Evidence map›Paper›PMID 37430795›Full record

ArticleSensors (Basel, Switzerland)2023

Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling.

Andrew Kwok Fai Lui, Yin Hei Chan, Kevin Hung

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Andrew Kwok Fai LuiSchool of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China.ORCID 0000-0003-4990-7570
Yin Hei ChanSchool of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China.
Kevin HungSchool of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China.ORCID 0000-0002-5421-7622

Funding

Research Grant Council, Hong Kong UGC/FDS16/E12/20
6 · The paper itself

Abstract

Functional objects are large and small physical entities installed in urban environments to offer specific functionalities to visitors, such as shops, escalators, and information kiosks. Instances of the novel notion are focal points of human activities and are significant in pedestrian movement. Pedestrian trajectory modelling in an urban scene is a challenging problem because of the complex patterns resulting from social interactions of the crowds and the diverse relation between pedestrians and functional objects. Many data-driven methods have been proposed to explain the complex movements in urban scenes. However, the methods considering functional objects in their formulation are rare. This study aims to reduce the knowledge gap by demonstrating the importance of pedestrian-object relations in the modelling task. The proposed modelling method, called pedestrian-object relation guided trajectory prediction (PORTP), uses a dual-layer architecture that includes a predictor of pedestrian-object relation and a series of relation-specific specialized pedestrian trajectory prediction models. The experiment findings indicate that the inclusion of pedestrian-object relation results in more accurate predictions. This study provides an empirical foundation for the novel notion and a strong baseline for future work on this topic.

Indexed as

deep learningfunctional objectspedestrian movement modellingpedestrian trajectoryrecurrent neural networksurban environments

Identifiers

PMID37430795
PMCPMC10221920

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.