Traffic safety system management

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.

Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management

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.

Traffic violations represent a critical issue for public health and safety globally. This paper introduces a novel machine learning framework designed to predict traffic violations using comprehensive data collected over one year from Qatar. The dataset includes five key features: date, time, zone number, street number, and type of license plate, with the type of traffic violation as the target variable. After data cleaning and preprocessing, including the conversion of input data to numerical values and normalization, the class imbalance is addressed. Various sampling techniques are tested, with random oversampling yielding the best results. This study evaluates several machine learning models, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), XGBoost, Neural Network, and Random Forest, for predicting traffic violations. To enhance performance, the proposed method introduces an improved version of the conventional Random Forest by incorporating novel modifications at two critical stages: tree construction and model evaluation. These enhancements aim to increase the accuracy and robustness of predictions, demonstrating the effectiveness of the approach over baseline models. Among these, the improved Random Forest model achieves the highest prediction accuracy at 99.5%. Feature importance analysis reveals that the street number is the most significant predictor, while the type of license plate is the least significant. The experimental results validate the effectiveness of the proposed method, highlighting its potential as a valuable tool for predicting and preventing traffic violations. This study aims to enhance traffic safety measures and demonstrates promise for broader applications in traffic management and safety systems.

Vehicle-Pedestrian near miss analysis at signalized mid-block crossings


 
 
 Traffic and road safety is a major concern of many governments around the world as it ensures the safety of its road users. Due to an increase in the number of road users, traffic-related accidents and fatalities have increased annually. Therefore, governments have stepped up their efforts to ensure the safety of road users and to reduce the number of traffic-related accidents and fatalities. However, these efforts have proven ineffective as the data gathered to measure the effectiveness of these efforts either lack credibility or are insufficient. This has led to inconclusive findings concerning the culture of road users and the extent of their awareness of traffic safety. Therefore, this paper performs a systematic data- and text-driven analytics and review of published literature on public awareness of traffic and road safety. The selected papers were first subjected to content analysis before being grouped according to the frequency of use of keywords discussing the issue at hand. As this method enabled us to successfully identify trends as well as the efficacy of traffic and road safety research, therefore, data and text analytics is an effective method of conducting a systematic literature review. The findings of this study will help traffic authorities gain credible insights into public perception, behavior, and attitude towards traffic safety awareness. Our assessment indicates that there are two important perspectives to be considered with regard to road safety; namely human and technology; the latter of which will be the main topic of study on public awareness of road and traffic safety. Moreover, an emerging trend in the literature suggests that more efforts should be allocated to promoting public awareness of road and traffic safety.
 
 

BackgroundDubai is one of the fastest growing cities in the world and aims to provide world class services in all sectors. The economy boom, the large increase in population and...

In recent years, with the extending highway construction scale in mountain areas, there is lack of traffic safety facilities in some mountain areas. The problems of unreasonable and irregular traffic guarantee engineering construction stand out. Especially in rural highway in mountain areas with complex topography, the traffic accident rates are increasing, causing lots of dead and economic losses. Studying deeply on rural highway traffic safety problems in mountain areas and improving rural highway traffic safety level are the problems that we need to solve urgently. This essay analyzes the law of rural highway traffic accidents in mountain areas and makes a conclusion that the existing problems in rural highway safety managements in mountain areas at present. Based on this, this essay analyzes systematically the influent factors of rural highway traffic safety and a conclusion can be drew that the frequent rural highway traffic accidents in mountain areas not only are caused by bad line type for roads and no security arrangements are set. At last, based on macro-management level, it proposes a series of positive highway safety management countermeasures including strengthening traffic safety education, establishing traffic safety management system, improving passenger transport policy and management system, formulating dangerous sections inspection mechanisms.

This chapter reviews the International Civil Aviation Organization air traffic management (ATM) work programme along with ATM developments taking place around the world. It examines the various elements of the ATM system and suggests some of die research, trials and demonstrations. Airspace management is traditionally recognized mainly as involving a dynamic or tactical sharing of airspace by civil and military users. Air Traffic Services itself is comprised of several subelements, one of these being air traffic control. The increasing number of long-range interregional flights may require integration of regional Air Traffic Flow Management systems and procedures. Free Right is the guiding vision, mission and operational concept of the Federal Aviation Administration, based on Communications, Navigation, Surveillance/Air Traffic Management systems (CNS/ATM). Africa is composed of many delicate national economies separated by vast wilderness areas. The main scope of Airspace management will be toward a strategic planning function of airspace infrastructure, and flexibility of airspace use.

The paper presented the PRT (Personal Rapid Transit) control and traffic management system. The genesis of the use of information systems for control and PRT traffic management was made. The concept of the PRT system was defined, a review of these systems and their structure was carried out. The mechanical and ergonomic systems of PRT vehicles have been described. The chassis structure and the ergonomics of the PRT vehicle cabs have been characterized. The process of estimating traffic conditions and simulations of exemplary PRT vehicles have been described. An analysis of the PRT system was carried out, the purpose of which is to determine the optimal number of PRT stations guaranteeing optimal service provision, in terms of the demand for PRT services. The stages of designing the PRT circulation system for districts on the outskirts of the city are illustrated. The OPTIMAL program tool (C-CAD) has been used.

The main focal points of improving the traffic safety management system are as follows. First, one must coordinate the interaction among all functional branches, their structural divisions and subsidiaries involved in the transportation process, based on common approaches to managing processes related to traffic safety. Second, one should implement the approaches and requirements that are congruent with international standards; to maintain a positive level of traffic safety culture through awareness of the importance and social responsibility of the holding employees when performing work that affects traffic safety, with simultaneous integration of mandatory and strict rules of conduct in all production processes. Third, one is to improve the regulatory framework. Train traffic safety is ensured at all stages of the life cycle of rolling stock, traffic support systems and infrastructure elements. Nevertheless, failures and malfunctions are detected during the operation. This article presents one of the methods for increasing the train traffic safety in the automation and telemechanics system.

In this study, a traffic safety management system (TSMS) for asphalt surfaced roads is developed to improve safety on different classes of roads in Bulgaria. TSMS is a strategic and systematic procedure to improve traffic safety when funding is limited and it is important to determine the most appropriate combinations of traffic safety improvement projects to provide maximum benefits to the population in terms of reducing traffic accidents. The factors included in the developed optimization methodology are the annual report on traffic accidents of the National Institute of Statistics, a study of the roads, a functional classification of the same, historically established, countermeasures to improve safety, costs and determinants of crash reduction in relation to measures to positively modify safety and average daily traffic (ADT). This study demonstrates how the proposed model can determine the best combination of activities to increase safety in order to maximize the benefits in terms of reducing overall crash frequency. The proposed methodology has been applied to highways in Bulgaria. It can be modified for application to roads in a particular city, region and district.

Road accident prevention model involving two-wheeled vehicles

PurposeResearchers have studied processes of improving road traffic-safety culture by explicitly evaluating the socio-psychological phenomenon of traffic-risk. The implicit traffic-system cues play an important role in explaining urban traffic-culture. This paper aims to ascertain an interpretive framework of the alternative processes of road traffic safety culture is antecedent to promote traffic-safety behaviour in Indian urban context. Subsequently, the authors discussed the reasons for those relationships exists.Design/methodology/approachFour experts of the urban traffic-safety domain participated in total interpretive structural modelling (TISM) study by completing an interpretive consensus-driven questionnaire. The drafted interpretive model was evaluated for road users proactive action orientation about the traffic-safety decision.FindingsThe evolved directed graph (digraph) of the culture of urban traffic-safety management was a serial three-mediator model. The model argued: In the presence of traffic-risk cues, people may become apprised to safety goals that initiate traffic-safety action. Consequently, expectancy-value evaluation motivates the continuation of traffic-safety intention that may lead to the implementation of adaptation plan (volitional control), thus habituating road users to traffic-safety management choice.Practical implicationsThe modellers of traffic psychology may empirically estimate and test for the quality criteria to ascertain the applicability of the proposed mechanism of urban traffic-safety culture. The decision-makers should note the importance of arousal of emotions regarding traffic-risk, reduce the impact of maladaptive motivations and recursively improve control over safety actions for promoting safety interventions.Originality/valueThe authors attempted to induce an interpretive model of urban traffic-safety culture that might augment extant discussion regarding how and why people behave in an urban traffic system.

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.