Urban safety: an image-processing and deep-learning-based intelligent traffic management and control system
S Reza, HS Oliveira, JJM Machado, JMRS Tavares - Sensors, 2021 - mdpi.com
With the rapid growth and development of cities, Intelligent Traffic Management and Control
(ITMC) is becoming a fundamental component to address the challenges of modern urban …
(ITMC) is becoming a fundamental component to address the challenges of modern urban …
[HTML][HTML] COVID-19 pandemic impacts on traffic system delay, fuel consumption and emissions
A dramatic reduction in traffic demand has been observed during the COVID-19 pandemic,
producing noticeable declines in traffic delays, energy consumption, and emissions. This …
producing noticeable declines in traffic delays, energy consumption, and emissions. This …
Traffic Signal Optimization to Improve Sustainability: A Literature Review
Optimizing traffic signals to improve traffic progression relies on minimizing mobility
performance measures (eg, delays and stops). However, delay and stop minimizations do …
performance measures (eg, delays and stops). However, delay and stop minimizations do …
Ecology based network traffic control: A bee colony optimization approach
The majority of fuel consumed in traffic on urban arterials is related to driving in congested
traffic, characterized by frequent speed fluctuations and stops at signalized intersections. A …
traffic, characterized by frequent speed fluctuations and stops at signalized intersections. A …
The impact of socio-demographic characteristics and driving behaviors on fuel efficiency
Fuel efficiency intuitively differs between drivers, depending on their socio-demographic
characteristics and driving behavior, and can change even for the same driver in different …
characteristics and driving behavior, and can change even for the same driver in different …
A simulation model to reduce the fuel consumption through efficient road traffic modelling
Controlling traffic dynamically is a complex task which includes meeting the increasing traffic
demand and decreasing delays at the intersection. The current traffic controllers based on …
demand and decreasing delays at the intersection. The current traffic controllers based on …
Vehicle delay estimation at signalized intersections using machine learning algorithms
MEC Bagdatli, AS Dokuz - Transportation research record, 2021 - journals.sagepub.com
Accurate determination of average vehicle delays is significant for effective management of
a signalized intersection. The vehicle delays can be determined by field studies, however …
a signalized intersection. The vehicle delays can be determined by field studies, however …
Two-layer coordinated reinforcement learning for traffic signal control in traffic network
F Ren, W Dong, X Zhao, F Zhang, Y Kong… - Expert Systems with …, 2024 - Elsevier
Intersection traffic signal control considering vehicle emissions has become an important
topic, however, the decision complexity of traffic signal control increases dramatically in a …
topic, however, the decision complexity of traffic signal control increases dramatically in a …
Optimizing Traffic Flow With Reinforcement Learning: A Study on Traffic Light Management
A Merbah, J Ben-Othman - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
The non-adaptive management of traffic lights has proven inefficient for a number of
drawbacks. They mainly impinge on CO2 emissions, fuel consumption, traffic waiting time …
drawbacks. They mainly impinge on CO2 emissions, fuel consumption, traffic waiting time …
Estimation of traffic stream density using connected vehicle data: Linear and nonlinear filtering approaches
The paper presents a nonlinear filtering approach to estimate the traffic stream density on
signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle …
signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle …