Pedestrian Behavior Research Articles

What do pedestrians consider when choosing a route? The role of safety, security, and attractiveness perceptions and the built environment during day and night walking

Increasing the use of non-motorized modes of transport, such as walking, is a worldwide objective aimed at improving the sustainability of cities. However, pedestrians may not choose to walk if the infrastructure fails to meet their needs or if they hold unfavourable perceptions regarding the built environment (BE). The current study aims to identify the significance of route attributes and perceptions of attractiveness, safety, and security at the route level, which influence pedestrians' preferences for last-mile route choices. A cross-sectional design was employed, utilizing a questionnaire comprising a stated preference (SP) experiment and a perception survey. The study considered theory-informed attributes influencing pedestrian route choice preferences, including: (1) individual-level determinants, (2) physical-level determinants, and (3) time of day. Two separate models were developed, considering the time of day, to examine the differences in trade-offs within pedestrians' route choice preferences between day and night. The results revealed that both the BE and perceptions of the BE play a crucial role in determining pedestrian route choice behaviour. Pedestrians showed a preference for routes fully encompassed by mixed or residential land uses during the daytime. The presence of vacant land along the walking route significantly decreased the likelihood of choosing a route at night. Generally, pedestrians favoured shorter walking times, lower posted speed limits, and comfortable walkway grades in their routes. Female pedestrians tended to avoid routes that were not well-lit and pleasant at night. Lowering roadway speed limits emerged as a strategy to encourage walking in suburban areas. The findings of this study hold the potential to play an essential role in the development of effective policy initiatives targeted at pedestrians in cities.

Impact of Covid-19 on pedestrian safety

The Covid-19 pandemic has affected everyday life in many areas, including transport. As a result of the reduction in traffic and epidemic measures, the number of traffic accidents and the number of people injured in accidents have decreased worldwide. However, the available research results are mainly limited to the investigation of the number of accidents and injuries. The aim of our research was to determine the impact of Covid-19 on pedestrian accidents, focusing also on the characteristics of injured pedestrians, and the willingness of drivers to give priority to pedestrians. We performed a multi-aspect analysis and used statistical methods to examine the characteristics of pedestrian accidents and injured pedestrians in Budapest between 2018 and 2021, and the changes caused by the epidemic. During the analysis of the accidents, we also created maps using the Kernel Density Function based on the severity index of the accident outcome. Pedestrian and driver behaviour were assessed based on the rates of giving the priority to pedestrians at pedestrian crossings, using the results of our own measurements before and during the Covid-19 period. As the main result of our research, we found that although the epidemic and related measures reduced the number of pedestrian accidents, there was no significant change in accident characteristics. In the case of injured pedestrians, we showed a significant change in the reduction of injuries between the ages of 0 and 18 and in case of the injuries of foreign citizens. In addition, we found that the willingness to give priority for pedestrians was negatively affected by the period of Covid-19.

Eye image effect in the context of pedestrian safety: a French questionnaire study.

Human behavior is influenced by the presence of others, which scientists also call 'the audience effect'. The use of social control to produce more cooperative behaviors may positively influence road use and safety. This study uses an online questionnaire to test how eyes images affect the behavior of pedestrians when crossing a road. Different eyes images of men, women and a child with different facial expressions -neutral, friendly and angry- were presented to participants who were asked what they would feel by looking at these images before crossing a signalized road. Participants completed a questionnaire of 20 questions about pedestrian behaviors (PBQ). The questionnaire was received by 1,447 French participants, 610 of whom answered the entire questionnaire. Seventy-one percent of participants were women, and the mean age was 35 ± 14 years. Eye images give individuals the feeling they are being observed at 33%, feared at 5% and surprised at 26%, and thus seem to indicate mixed results about avoiding crossing at the red light. The expressions shown in the eyes are also an important factor: feelings of being observed increased by about 10-15% whilst feelings of being scared or inhibited increased by about 5% as the expression changed from neutral to friendly to angry. No link was found between the results of our questionnaire and those of the Pedestrian Behavior Questionnaire (PBQ). This study shows that the use of eye images could reduce illegal crossings by pedestrians, and is thus of key interest as a practical road safety tool. However, the effect is limited and how to increase this nudge effect needs further consideration.

Pedestrian interactions with automated vehicles: Does the presence of a zebra crossing affect how eHMIs and movement patterns are interpreted?

Previous research has shown that the use of an eHMI can lead pedestrians to make earlier, and more, crossing decisions in front of an automated vehicle (AV). However, there has been little exploration of the impact of crossing infrastructure or AV approach direction on pedestrian behaviour. This CAVE-based pedestrian simulator study investigated the individual, and combined, effects of a pedestrian crossing, AV approach direction, AV yielding behaviour, and a novel external Human Machine Interface (eHMI) on pedestrian crossing decisions at a four-way crossroads. Thirty eight participants took part in a multi-method study consisting of a pedestrian simulator experiment, an online interview, and a short questionnaire. The main independent variables were: (1) presence or absence of a zebra crossing; (2) the direction from which the AV approached (oncoming/right); (3) the AV’s yielding behaviour (yielding/not yielding); and (4) the presence or absence of a light-based eHMI. The AV’s yielding behaviour was the most important source of information for pedestrians, followed by the crossing infrastructure. Participants showed a greater willingness to cross in front of yielding than non-yielding vehicles, and were more likely to cross in the presence of a zebra crossing. The eHMI had the most impact in the absence of a zebra crossing, promoting earlier crossings, and encouraging more participants to cross while the approaching AV was still moving. The results of this study show the importance of eHMIs for situations associated with uncertainty about right-of-way between an AV and other road users, and highlights the interaction between formal traffic infrastructure and explicit forms of communication for future AVs. This knowledge increases our knowledge of when and where explicit communication from AVs can reduce the likelihood of pedestrian misunderstanding of AV intentions, thus reducing the likelihood of accidents occurring around these vehicles.

Investigating mid-block pedestrian crossing behaviour and safety at urban streets in Cairo.

Pedestrians are the most vulnerable road users in the transportation system. Understanding pedestrian behaviour and road safety culture is critical for improving traffic safety in developing countries. The primary goal of this 2-fold study is to provide an investigation of pedestrian behaviour in Egypt, a developing country. The first part of this study validated the applying of Pedestrian Behaviour Scale (PBS) to investigate pedestrian behaviour in Egypt. Confirmatory Factor Analysis (CFA) was applied to 533 participants to assess the pedestrian's behavioural factors based on four validated categories: transgressions, lapses, aggressive and positive behaviours. The second part investigated pedestrian crossing behaviour at five different mid-block locations using video recordings. Then, logistic regression models were conducted to investigate pedestrian crossing safety. Males reported more aggressive behaviour than females. Pedestrians previously involved in a collision committed more transgressions and lapses. The presence of forced pedestrian crossing facilities, such as raised pedestrian crossings or traffic signals, significantly reduced the number of unsafe crossings when compared to uncontrolled pedestrian crossings. The aforementioned findings can be used by policymakers to improve road safety programs, create effective traffic safety campaigns, and enact appropriate laws, which could reduce the number of pedestrian-related crashes.

Understanding pedestrian route choice behavior in the continuous space: Diversity and equilibrium

Exploring general rules from diverse motion choices in the real world is a pivotal area of study that deepens our understanding of pedestrian behavior. In this research, we conduct pedestrian circle antipode experiments, from which we extract pedestrians' trajectories from repeated trials. We focus on the symmetric features identified from these experiments and employ the K-Medoids trajectory clustering method alongside several behavioral indicators, namely route length, travel time, and comfort level, to distinguish and compare different trajectory route choice patterns. In doing so, we delve into the heterogeneous behaviors exhibited by pedestrians within a single experiment and the stochastic behaviors seen across different experiments for a single pedestrian. Additionally, we establish a linear model to scrutinize the equilibrium relationship among these behavioral indicators, and further deploy the Gaussian process to shed light on the equilibrium characteristics inherent in pedestrian route choice behavior. Our findings suggest that despite the inherent heterogeneity and stochastic nature of individual behaviors, pedestrian route choice overall operates in a dynamic equilibrium state. These insights offer valuable implications for the design of facilities in transport hubs and crowd management strategies in social activities, potentially leading to more effective and organized public spaces.

Of cebras and citizens: Kinesthetic politics in Bolivia’s transport cities

AbstractUrban planners and foreign donors have long agonized over how politics, movement, and transportation infrastructure collide in the conjoined cities of El Alto and La Paz, Bolivia. As displaced tin and silver miners migrated to El Alto in droves during the 1980s and 1990s, they banked on that transportation sector to remake their lives, investing their severance packages in the lumbering “Micro” buses and minibuses that now choke both cities’ streets. La Paz’s patchwork of neighborhoods reflects its own history—and present—of racialized class mobility. This article examines the governance politics of municipal efforts to reform pedestrian and driver behavior in cities—mundane habits of movement that are freighted with political significance. In these urban education campaigns, the ways that residents move through transportation infrastructure comprises an important dimension of what it means to be a good citizen. As youth dressed as Cebras (zebras) playfully instruct residents on responsible urban behavior, they expose the somatic and especially kinesthetic dimensions of urban governance and belonging in the city. This article argues for greater attention to the forms of bodily attunement promoted by urban education campaigns and mobilized by city residents during daily encounters with transportation infrastructure.

Modeling of pedestrian turning behavior and prediction of pedestrian density distribution

As urban public spaces attract more pedestrians, it is essential to prevent excessive pedestrian aggregation. Use of a walking behavior model is crucial to predict the distribution of pedestrian density. In earlier studies, the modeling of walking behavior generally focused on straight walking or aimless strolling. In recent years, more complex models of walking behavior, including turning behavior, have increasingly gained attention. Because the majority of models are calibrated using data gathered from experimental settings, research on the prediction of turning behavior of multi-scale walking passages in life scenarios remains in its development.The aim of this paper is to develop a turning behavior model suitable for multi-scale pedestrian passages. The expected walking direction was predicted by combining the global path planning model with a gradually changing expected direction with the local path planning model based on the attention field. Then, it was integrated with social force model to predict the pedestrian walking behavior and density distribution. Settings in the turning behavior model such as adaptively changing the expected walking direction according to the particular circumstance, approaching the inner curve upstream of the passage intersecting space, and appropriate pedestrian heterogeneity traits were found to help make predictions more accurately. The proposed turning behavior model is applicable to physical environments with different widths without recalibrating parameters and is suitable for predicting the distribution of pedestrian density in urban life scenarios with a variety of passage widths, which helps in the effective design and renovation of pedestrian space.

Evaluating the gap choice decisions of pedestrians in conflict situations in mass religious gatherings and controlled experimental setup – A pilot study

Previous studies on modelling the microscopic behaviour of pedestrians have focused on conflict resolution among pedestrians in pedestrian-pedestrian interactions. Many of these models propose alternate mechanisms to avoid conflicts by introducing repulsive forces between pedestrians or a set of predefined rules stating the precedence of movements to sidestep obstacles and other pedestrians. However, the possibility of formulating the decision-making mechanism pedestrians use to overcome conflicts as a gap-seeking behaviour has not been explored. In this study, resolving conflicts between opposing pedestrians is modelled as gap choice decisions made by individuals. Pedestrians looking for gaps or spaces in a crowd to facilitate their movement form the basis for such an analysis. The study compares pedestrians' gap acceptance behaviour across two scenarios: pedestrian movement in a field setup (Kumbh Mela) and a controlled experiment. Multiple gap choice decisions of individuals are modelled to understand the effect of individual-level heterogeneity on gap choices. Apart from the gap duration, spacing, position of gap, linear density, age, and presence of luggage significantly influenced the gap choices. Model validation is done using appropriate methods for both field and experimental data. The bootstrap method of internal validation and holdout validation is used to assess the performance of the estimated model on field data and experimental data, respectively. It is seen that the models have reasonable predictive and discriminative abilities. The analysis results also indicate that pedestrians tend to force gaps to facilitate movement in their natural state. Consequently, controlled experiments might have limitations in reproducing or motivating the participants to behave like a crowd.

Task difficulty, risk feeling, and safety margin in the determination of driver behavior to prepare for traffic conflicts

Unpredictable pedestrian and cyclist behavior associated with their appearance on the road in blind spots contributes to traffic near-misses or crashes. When experienced drivers are confronted with uncertainty, they take defensive measures called hazard-anticipatory driving, such as decreasing the vehicle velocity and/or increasing the lateral distance. Our research sought to understand the motivational determinants and perceptual processes that determine driver behavior in preparation for traffic conflicts with covert hazards.This study aimed to investigate the influence of driving experience on drivers’ perceptions and behaviors to prepare for traffic conflicts. Two experiments were designed with 8 experienced and 13 inexperienced participants. In Experiment 1, participants were asked to provide their subjective impressions of task difficulty, feeling of risk, and statistical risk pertaining to assess their perceptions of the separation between task demand and capability after viewing animation clips of road scenes with blind intersections under different forced speeds. In Experiment 2, participants drove using a driving simulator in scenes with blind intersections, similar to those in Experiment 1. We sought to explore the motivational determinants of behavior regarding the relationship between subjective feelings and objective safety margins.The results showed that the driver’s perception of task difficulty correlated with their driving speed, and inexperienced participants tended to underestimate task difficulty compared to experienced participants. The task difficulty and the feeling of risk were strongly correlated regardless of experience, and estimation of statistical risk differed depending on experience. The subjective task difficulty (and/or risk feeling) and objective safety margin were strongly correlated for experienced participants. Experienced participants who perceived a higher degree of difficulty in the forced-paced driving task tended to have greater safety margins in the self-paced driving task. These findings suggest that experienced participants with individually tolerable safety margins adjust their driving velocity and/or lateral distance in the control of task difficulty (and/or risk feeling) to prepare for traffic conflicts. Therefore, the underestimation of task difficulty should be considered when designing effective measures, such as driver assistance systems, to guide inexperienced drivers toward normative behaviors.

Evaluation of a pedestrian call extension and cancelation system at a signalized midblock crossing

ABSTRACT Unnecessary vehicular delay occurs at actuated signalized intersections during ‘ghost’ pedestrian intervals when a pedestrian activates the pushbutton then jaywalks. Safety issues arise when pedestrians remain in the crosswalk at the end of the Flashing Don’t Walk phase, and vehicles receive a circular green. A Pedestrian Call Extension and Cancelation System was evaluated at a signalized midblock crossing in Hillsboro, OR. Thermal sensors and controller logic were implemented to detect pedestrians and passively cancel or extend calls. A Case 1-after study was conducted to evaluate how vehicular delay and pedestrian behavior was impacted by the system. 1,649 observations Case 1 and 3,002 observations after system installation were recorded. Pedestrian delay did not change, but average vehicular stop time delay significantly decreased by 12 sec. 76 extensions occurred with 99.2% accuracy. Findings suggest that transportation agencies could improve driver waiting time and pedestrian safety by using dynamic passive pedestrian detection.

Trajectory prediction considering the behavior of pedestrians intersecting with vehicles

In this paper, we propose a trajectory prediction method that takes into account pedestrian behavior. To realize safe automated driving in urban areas, it is necessary to predict the future movements of road users. Pedestrians entering the vehicle's direction are a target of interest, but they are difficult to predict because they change their behavior through significant interactions with the vehicle. In this study, we first predict whether pedestrians will yield the way to a vehicle. Next, the predictions are then used to predict the future trajectories of all road users in the scene. The proposed model consists of two neural network structures: the Yielding judgment module to predict pedestrian behavior and the Trajectory prediction module. The yielding judgment module provides intuitive and easily understandable indicators, which also helps to increase the interpretability of the overall model. We evaluated the usefulness of the proposed model using a publicly available dataset. The proposed method was found to reduce the average displacement error by 2.79% and the final displacement error by 2.70% compared to the case where the target pedestrian's behavior is not considered.

A Systematic Review and Meta-Analysis on the Efficacy of Virtual Reality Pedestrian Interventions to Teach Children How to Cross Streets Safely.

Over 7,000 American children die from pedestrian injuries annually, and pedestrian injury ranks among the top 5 causes of unintentional child death. Prevention efforts are multifaceted. One strategy, use of virtual reality (VR) to teach children to cross streets, is of growing interest to public health practitioners. The present study is a systematic review and meta-analysis that examined the efficacy of using VR to teach children pedestrian safety. Following PRISMA guidelines, searches among 7 databases were completed, followed by abstract/full-text screening and data extraction. Hedge's g was computed for the effect sizes of 3 outcomes: pedestrian knowledge, pedestrian-relevant cognition (attention to traffic, time to contact, start delay), and pedestrian behaviors (safe crossings, unsafe crossings). Risk of bias was assessed using Cochrane guidelines. Meta-regression analyses and subgroup analyses were conducted including 3 moderators: age, sex, and immersion level. A total of 20 studies, reported in 24 articles, were included in the qualitative analysis. Meta-analysis of the 12 studies with sufficient quantitative data available showed a statistically significant medium effect of VR safety interventions on child pedestrian knowledge and behavior. Mixed results emerged for the effect of VR safety interventions on children's pedestrian-relevant cognition. Age and sex moderated the effect of VR training on pedestrian knowledge. This synthesis of the literature on pediatric VR pedestrian safety interventions suggests an overall beneficial impact of VR interventions to teach children how to cross streets safely. Efforts should continue to develop and disseminate effective VR interventions. CRD42022309352.

Pedestrian delay models for compliant & non-compliant behaviour at signalized midblock crosswalks under mixed traffic conditions

The present study aimed to propose new pedestrian delay models for Signalized Midblock Crosswalks (SMC) for mixed traffic conditions. A detailed study of the literature revealed that most of the existing pedestrian delay models were developed for signalized intersections. Thus, the need for the study was established and data were collected at eight SMC in Hyderabad, one of the most densely populated metropolitan cities in India, using video-graphic technique. Two delay models were developed based on the compliance behaviour and non-compliance behaviour of pedestrians. Both models have two components i.e., waiting delay and crossing delay where the latter has two subset components i.e., frictional delay and pedestrian-vehicle interaction delay. The bidirectional effect (pedestrian-pedestrian interaction while crossing the road) of pedestrians was addressed as frictional delay while the non-compliance behaviour by pedestrians and vehicles was addressed as pedestrian-vehicle interaction delay in the present models. The waiting delay component was defined by modifying the Webster delay model for non-uniform pedestrian arrivals. The proposed delay models yielded an error of 5% and 7% for compliance behaviour model and non-compliance behaviour model respectively. The proposed models can be used for optimizing the signal timings and defining Level of Service (LOS) of facilities.

Estimating of Critical Gaps at Uncontrolled Intersections under Heterogeneous Traffic Conditions

Pedestrian crossing behavior can be studied by estimating critical gap, which is determined by analyzing accepted and rejected gaps by pedestrians. This can provide insight into safety levels at pedestrian facilities. The aim of this study is to determine critical gaps using various methods such as the Logit method, Raff's method, and Wu’s Method. These methods are then compared to identify the most appropriate one. Three locations in Malaysia were selected for data collection based on their land use, number of lanes, and carriageway width. Video cameras were used to capture mixed traffic flow and pedestrian crossing movements simultaneously at the selected sections. The results indicate that the critical gap values obtained from the three methods are highly comparable. Specifically, the Logit Method yielded a critical gap value of 8.4s, while Raff's Method and Wu's Method produced critical gap values of 7.7s and 7.12s, respectively. The study concludes that the Logit method is the most suitable for estimating critical gaps as it takes into account both pedestrian behavior and vehicular characteristics concurrently. The findings of this study have the potential to contribute to the review of design parameters for pedestrian crossing facilities, leading to the improvement of existing facilities and the enhancement of pedestrian safety.

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle-Pedestrian Interactions.

Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians' unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle-pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle-pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle-pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle-pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle-pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle-pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

An observational study on pedestrian and bicyclist violations at railroad grade crossings: Exploring the impact of geometrical and operational attributes

Introduction: In order to enhance the existing body of research regarding the prevalence of violations committed by pedestrians and bicyclists, this observational study utilized personal-specific, train-specific, operational, and behavioral data extracted from recorded videos at seven railroad grade crossings (RRGCs). Method: A total of 7,332 pedestrians and 1,684 bicyclist crossing events were observed during 279 gate operation phases at seven study locations. First, two separate violation models (binary logit) for pedestrians and bicyclists were developed to determine the influence of personal-specific characteristics and other behavioral traits on probability of pedestrians’ and bicyclists’ violation. Later, the impact of various geometrical (railway track, distance between gates, distance between gate and nearest track, and road width) and operational (train vehicle unit) parameters on the factors that caused non-motorist violations was examined by developing distinctbinary logit models. Results: The probability of pedestrians’and bicyclists’ violationwas found to be significantly influenced by train characteristics (train speed and train occupancy time), gate operational attributes (warning time and gate blockage duration), and personal-specificcharacteristics (gender, age, crossing speed, and looking both side). Furthermore, geometrical attributes along with train vehicle unit were observed to have impact over various factors that significantly influenced the behavior of pedestrians and bicyclists to violate. Practical Applications: The findings of this research provide essential information that could be applied in the initiatives of user awareness and enforcement program, emphasis on situational awareness and design of operational and geometricalelementsto significantlylimitthe effects of risky walking andbicycling behavior at RRGCs.

Optimization Method of Subway Station Guide Sign Based on Pedestrian Walking Behavior

The appropriate distribution of the guide signs in subway stations can allow passengers to obtain direction and wayfinding information quickly and accurately. The existing setting methods of subway station guide signs mainly consider the locations of facilities and pedestrian flowlines in the station. Therefore, this research proposes an optimization method based on pedestrian walking behavior to set guide signs more realistically and increase the guidance efficiency of signs. Firstly, through on-site investigation, this research analyzed the walking behavior characteristics of pedestrians and measured the average speed of pedestrians in different spaces to quantify the walking characteristics. Then, the guidance level of guide signs was defined to describe the guide ability of each sign and was the basis for determining the distribution of the guide signs. The constraint of the distance between two adjacent guide signs was added according to the short-term memory of pedestrian walking identification. Moreover, the variable of the number of floors between the two adjacent signs was added to avoid calculating floor-by-floor. Finally, Xi’an Xiaozhai Station was selected as an example to verify the proposed optimization method using the AnyLogic 8.7.6. The results show that the guide sign optimization method based on pedestrian walking behavior proposed in this paper can obtain an accurate distribution of guide signs, which can decrease the outbound time by 18.51s at the most, and the thickness at the bottleneck decreases by 5.90%.

Development of the Social Force Model Considering Pedestrian Characteristics and Behavior

Use of pedestrian facilities like sidewalks is influenced by pedestrian characteristics like age, gender, and purpose, for example, walking in social groups, shopping. Helbing and Molnár’s base social force model (SFM) has proven reliable at simulating pedestrian behavior, However, only a small number of simulation models, including SFM and its variations, have concentrated on modeling pedestrian walking while taking into account their traits and behavior. This study’s goal was to simulate bidirectional pedestrian walking behavior by taking into account various pedestrian characteristics (age, gender, behavior) by introducing a novel pedestrian simulation model: the social force model considering pedestrian characteristics and behavior (SFMPCB). Pedestrian gender alone was considered as a modification in our earlier study. The current study also implements a multistep global calibration framework based on a genetic algorithm that takes into account the bidirectional flow of pedestrians from various sidewalks. For calibration and validation, videographic data of pedestrian traffic from four sidewalks in Mumbai, India, were employed. The SFMPCB could be utilized to simulate pedestrian walking on sidewalks reflecting different characteristics and behavior. The quality of service and sidewalk design could also be better studied by utilizing the SFMPCB by generating pedestrian equivalent factors for pedestrian characteristics, similar to passenger car units.

Modified social force model considering emotional contagion for crowd evacuation simulation

Pedestrian behavior in emergency situations is an important part of emergency management, and crowd evacuation simulation provides a low-cost and low-risk method for investigating pedestrian evacuation dynamics by describing pedestrian evacuation behavior and predicting evacuation outcomes under hypothetical situations. This study proposed a modified social force model that combines dynamic hazard and emotional contagion frameworks to investigate the effect of panic on pedestrian evacuation dynamics under heterogeneous personality trait distributions. First, the emotional contagion framework was improved, and an updated emotional model was developed considering personality characteristics. Second, the individual self-driving force in the social force model was transformed from a physical drive mode to a psychological drive mode by introducing panic factors. Lastly, new rules for the spread of panic within small groups were formulated based on the important influence of small group behavior on evacuation. The simulation results confirmed the feasibility of analyzing crowd evacuation from the perspective of social psychology, and revealed that the spread of panic was closely related to the distribution of personality characteristics, individual walking speed, exit usage, evacuation efficiency, and small groups. In addition, the model could reproduce self-organizing behavior in crowd evacuation, such as fast is slow and herd behavior. Further, the results revealed that moderate panic can help improve evacuation efficiency, whereas excessive panic can result in uneven utilization of exits. In addition, the presence of small groups in panic crowds increased the crowd evacuation time.