Concise Encyclopedia of Traffic & Transportation Systems

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

In this paper, we have made a comprehensive study about the key technologies including applying wireless sensor networks to the traffic monitoring network, its traffic flow forecast based on gray forecasting model and traffic congestion control. According to the features that wireless sensor networks have no space constraints, flexible distribution, mobile convenience and quick reaction, we present a scheme that uses wireless sensor networks to monitor city transport vehicles and have designed a traffic monitoring system based on wireless sensor network that is applicable to all types of city environment. With the system, we can monitor the important roads that are easily blocked and find out the time changing law of traffic congestion, and then put the focus on monitoring them in order to greatly reduce the investment and achieve high efficiency. As far as the traffic flow forecasting methods concerned, we use Adaptive GM (1, 1) Model which have a real-time rolling forecast for city traffic and have a better forecast results. Because of the fewer study about the traffic congestion control in the current academia, we make a deep study about the traffic congestion control issues in this article. Learn from mature congestion control algorithm in computer network, we have designed an algorithm of traffic flow congestion control and scheduling for traffic network, which is called TRED. We have used it for real-time traffic scheduling and have opened up a new way to study and solve traffic congestion control problems.

Road traffic control rooms rely on human operators to monitor and interact with information presented on multiple displays. Past studies have found inconsistent use of available visual information sources in such settings across different domains. In this study, we aimed to broaden the understanding of observer behaviour in control rooms by analysing a case study in road traffic control. We conducted a field study in a live road traffic control room where five operators responded to incidents while wearing a mobile eye tracker. Using qualitative and quantitative approaches, we investigated the operators’ workflow using ergonomics methods and quantified visual information sampling. We found that individuals showed differing preferences for viewing modalities and weighting of task components, with a strong coupling between eye and head movement. For the quantitative analysis of the eye tracking data, we propose a number of metrics which may prove useful to compare visual sampling behaviour across domains in future.

<div class="htmlview paragraph">Recently, the control of traffic flow has been proposed using several types of criteria (e.g. minimum-time control, minimum fuel control and so on). Most recently, an environmental noise pollution problem caused by the road traffic is being aggravated more and more by the consolidation and expansion of roadway system particularly in urban areas. However, the objective of this paper is to control road traffic flow by regulating traffic noise propagation in an urban area in Cairo city. The results of traffic noise prediction obtained by trending of the experimental data collecting by systematic noise measurement and the evaluation of the traffic noise which is in close connection with physical parameters of traffic flow and noise propagation characteristics is presented. The analysis of road traffic flow noise control is based on the mixed integer non-linear programming technique, where the optimal control strategy is used. The results indicate that significant information gained for controlling road traffic in urban areas.</div>

This paper presents an intellectual decision making system for the vessel traffic service (VTS). Its aim is to increase the safety and efficiency of the traffic and the effectiveness of VTS. In general, the navigational decision making process in a traffic control system is based on radar and navigation information although other systems, such as automatic identification systems, GPS can be used with good effect. The application of an intellectual component during a decision making process increases safety and economic effects of the shipping as well as reduces the operator's workload.

Experience of the State of Qatar Introduction: Since the second half of the last decade of the twentieth century, Qatar has witnessed the implementation of a comprehensive development plan in various economic, urban and social scales. This comprehensive plan granted greater attention to developing the infrastructure and providing the necessary facilities. Road network development, with world-class engineering standards, was conducted with a view to improving the transportation system and providing suitable conditions for its sustainability. Another objective was to align the carrying capacity and design components of this system with the increase in the number of vehicles and population in the wake of the rising numbers of foreign workers and technical personnel induced by the implementation of the plan, along with the soaring number of various driver licenses and different types of registered vehicles. Components of the Study The study examined and analyzed statistical data of a number of elements, some of which are shown in Figure (1) below: 1. Statistics documented by the relevant authorities, showing a rise in the number of foreigners, which drove the population to grow four folds; 2. The number of vehicles registered at the Traffic Department, which also increased in proportion to the rate in (1) above; 3. The significant increase recorded in driver licenses awarded by the Traffic Department. The most important element in these variables is the number of injuries caused by road traffic accidents as a natural result reflecting the above-mentioned increases. Figure (2) shows the number of fatalities in road traffic accidents between 2000 and 2014, which peaked in 2006 as the fatality rate recorded 270, unprecedented in the State. Research Method The research method has mainly relied on collecting, analyzing and surveying the statistics and events documented by the relevant departments and authorities in the Ministry of Interior and other related bodies. This study comes in line with the vision and mission of the Ministry of Interior (in its capacity as the institution in charge of maintaining security and safety and protecting public and private properties, as well as establishing security and safety in the country through a highly professional performance of security services in a genuine partnership with society). During the implementation stage of the study, the documented findings have been treated as outcomes of the measures and events carried out by the Ministry with the aim of reducing road traffic accidents and mitigating the related human and material losses. The measures addressed in the study include the following: 1. The Ministry of Interior developed the legislation of Traffic Law no. 19 of 2007 to keep pace with the developments in the State, whereby stricter sanctions were introduced to the lawbreakers, mechanisms of testing drivers were activated and driving schools were developed. 2. The employment of sophisticated technologies in road traffic control and management. 3. Awareness-raising programs for the benefit of road users were adopted and carried out in a highly professional manner. Conclusions 1. The study and analysis the measures adopted show good results, as fatality rates in recent years, until 2014, are always below the rate recorded in 2006. 2. Traffic police patrols› reports and traffic surveillance systems show the development in traffic awareness and control. 3. The effective measures and programs carried out in driving schools have their positive good effects on schools, trainers and trainees. 4. This paper exposes an important aspect of development in the road traffic safety in general through the measures and effective regulations adopted. The research work also highlights some findings noticed on the road network outside cities (highways in particular) Recommendations 11. The study findings show the importance of putting laws and regulations into practice and the need for reviewing them from time to time in accordance with the changes in the components of transportation system›s design, implementation and operation. 22. It is necessary to adopt sophisticated technologies in traffic control and organization. The use of modern traffic technologies should be coordinated, serving as support means to the major components of traffic control: police and laws.

With the vast availability of traffic sensing data on highway, real-time traffic flow prediction is essential part of transportation, traffic control, reports of accidents and intelligent transportation systems. To satisfy the demand of traffic flow prediction, this paper presents the method of real-time traffic flow prediction based on Bayesian classifier and support vector regression (SVR). We first model the traffic flow and its relations on the roads using 3D Markov random fields in spatiotemporal domain. Based on their relations, we define cliques as combination of current road and its neighbors. The dependencies on the defined cliques are estimated by using multiple linear regression and SVR. Finally, the traffic flow at next time stamp is predicted by finding the speed level with decreasing the energy function. To evaluate the performance of the proposed method, it was tested on traffic data obtained from Gyeongbu expressway. The experimental results showed that the approach using SVR-based estimation had superior accuracy than linear-based regression.

The forecasting of traffic flow is an important part of intelligent transportation system; actual and accurate forecasting of traffic flow can give scientific support for urban traffic guidance and control. As there is big forecast error when modeling toward traffic flow data with discrete grey model DGM (1, 1), this paper amends the equal interval time sequence. According to the characteristic of time coefficient and backpropagation (BP) neural network, we propose an improved grey model by combining DGM (1, 1) model with BP neural network model. The experimental result indicates that the improved grey model is scientific and effective for the forecasting of traffic flow.

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid.

Rapid urbanization and increasing ownership of private vehicles have led to severe traffic congestion, which has become a critical challenge in modern urban areas operated with signalized intersections. This study proposes and evaluates a novel AI-based composite framework for traffic signal control optimization, which integrates a Long Short-Term Memory (LSTM) model for traffic flow prediction, a Deep Neural Network (DNN) for vehicle speed estimation, and a Genetic Algorithm (GA) for signal phase optimization under open-loop and closed-loop objectives. It has been validated through high-fidelity microscopic simulations (i.e. VISSIM), calibrated with field data at the atypical six-leg intersection in Busan, Korea. This study found that while the overall performance of the framework for 24 hours exhibits clear trade-offs (i.e. slight total traffic throughput decrease of 2.2%), its primary value lies in its demonstrated ability to effectively mitigate severe congestion during peak hours (i.e. speed increase of up to 38.5% during the evening peak), delivering significant gains in traffic quality in need. This study also revealed that the system actively performs traffic flow redistribution as an intelligent traffic control strategy. The contributions of this study are threefold. First, it provides a methodological blueprint for an interpretable, modular control framework that decouples prediction processes from optimization, offering a transparent alternative to end-to-end Reinforcement Learning (RL) models. Second, this study offers crucial empirical insight for the evaluation of adaptive traffic control systems. Their true effectiveness may be best measured by peak hour performance rather than 24-hour averages. Finally, this study makes a conceptual contribution by identifying and quantifying the ‘reality gap’ between the predictions of an offline-trained optimizer and the outcomes of a high-fidelity simulation, highlighting a fundamental challenge for the field. HIGHLIGHTS A hierarchical AI framework is an alternative to reinforcement learning model Closed-loop outperforms open-loop in optimizing real time traffic signal control A speed stabilization objective is adopted over a speed maximization approach Leading to tangible gains in traffic quality during peak hour over 24-hour average

Real-time and accurate traffic flow forecasting is very important to the intelligent traffic guidance, control and management. According to the characteristics of traffic flow, this paper proposes a new model of traffic flow forecasting based on wavelet networks and the forecasting algorithm of comparable time intervals. The structures of wavelet networks are optimized with genetic algorithm. The experiment results show that this model is superior to the common BP neural networks in the aspects of flow forecasting precision and network convergence.

On-line distributed prediction of traffic flow in a large-scale road network

With the rapid growth of transportation, all kinds of traffic data show explosive growth, and accurate and timely traffic forecasting is also particularly important. Among them, traffic flow prediction is the basis for realizing reasonable traffic guidance and control, and it is also a prerequisite for intelligent transportation. Traffic flow data belongs to a typical chaotic time series with strong non-linearity. In the application of neural network, a two-layer neural network can theoretically approach any continuous function infinitely, so as to achieve very accurate prediction. This paper proposes a predictive traffic flow model based on the combination of GRU network and BP neural network, and uses the US Twin Cities traffic data experiment to verify that the model is feasible in traffic flow prediction. The experimental results show that the combined model has high prediction accuracy and can capture the volatility of traffic flow at rush hour. At the same time, the model has strong robustness.

Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features; 2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.