PREDICTIVE MODELING FOR URBAN TRAFFIC OPTIMIZATION ENHANCING MOBILITY AND REDUCING CONGESTION COSTS

The increase in vehicle ownership and urban population puts a strain on roads and increases traffic congestion within developed and developing nations. Congestion affects an individual’s time and increases an individual’s fuel consumption and pollution output, undercutting efforts towards smart/ sustainable cities. Outdated statistical congestion predicting methods do not account for the non-linear, dynamic nature of congestion with regards to external factors such as, weather, road incidents, and infrastructure. The adoption of machine and deep learning develops the framework towards adaptable and data-driven congestion predicting methods. This study develops a framework that combines the fundamentals of machine learning algorithms, Queuing Theory, Decision Trees, Random Forests, and Deep Belief Networks (DBN) for robust and interprefig traffic congestion forecasting. The traffic flow micro-dynamics integration with Queuing Theory and the use of historical/ real-time multimodal data such as sensor feeds, GPS trajectories, and incident reports as a basis for forecasting helps in robust congestion prediction. Polynomials of moving and rest queues with defined traffic characteristics fuel flow mechanisms. The designed framework uses the DBN and Queuing Theory for robust traffic forecasting.

Traffic congestion prediction plays a guiding role in vehicle route selection. Through traffic congestion prediction, it can not only help to reduce urban traffic pressure, but also facilitate vehicle travel and save vehicle travel costs.[1]Aiming at the problem of poor accuracy of estimated traffic congestion time caused by inaccurate estimation of vehicle speed in case of serious traffic congestion, people extracted the key factors of urban traffic congestion such as average travel speed, morning and evening peaks, average travel time, average delay time, average waiting time, and number of weeks. People constructed a multi-linear regression prediction model based on R language and used the selected ones and used the selected five administrative districts of New York City (mainly on the main roads of cities and highways), the data of road traffic conditions are monitored every five minutes for calculation and validity verification.

Traffic prediction is a critical element of Intelligent Transportation Systems (ITS), enhancing road safety, traffic flow, and environmental sustainability. By leveraging big data and machine learning, ITS optimizes fuel consumption, route planning, and traffic signal control. This study evaluates the performance of four machine learning models—K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest—for classifying traffic conditions based on 11 months of vehicle count data collected every 15 minutes. The traffic levels are categorized into four classes: Heavy Traffic, Medium Traffic, No Traffic, and Traffic Jam. Among the models, Random Forest achieved the highest overall accuracy (99%) and delivered strong precision and recall across all traffic levels, especially in distinguishing between complex traffic patterns. KNN also showed high accuracy but required more computational resources. Decision Tree performed comparably, particularly for Heavy Traffic, while Naïve Bayes struggled with lower precision and recall due to its limitations in handling complex data distributions. The results underscore the robustness of Random Forest for real-time traffic condition classification and support its integration into ITS for effective, data-driven traffic and congestion management in urban environments.

Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called “pheromone”. We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.

<p style="text-align: justify;">Artificial intelligence (AI) is becoming a key element in enhancing transportation systems, enabling automation, optimization, and real-time traffic flow prediction. Its application in Intelligent Transportation Systems (ITS) contributes to more efficient passenger transport in urban areas, where infrastructure fails to keep up with growing demand, leading to frequent traffic congestion and bottlenecks.This paper will describe how advanced AI algorithms and deep learning models enable traffic analysis, congestion prediction, and route optimization, improving the reliability and efficiency of public transportation. The integration of AI into ITS not only optimizes traffic flows but also enhances sustainability by reducing emissions and improving energy performance. Data-driven smart solutions make transportation safer, more environmentally friendly, and better suited to user needs.</p>

Highway congestion has become a normal social problem that needs to be managed more accurately and deeply. In this paper, a traffic flow congestion prediction model is established based on LSTM, and the traffic parameters obtained from four observation points of China's long and deep high-speed video are used for training, so as to provide real-time early warning before the continuous congestion of the road section between the third and fourth points. The results show that the model constructed in this paper can reach 89.25% accuracy after predicting 400 times steps in 50 times Windows. After further evaluation of the constructed traffic flow congestion prediction model, it is found that the mean square error (MSE), mean absolute error (MAE) and R2 is 0.0897, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2334,0.6028$</tex>, respectively, revealing that the model can well predict the future traffic congestion.

Traffic congestion prediction is the premise to solve the problem of traffic congestion. Aiming at the problem that traffic volume was rarely considered in traffic congestion prediction based on spatio-temporal characteristics, this paper added two features of bus flow and time occupancy on the basis of temporal correlation and spatial correlation analysis of speed based on floating bus data. A BP neural network speed prediction model optimized by whale optimization algorithm (WOA) considering the temporal and spatial characteristics of bus flow was proposed, and the traffic state was divided into three levels by fuzzy theory. The results show that the speed prediction method based on temporal and spatial characteristics and bus flow characteristics proposed in this paper has good performance. Compared with the traditional BP neural network prediction results, the root mean square error and mean absolute error of WOA-BP neural network prediction model are reduced by 11.7% and 11.2% respectively, and the determination coefficient reaches 93.7%. The prediction accuracy of traffic congestion based on fuzzy theory is 90.06%, and the prediction accuracy of the model is higher.

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic congestion can provide a valuable guideline to address such problems. Most existing approaches focus on the tasks of predicting traffic volume or traffic speed, which do not effectively address the challenges of traffic congestion prediction. First, traffic congestion exhibits daily and weekly temporal patterns, but these patterns are not strictly the same, which indicates complicated long-term periodicity. Second, traffic congestion sparsely distributes over different periods of time, which leads to complex short-term and mid-term temporal dependencies. Third, since traffic congestion will propagate to adjacent road segments over time, it exhibits complex spatio-temporal correlations. To address them, we propose a periodic shift and event-aware spatio-temporal graph convolutional network for traffic congestion prediction. Specifically, we propose to capture the differences and similarities of long-term periodic temporal patterns to handle the complicated long-term periodicity. To effectively capture short-term and mid-term temporal dependencies, we regard a continuous time sequence of the congested condition as a traffic congestion event, and then adopt the widely-used long short-term memory model to learn the sequential dependencies of traffic congestion events. Finally, we integrate the graph convolutional network into the modeling of temporal dependencies to capture the complex spatio-temporal correlations. Extensive experiments demonstrate the superiority of our model. In addition, we deploy our model in production at Amap, and it achieves great performance improvement in terms of the F1-score compared to the production baseline. This confirms that our model is a practical solution for real-world congestion prediction services.

Road safety, traffic congestion and efficiency of the transport sector are major global concerns. Improving this is the primary objective of intelligent transport systems (ITS). Having Internet of things (IoT) based solutions for ITS would enable motorists to obtain prior contextual guidance to reduce congestion and avoid potential hazards. IoT based solutions enabling collection of data from client nodes in a wireless sensor network in the transport environment implementing ITS goals is studied. The parameters to be monitored, type of sensors and communication related design parameters are identified to develop an effective IoT based solution. Road safety techniques studied include distance sensing, improper driving detection and accident prevention, weather related events and negligent driving detection and accident avoidance. Vehicle to vehicle communication and vehicle to infrastructure based channels are studied. Wireless communication technologies suitable for the channels are studied. Additional benefits and services that can be added to a system with the IoT approach are also studied. The effectiveness of such a system is studied with the use of validation framework. Multiple case studies of current and future IoT based ITS along with the challenges in the application is discussed.

The dismal performance of the traditional traffic congestion measures and motorist attitudes on Nigeria roads have not only made traffic flow unpredictable and worsening but also worrisome and defiling every measures put-in-place by the Government. To solve these problems, the study assessed traffic congestion and Intelligent Transport System (ITS) in Ibadan, Nigeria. In achieving this, the study examined among others, factors aiding traffic congestion; effectiveness of the existing traffic congestion measures; and the performance and disposition to the use of ITS in the study area. Systematic random sampling was used to administer 386 copies of a questionnaire on commercial motorists, while data collected were analyzed using both descriptive and inferential (Pearson Chi-Square and Fisher's Exact Tests) statistics. Findings revealed that faulty intersections (3.586) and excesses of traffic officers (3.558) were worsening factors aiding traffic congestion in Ibadan city. Meanwhile, electronic traffic signalization (3.558) and law enforcement and regulation (3.499) were rated the most effective existing traffic congestion measures. In other words, real-time traffic signal devices (3.609); automated travel advisory system (3.595) and road congestion pricing devices (3.581) were highly rated and favourably disposed to use ITS. However, the Chi-Square Tests shows that the use Intelligent Transport System is statistically influenced by users' level of education (Sig. value &lt; 0.05). The study concludes that there is a total necessity and willingness to use ITS in the study area. Hence, the study recommends exigent and holistic approach in the provision and enforcement of ITS towards mitigating congestion and other suggestions for improved traffic flow in Nigerian cities including Ibadan.

With a large number of traffic parameters data, it is an important issue that how to set up an efficient model of classification and prediction to identify the congestion state as soon as possible. The article provided a model of predicting traffic congestion based on the learn vector quantization neural network by making use of traffic parameters such as speed, volume and occupancy which were detected by vehicle detectors. The model can finally classify the traffic congestion situation and normal situation by training the LVQ neural network in the software Matlab. The model can predict the road traffic situation by inputting the traffic flow data, thus providing exact road information for the dispersion of traffic congestion. Finally, an example was given to train and test the network. And the training result demonstrated the algorithm was feasible to the prediction of traffic congestion and can be actually useful in reality.

Due to advancements in the automobile industry’s technology, the usage of private vehicles, particularly cars, by civilians has grown widespread in this period of globalization. Moreover, the country’s business and trade procedures are improving year after year. This scenario has resulted in significant traffic congestion, air pollution, and inevitable accidents, especially on the Shah Alam-Puchong Highway. As a result, the government proposed the Intelligent Traffic Management System (ITMS) utilising the Internet of Things (IoT) as an alternative to extending the road system, which may be expensive. This method assists authorities in tracking, monitoring, processing, and managing traffic more effectively, therefore reducing traffic congestion concerns. This study report presents a survey on the effectiveness of the ITMS installation on Federal Route 3214 (Shah Alam-Puchong Highway) in reducing traffic congestion. The information gathered is evaluated using Microsoft Excel and the Statistical Package for Social Sciences (SPSS) software. The factors that contribute to traffic congestion and the mean impact of the traffic congestion on road users, society, and the economy of the Shah Alam-Puchong Highway are determined in this study. Furthermore, this study also evaluates the probabilities of IoT hardware utilisation in ITMS at Shah Alam-Puchong Highway.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.