Abstract:
Global urbanization and increasing traffic volume have intensified traffic congestion on transportation infrastructure. This highlights the critical need to
implement more intelligent and proactive transportation infrastructure solutions.
Transportation infrastructure on freeway such as toll plazas are crucial for traffic
flow and revenue, yet they often face congestion challenges, leading to longer
queues, increased travel times, and environmental issues. To combat toll plazas
congestion and optimize traffic management, this study proposes a proactive traffic
control strategy using advanced technologies. The approach involves deep learning
convolutional neural network models (YOLOv7-DeepSORT) for vehicle counting
and long short-term memory model for short-term arrival rate prediction. When
projected arrival rates exceed a threshold, the strategy proactively activates Variable
Speed Limits (VSL) and Ramp Metering (RM) strategies during peak hours.
Validated through a case study at Ravi Toll Plaza using PTV VISSIM, the proposed
method reduces queue length by 57% and vehicle delays by 47%, while cutting fuel
consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally,
an implementation framework alongside the proposed strategy, this study aims to
bridge the gap between theory and practice, making it easier for toll plaza operators
and transportation authorities to adopt and benefit from the advanced traffic
management techniques. Ultimately, this study underscores the importance of
integrated and proactive traffic control strategies in enhancing traffic management,
minimizing congestion, and fostering a more sustainable transportation system.
Intelligent Transportation System, Variable Speed Limits, Ramp Metering and Deep
Learning, YOLOV7-Deep-SORT and LSTM
