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.

The primary goal of Transportation systems is to provide transportation accessibility to opportunities. Equitable access to essential destinations encompassing social, recreational, educational, and civic opportunities needs to be more consistent across different social groups. This study evaluates the disparities in social justice using social equity as a measure of transit access and walk access to non-work amenities. These non-work amenities include grocery stores, personal services, retail outlets, recreational venues, entertainment centers, and healthcare facilities in the U.S. Logistic regression models are developed using the 2017 National Community Livability Survey data. The results indicate regressive public transit access for socially disadvantaged groups, including older citizens, non-drivers, Medicare/Medicaid beneficiaries, and non-metropolitan residents. Walk access inequities similarly affect older individuals, non-drivers, the physically disabled, the unemployed, students, women, and non-metropolitan residents. This research emphasizes the importance of addressing transit and walk-access inequities to non-work amenities within transportation systems. By acknowledging the disparities in transportation equity, decision-makers and communities can foster more inclusive and equitable access to essential destinations, thereby promoting social cohesion and overall community well-being.

This study investigates the effects of transportation, specifically public transit, the built environment, and sociodemographic factors on community quality of life (QOL) and individuals’ overall life satisfaction (OLS) in the United States. Using data from the 2017 National Community Livability Survey (NCLS), which includes responses from 994 adults across all 50 states, structural equation modeling (SEM) was applied to examine four latent constructs: the built environment, access to amenities, transportation quality, and support for transit funding. Additional observed variables included perceived importance of public transit, public transit need, ease of travel, and key sociodemographic indicators. Results show that the built environment and transportation quality significantly enhance community QOL but do not have a direct effect on individual OLS. In contrast, support for transit funding has a significant positive effect on individual OLS but not on community QOL. Public transit and walking access to amenities such as parks and recreation facilities, grocery stores, healthcare services, etc., improve both the built environment’s perceptions and community QOL. Interestingly, the perceived importance of public transit negatively influences community QOL, while perceived transit need negatively impacts individual OLS. Ease of travel is the only factor with a significant positive effect on both outcomes. Sociodemographic factors such as gender, age, income, health, education, and race are also found to significantly influence individual life satisfaction. These findings suggest that enhancing perceptions of the built environment, improving transportation quality—particularly public and active transit—and ensuring ease of travel can contribute to improved quality of life. Policies that integrate transportation planning with broader livability and well-being goals may offer more inclusive benefits at both the community and individual levels.

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.