TY - JOUR
T1 - Congestion Detection and Propagation in Urban Areas Using Histogram Models
AU - El-Sayed, Hesham
AU - Thandavarayan, Gokulnath
N1 - Funding Information:
Manuscript received September 14, 2016; revised January 16, 2017; accepted January 31, 2017. Date of publication February 8, 2017; date of current version November 14, 2018. This work was supported by the Roadway, Transportation, and Traffic Safety Research Center of the United Arab Emirates University under Grant 31R058.
Publisher Copyright:
© 2014 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Rapid growth of urbanization makes the roadways exacerbate many problems like traffic congestion, road accidents, and passenger discomfort. Many actions have been taken globally to solve or reduce this impact but still the congestion problem seems to be persistent globally. In this paper, we propose a new histogram-based model for congestion detection. We subsequently extended our model with the base platform concept and use Intelligent Transportation System (ITS) technologies to provide a novel rerouting strategy. The proposed model enables the microscopic visualization of the traffic patterns for every individual lane and predicts the congestion in priori and takes actions proactively. The rerouting strategy used in our approach provides a novel solution to the congestion problem by taking precaution measures prior to the critical point of congestion occurrence. The proposed algorithm is compared with various existing algorithms and the simulation results show that the proposed model addresses the congestion problem effectively and provides better solution compared to existing algorithms.
AB - Rapid growth of urbanization makes the roadways exacerbate many problems like traffic congestion, road accidents, and passenger discomfort. Many actions have been taken globally to solve or reduce this impact but still the congestion problem seems to be persistent globally. In this paper, we propose a new histogram-based model for congestion detection. We subsequently extended our model with the base platform concept and use Intelligent Transportation System (ITS) technologies to provide a novel rerouting strategy. The proposed model enables the microscopic visualization of the traffic patterns for every individual lane and predicts the congestion in priori and takes actions proactively. The rerouting strategy used in our approach provides a novel solution to the congestion problem by taking precaution measures prior to the critical point of congestion occurrence. The proposed algorithm is compared with various existing algorithms and the simulation results show that the proposed model addresses the congestion problem effectively and provides better solution compared to existing algorithms.
KW - Congestion estimation
KW - congestion propagation
KW - histograms
KW - intelligent transportation system (ITS)
KW - route guidance
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U2 - 10.1109/JIOT.2017.2665662
DO - 10.1109/JIOT.2017.2665662
M3 - Article
AN - SCOPUS:85056792773
SN - 2327-4662
VL - 5
SP - 3672
EP - 3682
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 5
M1 - 7847390
ER -