An AI-Enabled Intelligent Traffic Guidance System for Expressway Networks Based on IoT and GIS Integration

A Developed generalized mathematical traffic flow model for a single intersection

A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.

Adaptive traffic control systems in intelligent transportation systems: a systematic literature review

Traffic congestion continues to pose a major challenge, particularly in urban areas where infrastructure capacity and data availability are limited. An adaptive traffic control system (ATCS), part of intelligent transportation systems (ITS), can improve road network performance by dynamically adjusting signal timing in response to real-time traffic conditions. However, research on ATCS remains scattered and diverse, making it difficult to obtain a comprehensive picture of its trends, methods, challenges, and effectiveness. This study seeks to deliver a comprehensive overview of ATCS through a systematic literature review (SLR) involving 77 full-text articles using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The analysis focuses on four main aspects: research trends, implementation barriers, methods and technologies used, and performance evaluation metrics. The study’s findings indicate a significant increase in publications after 2019, with simulation-based studies dominating and major contributions coming from China and the United States. Methodological advancements have gradually transitioned from traditional rule-based approaches toward more advanced techniques, including reinforcement learning (RL), deep RL, multi-agent RL, and hybrid approaches. Various enabling technologies, such as edge computing, computer vision, floating-car data (FCD), sensor fusion, digital twins, and vehicle-to-everything (V2X) communication, are increasingly being integrated. However, real-world implementation still faces various challenges, including data limitations, low connected vehicle (CV) penetration, gaps between simulation and field conditions, and security and infrastructure readiness issues. Performance evaluation is also evolving into a multidimensional process encompassing mobility, safety, the environment, and system sustainability. Overall, there is no single universally optimal method; the performance of ATCS is strongly influenced by the nature of the available data and the level of system readiness. Therefore, future research should focus on improving field validation, standardizing evaluation methods, developing solutions that adapt to local conditions, and strengthening system security and reliability to support real-world implementation.

Practical Applications and Systemic Bottlenecks of Intelligent Transportation Systems in Urban Traffic Management: A Case-Based Analysis

The sharp increase in urban vehicle ownership has triggered severe traffic congestion and low road network operational efficiency, making intelligent transportation systems (ITS) a core technical means to optimize urban traffic management. Nevertheless, existing studies have obvious deficiencies: there is a lack of systematic collation and comparative analysis of ITS practical applications across diverse urban scenarios, and the exploration of developmental constraints is fragmented without integrating inherent correlations among industrial standards, equipment compatibility and operational costs. This paper adopts literature research, case analysis and inductive methods to select typical urban cases in Indonesia, Poland and Malaysia, exploring the practical effects of ITS on signal timing optimization, short-term traffic flow prediction and multi-intersection collaborative control under traffic disturbances. The results indicate that simulation modeling, regression algorithms and deep reinforcement learning can effectively reduce traffic queue length and travel delay, and improve traffic prediction accuracy and road network operation efficiency. This study further identifies three systemic bottlenecks restricting ITS large-scale promotion: poor adaptability to extreme weather and unexpected traffic incidents, insufficient standardization and scalability for city-wide deployment, as well as data privacy risks and user psychological resistance. It also clarifies research limitations and proposes future research directions, aiming to provide theoretical support and practical references for urban intelligent traffic governance and bottleneck breakthroughs.

Construction of a Smart City Infrastructure Dynamic Monitoring and Emergency Dispatch Model Supported by High-Precision Spatiotemporal Big Data

The increasing integration of IoT sensors, fiber-optic sensing and wireless communication links into urban architecture has made smart city infrastructure more vulnerable to disruptions as these complex, dynamic operating environments grow increasingly diverse with connectivity. Traditional static management frameworks cannot solve the problem of real-time perception and comprehensive planning of cross-system hazards. This paper proposes a dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data. A data twin view of an integrated system based on heterogeneous data in the form of IoT sensing, remote sensing, GIS, and social sensing is established with a common spatiotemporal reference point. Accordingly, multi-layer complex networks are used to describe infrastructure dependencies, and spatiotemporal graph convolutional networks are integrated to obtain dynamic risk propagation simulations. In addition, a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling. The results show that the model’s anomaly identification accuracy is 95.3, risk propagation prediction accuracy is 88.7, and only the time to generate the schedule is 4.2 seconds. The proposed data fusion and scheduling logic is consistent with smart-city systems that rely on wireless sensing, remote sensing and communication infrastructure.

Intelligent Urban Traffic Management Using Artificial Intelligence and Machine Learning Methods and Algorithms: Challenges and Opportunities

Assessing the Impact of Urban Boulevard Widening on Emergency Vehicle Mobility and Response Efficiency

Improving emergency vehicle mobility in congested urban environments is a critical challenge for transportation systems. Although roadway capacity expansions, such as widening roads, are often deployed to reduce congestion, their impact on emergency response performance is not always guaranteed, especially when delays concentrate at critical intersections. This study investigates how roadway capacity expansion affects emergency vehicle performance by using a microscopic traffic simulation framework. The study was applied to a real urban corridor in Mohammedia, Morocco, to provide a solid base for simulations with real-world conditions. A SUMO model was calibrated to represent two roadway configurations: a baseline two-lane layout and a three-lane post-widening scenario. Traffic volumes from 1056 to 3520 vehicles per hour were simulated, and performance was assessed using three emergency-specific indicators: Emergency Response Time (ERT), Delay Ratio (DR), and Priority Mobility Index (PMI). An initial single-run comparison suggested a substantial ERT reduction under moderate demand (343.40 s to 270.90 s, 21.11%); however, a 30-seed replication with paired Wilcoxon signed-rank tests shows that this and nearly all other widening effects are not statistically distinguishable from stochastic simulation noise. Only one of 12 emergency vehicle comparisons (Priority Mobility Index at 18:00) reached significance, and it favored the baseline configuration; none of 12 general traffic comparisons improved significantly, and general traffic was significantly slower under the widened configuration at 22:00 (p < 0.01). A supplementary sensitivity analysis (±20% emergency vehicle demand share) further shows that Delay Ratio conclusions are considerably more sensitive to this assumption (up to 34% relative change) than ERT or PMI (under 8%). These findings indicate that, in this network, roadway capacity expansion alone does not deliver a statistically robust improvement in either emergency vehicle or general mobility, and that a persistent signalized-intersection bottleneck remains the dominant constraint irrespective of lane geometry. The study provides a replicable, statistically validated simulation framework for assessing roadway capacity expansion effectiveness and cautions against single-run comparisons, which can substantially overstate the causal effect of infrastructure interventions in microscopic traffic simulation studies.