Traffic management methods for interurban - Urban interfaces

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

Although the number of automobiles on the road currently is more than it was a few years ago, the infrastructure of roads and traffic systems has not kept up with this expansion, which makes handling traffic challenging. This mismatch causes hence a lot of traffic congestion, crowding, and pollution. All throughout the globe, handling increasing traffic is a major challenge. Operations depend on traffic management in great part. It has to do with planning, running, and purchasing the necessary transportation services—cars, trucks, railroads, boats—that carry commodities and vehicles. Intelligent Traffic Systems (ITS) might be a fantastic approach to address these types of issues by means of modern technology. Given this, traffic control philosophy has to change to accommodate smart cities—which use computer vision techniques to run an adaptive traffic management system depending on CCTV image stream analytics. The adaptive traffic management system detects and counts vehicles accurately. In smart cities, where traffic lights must be automated based on vehicle density, this technique is particularly helpful. This study investigated intelligent traffic control, gathering and monitoring traffic data through using CCTV images. It embodies possible risks in the implementation state, benefits, challenges, as well as the future prospect of ITS.

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.

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.

The application, development and key matters of applying spatio-temporal GIS to traffic information management system are discussed in this paper by introducing the development of spatio-temporal database, current models of spatio-temporal data, traits of traffic information management system. This paper proposes a method of organizing spatio-temporal data taking road object changes into consideration, and describes its data structure in 3 aspects, including structure of spatio-temporal object, organizing method spatio-temporal data and storage means of spatio-temporal data. Trying to manage types of spatio-temporal data involved in traffic system, such as road information, river information, railway information, social and economical data, and etc, uniformly, efficiently and with low redundancy.

Systeme d'Information Routiere Intelligible aux Usagers (SIRIUS) is the largest urban field operational test of the advanced traveler information and automated traffic management system in Europe. With variable-message signs, SIRIUS has been in operation in the Paris region for 3 years. A preliminary investigation of the effectiveness of the SIRIUS system in traffic management is presented. The extent to which drivers respond to real-time traffic information and the consequential changes in link flow under SIRIUS is also presented. Time-series traffic data were analyzed to measure changes in mean flow rates at a selected link. It was found that variable-message signs influence drivers to choose less congested routes when drivers are provided with real-time traffic information, and that a driver's decision to divert is closely associated with the information pertaining to the level of congestion. In the Paris region, drivers received information on the length of the queue at the time of this study. As congestion becomes heavier, drivers are more likely to respond to variable-message signs. According to the data analysis, a queue length of 3 km seems to be a threshold at which a significant number of drivers choose to use an alternative route.

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

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.

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.

In major cities of Vietnam, serious road congestions are partially caused by rainfalls and road flooding. To help mitigate negative impacts of the adverse weather conditions on transport and traffic system, it is important to implement weather-responsive traffic management strategies for those cities. However, weather-responsive traffic management measures are often formulated based on practitioners’ experience and not tested prior to application. Taking Ho Chi Minh City as a case study, this paper aims to develop a methodological framework to help formulate and test such strategies. It consists of literature review of good practices, expert consultation process, road user interview survey, and strategic travel demand model-based scenario test. Active traffic management strategies are suggested for the city of study and a number of selected measures are tested by a VISUM model. It shows that vehicle speeds and travel times could be improved significantly if weather-responsive traffic management strategies are applied. This research could serve as a toolkit to assist the design of smart urban traffic management system.

Real-time traffic network management systems are envisioned to provide network operators with decision support capabilities to alleviate recurrent and non-recurrent congestion. These capabilities involve predicting the network congestion dynamics and facilitating the development of proactive traffic management schemes that integrate traffic control and demand management strategies. However, traffic networks are subject to numerous sources of stochasticity that make it difficult to accurately predict their operational conditions and generate effective traffic management schemes to cope with these conditions. This paper presents a decision support system for proactive-robust traffic network management, which accounts for uncertainty in the network operational conditions. The objective is to develop robust traffic management schemes such that the network overall performance remains close to optimality under all possible future operational conditions. The modeling framework of the system is presented, which adopts a rolling horizon framework that integrates a meta-heuristic search algorithm and a dynamic traffic assignment simulation-based methodology. The system performance is examined through application to the traffic network of the US-75 corridor in Dallas, TX, USA.

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.

Don’t shoot the messenger: Traffic-irrelevant messages on variable message signs (VMSs) might not interfere with traffic management

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.