Evidence map›Paper›PMID 40264962›Full record

ReviewPatterns (New York, N.Y.)2025

Insights into transportation CO

Zhenyu Luo, Tingkun He, Zhaofeng Lv, Junchao Zhao, Zhining Zhang, Yongyue Wang, Wen Yi, Shangshang Lu, Kebin He, Huan Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Tracing COEnvironmental science and ecotechnology · 2025
    Article
  3. Article
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

10 authors.

Zhenyu LuoState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Tingkun HeState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Zhaofeng LvState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Junchao ZhaoState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Zhining ZhangState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Yongyue WangState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Wen YiState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Shangshang LuState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Kebin HeState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Huan LiuState Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.

Indexed as

artificial intelligencebig datacarbon neutralityintelligent transportation systemtransportation emissions

Identifiers

PMID40264962
PMCPMC12010448

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.