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.

Selecting the right maintenance contractor is crucial for efficient operation and project success. Traditionally, this selection has been cost-driven, but the ever-growing complexity of projects has led to a shift towards best-value selection. The best value selection criteria evaluate the contractors based on factors like experience and past performance, along with the proposed cost. However, this approach lacks substantiated knowledge of these factors and often includes factors that cannot be validated at the time of procurement. This paper proposes a framework that applies the Analytical Hierarchy Process (AHP) to the maintenance contractor selection process. A detailed literature review was carried out to identify factors involved in maintenance selection. Data were collected from experts through a questionnaire developed based on the identified factors, facilitating AHP implementation. Substantiation strategies were identified using expert judgments. Our findings reveal that past performance criteria hold the maximum weight in the selection process. The proposed framework offers a more comprehensive approach for selecting maintenance contractors, ensuring both value and efficiency.

The three-wheeled motorized rickshaw (3-MR) is the dominant mode of transportation in developing countries, primarily for short trips with a passenger capacity of four to six and is also used for small-scale goods transport. 3-MRs are often linked to road traffic crashes, which create serious socioeconomic and public health challenges. While early research focused primarily on the safety of two- and four-wheeled vehicles, there has been limited investigation into the safety dynamics of 3-MR crashes. This study aims to identify crash hotspots using 3-MR crash data (January 2022 to April 2023) from Rawalpindi, Pakistan. The study uses advanced spatial analytic methods, including emerging hotspot analysis with a space–time cube spatial autocorrelation, which has recently gained popularity for incorporating the temporal dimension in spatial analysis. Incremental spatial autocorrelation was first used to determine the threshold distance, followed by Moran's I and Getis-Ord Gi* to reveal recurring hotspot patterns in 3-MR crashes. Further utilizing the emerging hotspot analysis technique, substantial spatiotemporal clustering is identified. Hotspots were found to be associated with major bus stops, commercial areas, intersections, hospitals, airports, and high-density residential areas. The findings of this study provide valuable insights for stakeholders to identify hotspot locations, supporting targeted policies to reduce 3-MR crashes and improve road safety, particularly in developing countries with similar transportation modes, such as Bangladesh, Pakistan, India, and Sri Lanka.

Congestion persists despite various demand management techniques, particularly for handling recurrent congestion. However, non-recurrent congestion from events like VIP movements poses unique challenges, especially during peak hours. This study investigates the environmental and economic impacts of road blockages due to VIP movements in developing countries, focusing on Pakistan. Considering practiced standard operating procedures associated with VIP movements, this study finds significant delays and economic burdens in debt-ridden economies. It uses discrete choice modeling and microsimulation techniques to evaluate the value of travel time and quantifies road blockage effects on fuel consumption, travel time, and carbon emissions. Data from central blockage locations in Rawalpindi and Islamabad reveal a value of travel time estimated at 1.77 USD/h, with income and gender significantly influencing mode choices during VIP movements. Moreover, road blockages exceeding two minutes substantially negatively impact the environment and economy, particularly in developing nations. Urgent action is needed for effective mitigation strategies and sustainable transportation policies to address the detrimental effects and promote alternative transportation modes. Recommendations include limiting VIP blockages to a maximum of two minutes and implementing policies to discourage private car usage. Despite limitations, the study emphasizes the critical role of sustainable transportation policies in enhancing the well-being of road users in developing nations.