Intelligent Technologies in Traffic Flow Control: A Review

Xie, Aoyu · 2025 · Crossref

DOI: 10.54254/2755-2721/2025.20047

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This review paper addresses the critical challenge of traffic congestion in modern cities, driven by rapid urbanization and increasing vehicle numbers. The authors argue that while traffic flow prediction techniques are essential for mitigating economic losses and quality-of-life declines, relying on a single technology is insufficient for complex transportation environments. Consequently, the study examines the individual roles and limitations of three key intelligent technologies—5G, Artificial Intelligence (AI), and Vehicular Networking (V2X)—and proposes a fusion scheme that leverages their complementary advantages to optimize intelligent traffic management systems. The paper systematically reviews the capabilities of each technology. 5G is analyzed for its high-speed, low-latency, and large-scale connectivity features, specifically highlighting Massive MIMO, millimeter-wave bands, and network slicing. However, the authors note that 5G faces capacity limitations during traffic surges or emergencies. AI is evaluated for its data processing power, with a focus on deep learning models like Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) for handling sequential traffic data, though these models are constrained by their dependence on high-quality data. V2X is reviewed for its ability to facilitate real-time information exchange between vehicles and infrastructure, improving safety and mobility, but requiring robust network support. The review also cites specific studies, such as Li’s combination of Support Vector Regression with Artificial Bee Colony Optimization and Ho and Ioannou’s use of Artificial Neural Networks for highway flow control. The findings emphasize that technological convergence is necessary to overcome individual shortcomings. The paper presents case studies demonstrating this synergy. One study by Abdellah utilized LSTM models within 5G V2X networks to predict traffic flow and prevent communication congestion, finding that a transmission rate of 4 packets per second yielded optimal performance. Another study by J employed a Restricted Boltzmann Machine combined with Cuckoo Search Optimization (CSORBM-TA) for traffic analysis in 5G V2X systems. This model achieved a Mean Absolute Percentage Error (MAPE) of 9.26% and a Root Mean Square Error (RMSE) of 0.5187, outperforming traditional LSTM models which recorded a MAPE of 20.34%. Additionally, simulations using SUMO and OMNeT++ showed that V2X could increase average speed by 40% and traffic flow by 36% in high-density scenarios. The significance of this work lies in its conclusion that the synergistic application of 5G, AI, and V2X provides a feasible solution for future Intelligent Transportation Systems. By integrating 5G’s communication infrastructure, V2X’s real-time data generation, and AI’s analytical capabilities, the proposed fusion scheme effectively alleviates congestion and enhances road safety. The paper asserts that this multi-technology approach is crucial for developing efficient, intelligent traffic management systems capable of handling the dynamic complexities of modern urban transportation.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• automated enforcement cameras