Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, 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, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from 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.

Open-access multilane highways have a significant share in the highway network of Pakistan and other developing countries in Asia. These highways have high access density, design inconsistency, and other operational characteristics that differentiate them from partially controlled-access multilane highways. This study identified significant factors affecting the mobility of open-access highways based on road users’ perceptions as well as field observations. An interview-based questionnaire survey from local respondents and an in-service road survey formed the database for the present study. Questionnaire survey results showed that heavy traffic was the most critical mobility influencing factor on open-access multilane highways, followed by road width and condition, whereas the result of multilinear regression revealed that the most significant variable was access density, followed by the flow and pedestrian crossings. However, it was concluded that controlling access density, preventing pedestrian crossings, and improving pavement condition will improve the mobility of open-access multilane highways.

Current imbalance in freight share (96% roads vs. 4% railways) in Pakistan, is impacting the performance of highways and is causing under-utilization of existing railway network. Expected increased freight demand due to China-Pakistan Economic Corridor (CPEC), necessitates a strategic shift towards multimodal freight transportation. With this background, this research provides a multidimensional analysis of strategies to enhance Pakistan Railways (PR) freight capacity by shifting calculated freight load from highways under CPEC scenario and examines their effects on the sustainability of transportation infrastructure owing to reduction in highways maintenance and rehabilitation (M&R) expenditure. Addressing the crucial issue of over-reliance on roads for freight transportation, this research employs economic indicators in statistical modeling to forecast freight demand. Three strategic railway freight capacity enhancement options have been proposed for 2025 and 2030. This study highlights that PR would be able to accommodate up to 24.07% of total freight by 2025 without compromising railway passenger load. Additionally, this study reveals that if the current freight share distribution remains unchanged, then highways M&R expenditure is expected to be increased by over 84%, by the year 2025, compared to the year 2015. However, highways M&R expenditure is expected to be reduced by 78.77% and 60.56% by the year 2025 and 2030, respectively, basing on the proposed freight load distribution strategies. This study would be highly beneficial for transportation agencies in optimal freight transportation management while ensuring efficient sustainability of transportation infrastructure, especially for the countries that have a high dependency on roads for freight transportation.