on pedestrian behavior in right-angled corners: Influence of departure position and passage width

This article presents an experimental investigation into pedestrian behavior while navigating through right-angled corners (defined as angles measuring exactly 90 degrees), emphasizing on the effects of departure position and path width, which have not been thoroughly explored in previous research. The study utilizes controlled experiments and introduces criteria for identifying the initiation and completion of turning maneuvers. Key findings reveal that participants adjust their trajectories based on their initial departure positions, with those farther from inner boundaries displaying more pronounced boundary-approaching behavior. Notably, the starting positions of turning are significantly influenced by the pedestrians’ initial departure positions, while the passage width has only a limited impact on the starting positions for turning. Conversely, the ending positions exhibit a consistent and substantial trend of increase with the widening of the path width, while showing minimal correlation with the initial departure position. Kinematic features, such as speed, acceleration, and angular speed, exhibit distinctive patterns during turning. These insights enhance our understanding of pedestrian behavior in corner navigation, offering implications for facility design and pedestrian dynamic modeling.

Analysis of Pedestrian Behavior for the Optimization of Evacuation Plans in Tall Buildings: Case Study Santiago, Chile

Countries located in the Pacific Ring of Fire, such as Chile, require robust evacuation plans for tall buildings to manage the ongoing threat of natural disasters. This study presents a methodology for developing evacuation plans by conducting pedestrian movement simulations with agents that have a model of their surroundings. This approach allows us to assess different scenarios and choose the best option based on the specific characteristics of the site. The method combines simulation and data analysis, using the Monte Carlo method to improve emergency evacuations. Initially, Pathfinder software was employed to simulate the evacuation of a tall building. This involved modeling pedestrian movements using a multiagent system. These agents were programmed to behave like real pedestrians and make decisions during evacuation scenarios, providing valuable information. The effectiveness of two evacuation strategies was then evaluated using the simulation data. The proposed methodology was validated using a case study. The simulations showed that the best strategy depends on factors such as the distribution of people, the capacity of the exits, and the time available for evacuation. Finally, the model includes a training process that uses virtual reality technology to improve situational awareness.

Pedestrian behavior interacting with autonomous vehicles during unmarked midblock multilane crossings: Role of infrastructure design, AV operations and signaling

One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrians' crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane, and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality (VR) to simulate an urban unmarked midblock environment where pedestrians interact with AVs as they cross a four-lane arterial roadway. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrians' unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. But only the roadway centerline design features significantly impact the walking time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle of the road. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue signals significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle of the road. Other factors, such as demographics, past behaviors, and walking exposure of pedestrians, are also explored. Results indicate that older individuals tend to wait longer before making crossing decisions, and pedestrians’ past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior interacting with AV.

Using Sensor Fusion and Machine Learning to Distinguish Pedestrians in Artificial Intelligence-Enhanced Crosswalks

Pedestrian safety is a major concern in urban areas, and crosswalks are one of the most critical locations where accidents can occur. This research introduces an intelligent crosswalk, employing sensor fusion and machine learning techniques to distinguish the presence of pedestrians and drivers. Upon detecting a pedestrian, the system proactively activates a warning light signal. This approach aims to quickly alert nearby people and mitigate potential dangers, thereby strengthening pedestrian safety. The system integrates data from radio detection and ranging sensors and a magnetic field sensor, using a hierarchical classifier. The One-Class support vector machine algorithm is used to classify objects in the radio detection and ranging data, while fuzzy logic is used to filter out targets from the magnetic field sensor. Additionally, this work presents a novel method for the manufacture of the road signaling system, using mixtures of resins, aggregates, and reinforcing fibers that are cold-injected into an aluminum mold. The mechanical, optical, and electrical characteristics were subjected to standardized tests, validating its autonomous operation in real-world conditions. The results revealed the system’s effectiveness in detecting pedestrians with a 99.11% accuracy and a 0.0% false-positive rate, marking a substantial improvement over the previous fuzzy logic-based system with an 81.33% accuracy. Attitude testing revealed a significant 33.33% reduction in pedestrian erratic behavior and a substantial decrease in driver speed (32.83% during the day and 70.6% during the night) compared to conventional crossings. Consequently, this comprehensive work offers a unique solution to pedestrian safety at crosswalks by showcasing the potential of machine learning techniques, particularly the One-Class support vector machine algorithm, in advancing road safety through precise and reliable pattern recognition.

Dynamic coupling model of FDS and cellular automata considering trampling behavior

Fire accidents usually cause large casualties. In this paper, a fire evacuation model based on the dynamic coupling of FDS and CA, which dynamically couples disaster data and evacuation behavior, is designed to reflect the impact of disaster factors on the pedestrian evacuation process in real-time. Furthermore, pedestrians are divided into two categories, one type of pedestrians will choose to move against the wall to reduce the risk of stampede accidents, and the other type of pedestrian will evacuate to the exit according to the normal movement method. And considering the role of information transmission between pedestrians, the two types of pedestrians will transform into each other. In addition, the concept of pedestrian crowding, support, and friction is introduced and viewed as an influencing factor of normal pedestrian falls. Set the panic value as the influencing factor of the efficiency of pedestrian information transmission and the death probability of downed pedestrians. The impact of pedestrian density, pedestrian walking behavior towards walls, evacuation response time, information propagation radius, heat release rate of the fire source, and the interference of smoke on the pedestrians' visibility range on evacuation outcomes has been discussed through numerical simulations. The analysis shows that although the presence of pedestrians walking against the wall will slightly increase the evacuation time, when the available evacuation time allows, the risk of a stampede will be greatly reduced; The increase in evacuation response time will aggravate the panic psychology of pedestrians and increase the risk of stampede accidents; The increase of the pedestrian information transmission radius will reduce the risk of a stampede, and the effect is more obvious when the pedestrian density is smaller; Selecting building materials with lower heat release rates can effectively reduce the risk of fire incidents; The restriction of visibility range by smoke seriously endangers pedestrian safety and reduces the efficiency of evacuation.Our research results can play a reference and guide for future building disaster prevention design and public safety research.

Child pedestrian crossing behaviour and associated risk factors in school zones: a video-based observational study in Kampala, Uganda

BackgroundPedestrian crashes, often occurring while road crossing and associated with crossing behaviour, make up 34.8% of road casualties in Uganda. This study determined crossing behaviour and associated factors among child...

The validation of pedestrian trajectories during turning and obstacle avoidance in virtual environments

Virtual Reality (VR) enables to study pedestrian evacuation behavior in emergencies. Yet, studies are lacking in assessing the similarity of pedestrian behaviors like avoiding and turning in virtual and real environments. In this work, we test pedestrians' avoiding and turning behavior in virtual environments from the first-person and third-person perspectives and compare the results in real environments based on the pedestrian trajectories. It is shown that the differences between the real and virtual environments in many indicators were significant in statistics, and the trajectories of participants from the third-person perspective in the virtual environment were much more similar to those from the real environment under our experimental settings. Meanwhile, most participants (22 out of 23) think that their avoiding and turning behaviors in the virtual environment from the third-person perspective were more in line with those in the real environment. The study shows that the third-person perspective is better than the first-person perspective in terms of trajectory indicators and controlling experience. The study could be used as a basis in the future for the perspective choosing while carrying out similar virtual experiments, so as to ensure the similarity of pedestrian trajectories in virtual and real environments.

Calibration of inverted pendulum pedestrian model for laterally oscillating bridges based on stepping behaviour

Despite the problem of lateral excitation of bridges by walking pedestrians being intensively studied for over two decades, there is currently no model which could quantitatively explain pedestrian-generated lateral forces. The most promising of the models proposed thus far, that is the rigid-leg inverted pendulum model (IPM) confined to the frontal plane, has been previously shown to qualitatively capture the main features of pedestrian behaviour on laterally oscillating ground, including kinematics and kinetics. However, to make the model suitable for engineering applications, it needs be reconciled with observations. To this end, a dataset collected from pedestrians walking on a laterally-oscillating instrumented treadmill is used to derive fundamental properties of pedestrian walking gait. Informed by these findings, a foot placement control law is proposed ensuring good match between the outputs of the IPM and empirical data in terms of the main components of pedestrian-generated lateral forces. That control law is then parametrised based on pedestrian anatomical and gait characteristics, thus generalising the calibrated IPM and making it suitable for use in a predictive manner. Simulations of the lateral bridge response to the lateral loading from a crowd of pedestrians with random parameters and interacting with a bridge are also conducted. It is shown that the calibrated IPM can generate self-excited forces in close agreement with those measured and that the generalised IPM, producing slightly conservative loading predictions on average, can serve in the assessment of lateral structural stability.