Evidence map›Paper›PMID 36649222›Full record

ArticlePloS one2023

Analyzing time-varying trip distributions with a random-effect spatial OD dependence model.

Linglin Ni, Xiaokun Cara Wang, Xiqun Michael Chen, Dapeng Zhang

Abstract read
In one paragraph

Article in PloS one, 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

4 authors.

Linglin NiBeijing Wuzi University Logistics school, Beijing, China.
Xiaokun Cara WangDepartment of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
Xiqun Michael ChenCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Dapeng ZhangSchool of Public Administration and Policy, Renmin University of China, Beijing, China.ORCID 0000-0001-9616-8059

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a random-effect spatial OD (origin-destination) dependence model to investigate varying trip distributions over time. By proposing a maximum likelihood estimation with spectral decomposition methods, the effects of spatial dependences and the unobservable zonal heterogeneity at the origin and destination can be estimated simultaneously. A series of numerical experiments and a real-world trip distribution study with cellular signaling data collected in Hangzhou, China, are conducted. This paper enriches the existing literature by developing (1) an innovative specification to allow for random effects in existing spatial OD dependence models; (2) an innovative estimation method to obtain the values of parameters and improve model fittings; and (3) a set of numerical experiments and an empirical trip distribution analysis that jointly captures spatial effects (spatial interaction and spatial OD dependences), and the unobservable zonal heterogeneity. This paper can equip policymakers with an effective tool for analyzing the OD travel flow over time which is a groundwork for making appropriate transportation policies.

Indexed as

Models, TheoreticalTravelChinaSpatial Analysis

Identifiers

PMID36649222
PMCPMC9844907

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.