Pedestrian wayfinding behavior in a multi-story building: a
comprehensive modeling study featuring route choice, wayfinding performance,
and observation behavior
- URL: http://arxiv.org/abs/2304.11167v1
- Date: Thu, 20 Apr 2023 08:22:10 GMT
- Title: Pedestrian wayfinding behavior in a multi-story building: a
comprehensive modeling study featuring route choice, wayfinding performance,
and observation behavior
- Authors: Yan Feng, Dorine C. Duives
- Abstract summary: This paper proposes a comprehensive approach for modeling pedestrian wayfinding behavior in complex buildings.
Four wayfinding tasks were designed to determine how personal, infrastructure, and route characteristics affect indoor pedestrian wayfinding behavior.
We find that pedestrian route choice behavior is primarily influenced by route characteristics, whereas wayfinding performance is also influenced by personal characteristics.
- Score: 17.538302663734225
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper proposes a comprehensive approach for modeling pedestrian
wayfinding behavior in complex buildings. This study employs two types of
discrete choice models (i.e., MNL and PSL) featuring pedestrian route choice
behavior, and three multivariate linear regression (MLR) models featuring the
overall wayfinding performance and observation behavior (e.g., hesitation
behavior and head rotation). Behavioral and questionnaire data featuring
pedestrian wayfinding behavior and personal information were collected using a
Virtual Reality experiment. Four wayfinding tasks were designed to determine
how personal, infrastructure, and route characteristics affect indoor
pedestrian wayfinding behavior on three levels, including route choice,
wayfinding performance, and observation behavior. We find that pedestrian route
choice behavior is primarily influenced by route characteristics, whereas
wayfinding performance is also influenced by personal characteristics.
Observation behavior is mainly influenced by task complexity, personal
characteristics, and local properties of the routes that offer route
information. To the best of our knowledge, this work represents the first
attempt to investigate the impact of the same comprehensive set of variables on
various metrics feature wayfinding behavior simultaneously.
Related papers
- A data-driven approach to predict decision point choice during normal
and evacuation wayfinding in multi-story buildings [19.36581352680941]
This paper presents a data-driven approach for understanding and predicting the pedestrian decision point choice during normal and emergency wayfinding in a multi-story building.
We first built an indoor network representation and proposed a data mapping technique to map VR coordinates to the indoor representation.
We then used a well-established machine learning algorithm, namely the random forest (RF) model to predict pedestrian decision point choice along a route during four wayfinding tasks in a multi-story building.
arXiv Detail & Related papers (2023-08-07T12:05:55Z) - A Deep Behavior Path Matching Network for Click-Through Rate Prediction [9.800832176496002]
We propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app.
We design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths.
Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model.
arXiv Detail & Related papers (2023-02-01T08:08:21Z) - Behavioral Intention Prediction in Driving Scenes: A Survey [70.53285924851767]
Behavioral Intention Prediction (BIP) simulates a human consideration process and fulfills the early prediction of specific behaviors.
This work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications.
arXiv Detail & Related papers (2022-11-01T11:07:37Z) - Task Formulation Matters When Learning Continually: A Case Study in
Visual Question Answering [58.82325933356066]
Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge.
We present a detailed study of how different settings affect performance for Visual Question Answering.
arXiv Detail & Related papers (2022-09-30T19:12:58Z) - Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for
Multi-Behavior Recommendation [52.89816309759537]
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios.
The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input.
We propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning framework to learn shared and behavior-specific interests for different behaviors.
arXiv Detail & Related papers (2022-08-03T05:28:14Z) - Recommender Transformers with Behavior Pathways [50.842316273120744]
We build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism.
We empirically verify the effectiveness of RETR on seven real-world datasets.
arXiv Detail & Related papers (2022-06-13T08:58:37Z) - Active Dynamical Prospection: Modeling Mental Simulation as Particle
Filtering for Sensorimotor Control during Pathfinding [5.817576247456002]
We model pathfinding behavior in a continuous, explicitly exploratory paradigm.
In our task, participants (and agents) must coordinate both visual exploration and navigation within a partially observable environment.
We show that our model, Active Dynamical Prospection, demonstrates similar patterns of map solution rate, path selection, and trial duration, as well as attentional behavior when compared with data from human participants.
arXiv Detail & Related papers (2021-03-14T16:26:33Z) - Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes.
We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way.
We show competitive detection and attribute recognition results, as well as a more stable MTL training.
arXiv Detail & Related papers (2020-12-04T15:10:12Z) - Studying Person-Specific Pointing and Gaze Behavior for Multimodal
Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing.
Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints.
We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
arXiv Detail & Related papers (2020-09-23T14:56:19Z) - Pedestrian Action Anticipation using Contextual Feature Fusion in
Stacked RNNs [19.13270454742958]
We propose a solution for the problem of pedestrian action anticipation at the point of crossing.
Our approach uses a novel stacked RNN architecture in which information collected from various sources, both scene dynamics and visual features, is gradually fused into the network.
arXiv Detail & Related papers (2020-05-13T20:59:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.