TariqAmn's Innovative Road Safety Paradigm through Smart Technology

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO2 emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life.

From 3 lakh vehicles in 1985, to about 53 lakh now, vehicle population in Bangalore has increased more than five fold during the last two decades. While the vehicle population is growing by at least one lakh every year, total road length has remained the same. Many of the arterial roads and junctions are operating over the capacity and to upgrade the city's traffic management system and resulted in the creation of the Bangalore Traffic Improvement Project, B-TRAC 2010 by Bangalore City Traffic Police. The BTRAC Project was conceptualized to address traffic congestion in Bangalore by employing the latest traffic management technologies. The components of BTRAC which are a reality on ground include the state-of-the-art Traffic Management Center which is the hub of transportation management system in Bangalore, Intelligent Transport Systems, Surveillance and Enforcement Cameras, Signage, Road Markings, Street Furniture, Junction Improvements and Capacity Building. Various traffic and road safety education and training activities have also been conducted with citizens and traffic personnel throughout the city for generating awareness about traffic rules and familiarising them with the latest technologies being adapted. As a result of such consolidated traffic control and management effort, Bangalore today has a full-fledged traffic maintenance system. This has resulted in creating transparency in enforcing traffic rules and levying adequate and timely penalties. BTRAC has resulted in better management of traffic, ensuring safety and security on roads and prevention of road accidents, with the monitoring and regulation of flow of vehicles in a systematic manner. BTRAC has reduced the occurrence of road accidents in Bangalore and is creating a safe road environment for commuters. The absolute number of road accidents witnessed a growing trend till the year 2007 and then followed a declining trend over the consecutive years with 5230 cases being reported in 2013 and 5004 cases in 2014. Even the fatal accident rate is showing a declining trend over the past five years.

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.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

Purpose: The aim of the article is to present the results of a study on the perception and evaluation of intelligent communication and transportation systems, as well as their management, by residents of cities in the Silesian Voivodeship who travel by car, bicycle (scooter), or on foot, as factors contributing to safe and fast movement within the city. Design/methodology/approach: The study was conducted using a proprietary survey based on the Consumer Satisfaction Index (CSI) methodology, and the analysis of the survey results was preceded by a brief literature review on the subject. Findings: The study made it possible to determine how road users evaluate the functioning of the intelligent communication and transportation system in their cities, which factors are the most and least satisfactory for them, as well as the importance they assign to these solutions. Research limitations/implications: The study had a limited scope as it was a one-time survey. It is advisable to continue and expand the sample size for future studies to allow for comparative analyses. Practical implications: The article may provide managers of intelligent communication and transportation systems in cities with knowledge about the expected outcomes of their operation for road users. Social implications: Utilizing the research results may enable better adaptation of ITS to the needs of road users and enhance road safety. Originality/value: The study included drivers, cyclists (scooter riders), and pedestrians. In contrast, most similar studies are limited to verifying the opinions of drivers. Keywords: smart city, smart mobility, management intelligent transports system, cyclists, drivers, pedestrians. Category of the paper: research paper.

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.

With the gradual maturity of geographical multimedia information system, applying geographical multimedia information system to urban intelligent traffic management information system and urban traffic sharing information platform can realize unified management and real-time update of traffic information. Through the urban traffic sharing information platform, it can increase the value of information. The multimedia information of the entire intelligent traffic management system can be stored, organized and processed by the shared multimedia information platform. It can more effectively ensure the correctness, comprehensibility and avoid redundancy of data relationships. Then it improves the utilization and transmission speed of multimedia information in the system. The urban traffic multimedia information management system has improved the use efficiency of urban road construction in China. And it also made the traffic multimedia information system more scientific and intelligent. It can also improve its management level and play an important role in urban traffic management. This paper first briefly analyzes the current traffic situation and the development of intelligent traffic multimedia information system in China. The basic concepts of GIS and urban intelligent transportation system based on GIS are introduced secondly. At last, the possibility about GIS as urban intelligent traffic management system sharing multimedia information platform is analyzed. The system framework and the basic function of GIS platform are put forward, and the application of GIS in urban traffic common information platform using the key technical support was discussed. It expounds the application of GIS in urban traffic common multimedia information platform.

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.

The issue of traffic congestion is a significant problem that affects numerous cities around the world, including the city of Caloocan in the Philippines. With the increasing number of vehicles on the roads and the limited infrastructure to support this growth, traffic management has become a critical concern for the city government. In order to address this issue effectively, it is essential to implement appropriate strategies and plans that will help alleviate traffic congestion and improve overall road safety. One key aspect of effective traffic management is the development of a comprehensive training plan for traffic enforcers and personnel in Caloocan City. This plan will serve as the basis for equipping traffic officers with the necessary skills and knowledge to effectively manage traffic flow, enforce road rules, and ensure the safety of motorists and pedestrians. By providing comprehensive training, the city government will be able to improve the efficiency and effectiveness of its traffic management strategies, ultimately leading to a smoother and safer traffic flow throughout the city. The proposed training plan for traffic management in Caloocan City will encompass a wide range of topics, including but not limited to traffic rules and regulations, traffic signal management, accident response and management, public relations and communication skills, and the use of technology in traffic management. By providing training in these key areas, traffic enforcers will be better equipped to handle the various challenges and situations they may encounter on the road, leading to improved traffic management outcomes in the city. In addition to providing training on specific traffic management skills, the proposed training plan will also focus on enhancing the professionalism and integrity of traffic enforcers in Caloocan City. By instilling a sense of duty, responsibility, and accountability among traffic officers, the city government will be able to improve the overall quality of traffic management services provided to the public. This, in turn, will help build trust and confidence among motorists and pedestrians, leading to a more harmonious and cooperative relationship between the community and traffic enforcers.

Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey

A Convolutional Approach to Traffic Sign Detection presents a deep learning-based method using Convolutional Neural Networks (CNNs) for accurate traffic sign detection and classification. The system follows a two-stage pipeline: region proposal and sign classification, leveraging data augmentation and transfer learning for improved performance. It demonstrates robustness to challenges like poor lighting and occlusion and supports real-time operation, making it suitable for ADAS and autonomous vehicles. Experimental results show high accuracy across datasets, highlighting its potential in enhancing road safety and intelligent transportation systems. Index Terms — Traffic Sign Detection, Convolutional Neural Networks (CNN), Deep Learning, Computer Vision, GTSRB, Image Classification, Object Detection, Real-Time Detection, Autonomous Driving, Data Augmentation, OpenCV, Keras, TensorFlow, Image Preprocessing, Road Safety

Enhancing road safety through advanced predictive analytics in V2X communication networks

In this Hi-Fi modernization world coping up with road safety and traffic congestion is the need of the hour. Many research works are proposed and undergoing in this particular thrust area. This paper proposes an Intelligent Transport System (ITS) that provides an effective Vehicle to Vehicle (V2V) communication mechanism using Zigbee. Especially in V2V communication Zigbee proves vital and it is the key protocol for wireless sensor network applications. The key features of Zigbee include long battery life, low-cost for installation and ease maintenance. These features in Zigbee enable uniform mesh networking, which effectively supports the wireless communication between many vehicles, routers and receivers. In this paper suggestions are proposed for periodic monitoring of vehicular movements, enhancing road safety and handling traffic congestion are dealt with. Since these two above mentioned issues are the core aspects in transportation industry and an important problem which the world faces today, this paper deals with effective inter communication of vehicles using Zigbee protocol.

Comprised of a broad range of diverse technologies, including information processing, communications, control, and electronics, Intelligent Transportation Systems (ITS) were first introduced as a means to solve the conflict between constantly increasing travel demand and insufficient transportation infrastructure. However, the impact of ITS on road safety was soon realized, and it became one of the major purposes of some ITS systems instead of only a side product. This paper summarizes the impacts of ITS technologies on road safety and the challenges of ITS safety evaluation. Based on the evaluation of collected ITS benefits information, this paper concludes that a variety of ITS technologies have the potential to make a substantial contribution to road safety and thereby have a substantial positive economic impact in the United States.

A survey on vehicular cloud computing