TY - JOUR
T1 - Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data
AU - Mishra, Sumit
AU - Singh, Vishal
AU - Gupta, Ankit
AU - Bhattacharya, Devanjan
AU - Mudgal, Abhisek
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Mishra, S., Singh, V., Gupta, A., Bhattacharya, D., & Mudgal, A. (2023). Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data. Transportation Letters, 15(4), 296-307. https://doi.org/10.1080/19427867.2022.2050493 ----The present work in the paper was not funded by any organization. One of the coauthor, Devanjan Bhattacharya has received funding from UKRI ESRC Impact Acceleration Grant (ES/T50189X/1), and European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie COFUND Grant Agreement No. 801215: TRAIN@Ed: ‘Transnational Research And Innovation Network At Edinburgh.’
PY - 2023/4/21
Y1 - 2023/4/21
N2 - Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.
AB - Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.
KW - Crowdsourced data
KW - Google Map API
KW - Traffic signal optimization
KW - Real-time congestion management
KW - AIMD based signalling
UR - http://www.scopus.com/inward/record.url?scp=85126530759&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000768096100001
U2 - 10.1080/19427867.2022.2050493
DO - 10.1080/19427867.2022.2050493
M3 - Article
SN - 1942-7867
VL - 15
SP - 296
EP - 307
JO - Transportation Letters
JF - Transportation Letters
IS - 4
ER -
