Urban centers worldwide confront mounting pressure from accelerating vehicle ownership and inadequate infrastructure adaptation. Peak-hour congestion degrades travel efficiency, exacerbates energy waste, harms the environment, and undermines residents’ quality of life. Published research introduces multifaceted analytical frameworks synthesizing statistical methodologies, neural network architectures, and microscopic simulation platforms to revolutionize how transportation agencies forecast demand, evaluate capacity constraints, and implement safety interventions.
The analytical framework employs hybrid forecasting that merges regression techniques with modern machine learning models. Research shows an evolution from multivariate regression of time-fluctuation, weather, and event variables to deep learning approaches. Multi-layer perceptron captures nonlinear traffic patterns, while Long short-term memory networks (LSTM) enhance temporal prediction. Validation on real-world data achieves accuracy within three percent, supporting Proactive signal-timing adjustments, lane reconfigurations, and congestion control during peak periods.
Capacity evaluation methods provide critical infrastructure assessment tools for transportation planning and flow bottleneck detection.. Research integrates Highway Capacity Manual (HCM) methodologies with VISSIM microsimulation. HCM frameworks analyze throughput based on flow, geometry, and signal coordination, while VISSIM models Vehicle dynamic behavior in complex networks. The combined approach identifies congestion points and evaluates improvements from geometric changes to intelligent control systems.
Safety-focused research develops countermeasure frameworks addressing accident causes through infrastructure standardization and technological solutions. Recommended interventions include standardized lane widths, advanced drainage, high-friction surfaces, and intelligent transportation systems with adaptive signal timing and real-time monitoring.
Implementation methodologies translate theory into operational traffic engineering across signal systems, roadway geometry, maintenance, and intelligent infrastructure. Documented applications involve sensor network deployment for real-time flow aggregation and predictive modeling during peak demand. Capacity-based planning establishes throughput limits and identifies critical constraints, while corridor management integrates analytics and simulation to guide dynamic pricing and temporary capacity expansion during traffic surges.
This research originates from Naizhong Cui, holding graduate credentials including a Master of Science in Project Management from Harrisburg University of Science and Technology, a Master of Science in Civil and Environmental Engineering from the University of Illinois at Urbana-Champaign, and an undergraduate foundation in Civil Engineering from Purdue University. Professional specialization encompasses comprehensive traffic engineering, including signal timing design, signing and pavement marking specifications, maintenance of traffic coordination, lighting design, and intelligent transportation infrastructure deployment. Project contributions span signal reconstruction initiatives, interstate corridor maintenance planning, and analytical studies supporting safety improvement programs and multimodal facility design, with representative projects including the MD 140 Smart Signal System Reconstruction, I-695 (Francis Scott Key Bridge) Repair and Subgrade Rehabilitation near Bear Creek, MCDOT Roadway Departure Analysis, and Richmond Highway BRT signal design.
These contributions advance transportation management through rigorous integration of predictive capabilities, infrastructure optimization principles, and evidence-based safety engineering. By bridging traditional civil engineering practice with computational intelligence and smart infrastructure technologies, this work establishes actionable frameworks for accident reduction and capacity utilization improvement. Demonstrated forecasting precision combined with systematic hazard identification methodologies sets enhanced standards for contemporary traffic engineering, providing transportation agencies with practical implementation guidance for constructing more resilient, efficient, and safe urban mobility systems.