@Article{crph1151,
AUTHOR = {Nampalli, Rama Chandra Rao},
TITLE = {Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent Systems},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {86-99},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1151},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2021.1151},
ABSTRACT = {Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.},
}

%0 Journal Article
%A Nampalli, Rama Chandra Rao
%D 2021
%J Current Research in Public Health

%@ 2831-5162
%V 1
%N 1
%P 86-99

%T Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent Systems
%M doi:10.31586/jaibd.2021.1151
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1151

TY  - JOUR
AU  - Nampalli, Rama Chandra Rao
TI  - Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent Systems
T2  - Current Research in Public Health
PY  - 2021
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 86
EP  - 99
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1151
AB  - Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.
DO  - Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent Systems
TI  - 10.31586/jaibd.2021.1151
ER  -