Pedestrian Route Choice Research Articles

Cellular automaton simulations of hybrid pedestrian movement in two-route situation

In this paper, we simulate the hybrid pedestrian movement considering both operational and tactical levels. The video data of our pedestrian route choice experiments are used for validations. Based on the CA model proposed by Nowak and Schadschneider, we build a new modeling framework and introduce six new parameters. Their values are determined by sensitivity analysis. To consider the influence of five statistical results in the simulations, including the velocities and densities of two routes, and the proportion of choosing the longer route, we propose a new metric named OPE (Overall Percentage Error). In order to reduce OPE, four models with different configurations are discussed and compared. We find that the use of shorter time step, modified SFF and the two-stage decision can help to improve the model performance. The simulation results in some other experimental run also shows the validity and universality of the model. In addition, we also compare the outputs of some typical pedestrian simulation software (e.g., AnyLogic), and we find our model has better results.

Parameterizing agent-based models using an online game

Agent-based models (ABMs) of human systems are often parameterized using real-world data. For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another. In this paper we implement an online decision game to parameterize an agent-based model of pedestrian and cyclist route choice decisions in a neighbourhood. Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM. The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world. The results of our analysis suggest that the decision model is consistent with some general theory about decision making, but the ABM illustrates some unique and contextually specific patterns of flow. ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.

Day-to-day behaviour for pedestrians in a circle antipode scenario: experiment and simulation

This study aims to investigate the behavioural evolution patterns of pedestrians who repeatedly traverse the same scenarios. To accomplish this objective, we implemented a repeated circle antipode experiment, ensuring consistent conditions for all participants. This experimental design allows for an effective examination of participant performance across multiple rounds. Our findings revealed that specific walking characteristics experienced significant changes with the progression of successive experiments, and participants demonstrated notable differences in their chosen routes. Recognizing the ability of the day-to-day dynamic model to describe the evolution of network flows and the similarities between traffic and pedestrian flows, we apply the modelling approach of the day-to-day dynamic model to the construction of pedestrian route choice modelling. Consequently, we developed a series of round-to-round pedestrian route choice models to characterize our experiment. These models factored in both historical walking experiences and the influence of neighbours. Our model proved to be reliable, achieving a route choice accuracy of approximately 80% in simulations of circle antipode experiments. The results of this study can provide valuable insights into pedestrian dynamics, aiding in understanding pedestrian behaviour during repetitive walking and facilitating the development of more accurate round-to-round route choice models.

Routes with roots: Pedestrian route choices and sense of place of an urban university community

Understanding how people choose routes in urban environments is essential for effective urban planning. While conventional transportation studies focus on utilitarian decision-making, this research investigates the complex interplay between human-environment interactions and emotional attachments to places, which influence transportation choices. Specifically, we examine the impact of sense of place in pedestrian route choice within a densely populated urban university community. Unlike typical urban settings characterized by clear roads and landmarks, university environments often feature intricate layouts with diffuse pathways, shared spaces, and a lack of clear spatial hierarchies. This complexity challenges conventional spatial knowledge acquisition methods. Individuals navigating such environments tend to rely on socio-sensory wayfinding strategies, developing emotional connections to specific places and routes over time. Consequently, route choices in these contexts may not always be deliberate but rather subconscious and nuanced. Our study focuses on elucidating the impact of the sense of place—a composite of conscious and subconscious perceptions, emotions, and attachments to a location—on daily route decisions. Through structural equation modeling (SEM) analysis, we demonstrate that the sense of place significantly influences route choices within community building complexes, surpassing utilitarian considerations as a primary explanatory factor. These findings underscore the importance of emotional and psychological factors in shaping urban route decisions, offering valuable insights for urban planning and management strategies.

Research on Pedestrian Wayfinding Behavior in Large Public Space Utilizing Non-immersive VR Platform

In this study, we conducted experiments to investigate pedestrian wayfinding behavior using a non-immersive virtual reality platform. We developed a route choice model that combines environmental factors and individual pedestrian attributes, employ-ing a hybrid Logit model. The dataset for pedestrian route choices is collected from partic-ipants’ walking trajectories within the Tianjin West Railway Station scenario. The results of our experiments demonstrate that the model effectively represents pedestrians’ route choices within the environment and exhibits strong predictive capabilities for wayfinding.

Exploring the Pedestrian Route Choice Behaviors by Machine Learning Models

To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route.

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.

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.

Modelling and solving dynamic entry pedestrian flow assignment problem

In this paper, a dynamic entry pedestrian flow assignment problem is investigated. The problem concerns a scenario, i.e. a number of pedestrians enter a public place with multiple entrances and internal obstacles, and then get to their destinations in the public place. The problem aims at determining the access entrance, moving route, and destination position choice of pedestrians so as to minimize the total entry time of all pedestrians. A mesoscopic model is developed to formulate the dynamic entry process of pedestrians in the scenario and a heuristic algorithm is proposed to solve the dynamic entry pedestrian flow assignment problem. Moreover, a set of numerical investigations are provided to demonstrate the applications of the model and the effectiveness of the algorithm, especially to a case with a larger number of entry pedestrians.

Towards dynamically generating immersive video scenes for studying human-environment interactions

Abstract. Studies where participants interact with their environment are necessary, for example to investigate pedestrian behaviour, in urban planning or for environmental studies. Existing methods such as field studies or 3D virtual reality environments frequently require a lot of effort to create realistic 3D worlds or are subject to real-world interferences. In this paper we propose an alternative approach that uses an immersive video environment (IVE) and a dynamic scene generator (DSG).We use overlays to dynamically generate video scenes to facilitate studying specific combinations of environmental factors with participants. We report on an initial case study in the context of studying pedestrian behaviour, where we show how the approach can facilitate studying how crowds and pedestrian signage affect route choices. Our approach can help researchers studying human-environment interactions in the lab.

The symmetry and asymmetry of pedestrian route choice

During everyday activities, people sometimes take a different route in one direction than they do on the return trip. Yet we do not fully understand the degree to which people choose particular routes, and this asymmetry has yet to be systematically quantified. To address these questions, we designed a multi-segment route on a college campus that included two pairs of reciprocal segments, in which young adult participants (n = 52, ages 18–23, 36f/16m) walked from place A to place B and then later in the route—without being alerted to its reciprocal nature—went from place B to A. We used GPS tracking to record the routes our participants walked, and we developed novel continuous measures of route dissimilarity which we used to analyse both reciprocal and nonreciprocal routes. Our results indicate that there is substantial asymmetry in route choice but no consistent tendency for individual participants to take symmetric or asymmetric routes. We also found substantial variation across participants in the route taken across all segments, but this natural variability in routes did not entirely explain the asymmetries we observed, suggesting that there is systematic asymmetry in route choice that goes over and beyond the tendency simply to take variable routes. Overall, our controlled test of route choice balanced experimental control with ecological validity. Combined with our novel measures of route (dis)similarity, these findings provide a new perspective on this classic route choice problem.

Effects of solar radiation in the streets on pedestrian route choice in a city during the summer season

There is a hypothesis that on severe hot weather days, pedestrians may choose routes based on the presence of solar radiation. However, this hypothesis is yet to be verified. Therefore, the present study experimentally clarified the effect of solar radiation in streets on pedestrian route choice. In the experiments, nineteen participants walked freely from the starting point to the designated destination on a sunny summer day. The area for the experiment is a commercial area around the Tokyo Station, which is the central area of Tokyo, Japan. In the experiment, participants were asked to verbalize the reasons for their route choices at every diverging point during their walk. The results are as follows: Most participants chose shaded routes in front of a large commercial facility. The most common reason given was “because there is shade” (28.2%), indicating that the presence or absence of shade is a major factor in route choice under severe thermal environment. Furthermore, in the questionnaire conducted after the walk, the most common reason participants provided for choosing shade was to protect their health, such as preventing heat stroke and sunburn. In summary, participant responses indicated their awareness of the dangers of solar radiation and their defensive motivation to avoid it, which might have led them to prioritize shade over other reasons in their route choice. The clarification of the effect of solar radiation on pedestrian route choice and the reasons for choosing shaded areas is very useful for future consideration in building design and urban planning.

Neighbourhood-level pedestrian navigation using the construal level theory

Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. We present a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network-based decisions at the neighbourhood level. The framework is based on construal level theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. We implement this route choice framework in a spatial agent-based model in which pedestrian and vehicle agents complete trips in an urban environment. Using global sensitivity analysis techniques, we demonstrate the interaction between route choice components representing decision making at different spatial and temporal scales. Additionally, through comparison to a least cost network model, we demonstrate the increased route heterogeneity produced by this approach. This work could form the basis of an alternative method for producing pedestrian route alternatives. The granularity and scale of the modelled pedestrian trajectories could also help improve appraisals of street infrastructure.

Investigating pedestrian crossing route choice at mid-blocks without crossing facilities: The role of roadside environment

Due to the lack of instructions and regulations, pedestrian crossing areas and routes are usually uncertain at mid-blocks without crossing facilities, resulting in higher crossing risks. Using Unmanned Aerial Vehicle and roadside camera data, this study investigates pedestrian crossing behaviors at mid-blocks without crossing facilities by proposing metrics for characterizing pedestrian crossing routes, including entry point, crossing angle and trajectory shape. This study also contributes to the literature by investigating the role of roadside environment (i.e. pedestrian’s start–end Points of interest (POIs), distribution of curb cuts, roadside parking and bus stops) in explaining pedestrian route choice, which could help with the street layout design at such areas.The results emphasize the attractiveness effects of curb cuts on pedestrians’ choices of entry points. Moreover, crosswalk usage and choosing curb cut as the entry point could contribute to a larger crossing angle. In addition, pedestrians with direct opposite start–end POIs also have a higher inclination of crossing perpendicularly. Pedestrians alighting from roadside-parked cars are more likely to cross with larger angles and complex-curve trajectories. In contrast, crossings with bus stop as the start POI and end POI are related to smaller crossing angles. Finally, practical suggestions are also provided to help with the street layout design and the development of drivers warning facilities, further enhancing pedestrian crossing safety at mid-blocks without crossing facilities.

Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach

Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the computation of value functions remains unsolved. This issue is particularly significant for model estimation, during which the parameters are updated every iteration and may violate the feasibility condition of the value function. To solve this numerical issue of the value function in the model estimation, this study performs an extensive analysis of a prism-constrained RL (Prism-RL) model proposed by Oyama and Hato (2019), which has a path set constrained by the prism defined based upon a state-extended network representation. The numerical experiments have shown two important properties of the Prism-RL model for parameter estimation. First, the prism-based approach enables estimation regardless of the initial and true parameter values, even in cases where the original RL model cannot be estimated due to the numerical problem. We also successfully captured a positive effect of the presence of street green on pedestrian route choice in a real application. Second, the Prism-RL model achieved better fit and prediction performance than the RL model, by implicitly restricting paths with large detour or many loops. Defining the prism-based path set in a data-oriented manner, we demonstrated the possibility of the Prism-RL model describing more realistic route choice behavior. The capture of positive network attributes while retaining the diversity of path alternatives is important in many applications such as pedestrian route choice and sequential destination choice behavior, and thus the prism-based approach significantly extends the practical applicability of the RL model.

Study on the route choice of pedestrians from a station using commercial-based GPS

Study on the route choice of pedestrians from a station using commercial-based GPS

How building layout properties influence pedestrian route choice and route recall

How pedestrians find and choose routes in buildings is a fundamental research topic that is immediately relevant to building design and safety. However, few studies have explored the relationship between pedestrian route choice and building layout in a systematic way. Here, we introduce a method based on spatial network theory for generating buildings with various layout properties. We conduct a virtual experiment with over 200 participants and many generated buildings to investigate how layout properties influence different aspects of pedestrian route choice. Our findings suggest route recall is worse in buildings that have more connections and possible routes, even when the overall size of buildings and length of routes is kept constant. Pedestrians also prefer more regular building layouts and are more likely to adopt the heuristic of walking along the outer edges of buildings the less regular they are and the more connections they have.

Geographic Information System-Based Model of Outdoor Thermal Comfort: Case Study for Zurich

The importance of walking, the most basic form of transportation, is growing. Climate change and the associated warmer temperatures could reduce comfortable walking distances drastically. There is a clear need to better understand how outdoor thermal comfort (OTC) and walking interact. In this work, thermoregulation of the human body is modeled with the two-node model to determine the influence of the microclimate on pedestrians’ OTC. First, the impact of the current microclimate in Zurich on the route choice of pedestrians is analyzed. No significant correlation is found between simulated OTC of walking a particular route and route choices for all trips, but results for longer trips indicate a possible influence of OTC. It is pointed out that the same assessment could be done for other regions, and the results could contribute to more accurate pedestrian modeling. Second, a tool is developed that can estimate OTC-corrected walking distances from any location. The tool is applied to the current climate and future climate scenarios. The results show that, in the future, OTC of pedestrians in Zurich will be severely decreased. Further, the tool can detect where there is potential for and, through its accessibility approach, quantify improvements to the built environment citywide. Future work should focus on enhancing physiological input parameters to the model. This work provides a novel use of the two-node model for walking subjects in a citywide assessment.

A walk accessibility-based approach to assess crowd management in mass religious gatherings

The effectiveness of any crowd management plan is constrained by the behavior of individuals in the crowd. In mass religious gatherings, the strict time schedules to perform specific activities and the arrival and departure constraints force pilgrims to incorporate multiple stops in the same tour based on the proximity of these sub-destinations. This paper explores the role of accessibility of prime activity locations, possible trip chaining patterns, and the resultant shortest paths in the context of crowd management. The extracted mean speed of pedestrians from the video data of Kumbh Mela, Ujjain (2016) is used as an input to generate the spatial time-based accessibility map. Two methods – space syntax and accessibility considering travel time are used to understand the pilgrim's travel choices. The findings from the study confirm that both distance and directions are the major determinants of pedestrian route choice in mass religious gatherings. The study also evaluates the effect of temporary road closures on the route choices of pilgrims The methodology adopted, and insights derived from the analysis could be used to manage the crowd more effectively by ensuring that critical attention is given to the routes that are more likely to carry the diverted pedestrians during road closures.

How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco

This study adds to the nascent but growing literature on the use of big data for pedestrian route choice analysis. We explore behavioral preferences for various route attributes in Boston, MA using a large dataset of GPS trajectories (n = 11,165) sourced from a third-party smartphone app. Although the data are anonymized and limit our exploration of user heterogeneity, the sample size and area coverage are both much larger than seen in most previous studies. We estimate route choice preferences using a path size logit model, and operationalize the coefficients for policy-making through ‘willingness-to-walk’ measures. The value of these measures is demonstrated through an application of computing pedestrian accessibility to transit stations. Additionally, we compare our findings to a previous study in San Francisco, CA using similar data and methods, and previous literature to explore similarities and differences in pedestrian route choice behavior across major metropolitan areas more generally. While our findings can inform walkability policy and practice on several counts, we recommend future efforts to focus on supplementing this study by surveying hard-to-reach populations for more equitable policy-making.