Assessment of urban traffic vehicle emissions reduction after integration of ‘traffic light countdown’ module in navigation

Abstract As China experiences a dramatic surge in carbon emissions from the transportation sector, reducing these emissions has become crucial in mitigating climate change. Since 2020, Navigation Service Providers has collaborated with traffic police departments to integrate the ‘Traffic Light Countdown’ module (TLCM) in the navigation applications in multiple cities across China. This initiative aims to improve vehicle traffic efficiency, reduce transportation carbon emissions, and enhance public driving safety simultaneously. To quantify the effectiveness of this module, we selected a section of Qilu Avenue in Jinan City as a case study. Localization of Motor Vehicle Emission Simulator (MOVES) model parameters to accurately reflect the specific conditions of the study area was achieved by incorporating local traffic, climate, vehicle, and fuel information. Utilizing this localized model, we evaluated the changes in traffic conditions, drivers’ driving behavior and running exhaust emissions in the study. The results indicate that, following the integration of the TLCM, the average vehicle speed on the selected road section increased from 29.5 to 31.8 km h −1 . Moreover, the ratio of rapid acceleration per vehicle decreased from 0.08 to 0.05, while the rapid deceleration dropped from 0.04 to 0.01. In terms of carbon emissions, the daily average carbon emissions for this road section decreased by 4.4%, with a particularly significant reduction during the evening rush hours and post-evening rush hours (16:00–22:00 Local Standard Time). This is the first quantitative assess the potential effect of the TLCM on improving road traffic conditions and reducing carbon emissions. These findings demonstrate that the integration of TLCM in navigation applications has not only improved traffic efficiency, reduced the occurrence of risky driving behaviors (rapid acceleration and deceleration) and thus enhanced road safety, but also positively contributed to the reduction of carbon emissions from transportation and the mitigation of climate change.

Authors

Institutions

Publication Details

Journal
Environmental Research Communications
Published
2025-03-01
DOI
https://doi.org/10.1088/2515-7620/adbfdf
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessment of urban traffic vehicle emissions reduction after integration of ‘traffic light countdown’ module in navigation

Na Qin, Wei Qin, Xifan Chen, M. Zhang et al.
Environmental Research Communications
Vehicle emissions and performance
article

Assessment of urban traffic vehicle emissions reduction after integration of ‘traffic light countdown’ module in navigation

Na Qin, Wei Qin, Xifan Chen, M. Zhang, Qian Liu, Jing Wang, Siyuan Wang, Hao Wang, Xiangmin Li, Hao Fu, Di Liu, Sixian Qin
article en

Abstract

Abstract As China experiences a dramatic surge in carbon emissions from the transportation sector, reducing these emissions has become crucial in mitigating climate change. Since 2020, Navigation Service Providers has collaborated with traffic police departments to integrate the ‘Traffic Light Countdown’ module (TLCM) in the navigation applications in multiple cities across China. This initiative aims to improve vehicle traffic efficiency, reduce transportation carbon emissions, and enhance public driving safety simultaneously. To quantify the effectiveness of this module, we selected a section of Qilu Avenue in Jinan City as a case study. Localization of Motor Vehicle Emission Simulator (MOVES) model parameters to accurately reflect the specific conditions of the study area was achieved by incorporating local traffic, climate, vehicle, and fuel information. Utilizing this localized model, we evaluated the changes in traffic conditions, drivers’ driving behavior and running exhaust emissions in the study. The results indicate that, following the integration of the TLCM, the average vehicle speed on the selected road section increased from 29.5 to 31.8 km h −1 . Moreover, the ratio of rapid acceleration per vehicle decreased from 0.08 to 0.05, while the rapid deceleration dropped from 0.04 to 0.01. In terms of carbon emissions, the daily average carbon emissions for this road section decreased by 4.4%, with a particularly significant reduction during the evening rush hours and post-evening rush hours (16:00–22:00 Local Standard Time). This is the first quantitative assess the potential effect of the TLCM on improving road traffic conditions and reducing carbon emissions. These findings demonstrate that the integration of TLCM in navigation applications has not only improved traffic efficiency, reduced the occurrence of risky driving behaviors (rapid acceleration and deceleration) and thus enhanced road safety, but also positively contributed to the reduction of carbon emissions from transportation and the mitigation of climate change.

Environmental Research CommunicationsVol. 7(3)
Nanjing Surveying and Mapping Research Institute (China) (CN), Research Center for Eco-Environmental Sciences (CN), University of Chinese Academy of Sciences (CN), Hubei University (CN), Baidu (China) (CN)
National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China
Openalex Percentile: Top 96%
Vehicle emissions and performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.