Traffic Monitoring on City Roads Using UAVs

Unmanned Aerial Vehicles (UAVs) based systems are a suitable solution for monitoring, more particularly for traffic monitoring. The mobility, the low cost, and the broad view range of UAVs make them an attractive solution for traffic monitoring of city roads. UAVs are used to collect and send information about vehicles and unusual events to a traffic processing center, for traffic regulation. Existing UAVs based systems use only one UAV with a fixed trajectory. In this paper, we are using multiple cooperative UAVs to monitor the road traffic. This approach is based on adaptive UAVs trajectories, adjusted by moving points in UAVs fields of views. We introduced a learning phase to search for events locations with a frequent occurrence and to place UAVs above those locations. Our approach allows the detection of a lot of events and permits the reduction of UAVs energy consumption

Local Government Policy and Planning for Unmanned Aerial Systems

This research identifies key state and local government stakeholders in California for drone policy creation and implementation, and describes their perceptions and understanding of drone policy. The investigation assessed stakeholders’ positions, interests, and influence on issues, with the goal of providing potential policy input to achieve successful drone integration in urban environments and within the national airspace of the United States. The research examined regulatory priorities through the use of a two-tiered Stakeholder Analysis Process. The first tier consisted of a detailed survey sent out to over 450 local agencies and jurisdictions in California. The second tier consisted of an in-person focus group to discuss survey results as well as to gain deeper insights into local policymakers’ current concerns. Results from the two tiers of analysis, as well as recommendations, are provided here

AVARS -- Alleviating Unexpected Urban Road Traffic Congestion using UAVs

Reducing unexpected urban traffic congestion caused by en-route events (e.g., road closures, car crashes, etc.) often requires fast and accurate reactions to choose the best-fit traffic signals. Traditional traffic light control systems, such as SCATS and SCOOT, are not efficient as their traffic data provided by induction loops has a low update frequency (i.e., longer than 1 minute). Moreover, the traffic light signal plans used by these systems are selected from a limited set of candidate plans pre-programmed prior to unexpected events' occurrence. Recent research demonstrates that camera-based traffic light systems controlled by deep reinforcement learning (DRL) algorithms are more effective in reducing traffic congestion, in which the cameras can provide high-frequency high-resolution traffic data. However, these systems are costly to deploy in big cities due to the excessive potential upgrades required to road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles (UAVs) can play a crucial role in dealing with unexpected traffic congestion because UAVs with onboard cameras can be economically deployed when and where unexpected congestion occurs. Then, we propose a system called "AVARS" that explores the potential of using UAVs to reduce unexpected urban traffic congestion using DRL-based traffic light signal control. This approach is validated on a widely used open-source traffic simulator with practical UAV settings, including its traffic monitoring ranges and battery lifetime. Our simulation results show that AVARS can effectively recover the unexpected traffic congestion in Dublin, Ireland, back to its original un-congested level within the typical battery life duration of a UAV

TCitySmartF: A comprehensive systematic framework for transforming cities into smart cities

A shared agreed-upon definition of "smart city" (SC) is not available and there is no "best formula" to follow in transforming each and every city into SC. In a broader inclusive definition, it can be described as an opportunistic concept that enhances harmony between the lives and the environment around those lives perpetually in a city by harnessing the smart technology enabling a comfortable and convenient living ecosystem paving the way towards smarter countries and the smarter planet. SCs are being implemented to combine governors, organisations, institutions, citizens, environment, and emerging technologies in a highly synergistic synchronised ecosystem in order to increase the quality of life (QoL) and enable a more sustainable future for urban life with increasing natural resource constraints. In this study, we analyse how to develop citizen- and resource-centric smarter cities based on the recent SC development initiatives with the successful use cases, future SC development plans, and many other particular SC development solutions. The main features of SC are presented in a framework fuelled by recent technological advancement, particular city requirements and dynamics. This framework - TCitySmartF 1) aims to aspire a platform that seamlessly forges engineering and technology solutions with social dynamics in a new philosophical city automation concept - socio-technical transitions, 2) incorporates many smart evolving components, best practices, and contemporary solutions into a coherent synergistic SC topology, 3) unfolds current and future opportunities in order to adopt smarter, safer and more sustainable urban environments, and 4) demonstrates a variety of insights and orchestrational directions for local governors and private sector about how to transform cities into smarter cities from the technological, social, economic and environmental point of view, particularly by both putting residents and urban dynamics at the forefront of the development with participatory planning and interaction for the robust community- and citizen-tailored services. The framework developed in this paper is aimed to be incorporated into the real-world SC development projects in Lancashire, UK

IoT-based emergency vehicle services in intelligent transportation system

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Establishing effective and economical traffic surveillance in Tonga

The Pacific Islands are seriously challenged by the growth in wealth and the expansion of international material possessions. On the roads traffic has grown dramatically and the types of vehicles now using Island roads has greatly changed. With the importation of cheap second hand vehicles designed for freeway speeds serious safety issues have grown proportionally with the increasing numbers. In this research we consider the prohibitive costs of traditional traffic controls to economy and propose a light weight highly mobile aerial surveillance system that integrates with ground policing capability. Our research question was: How can road safety and security be enhanced with economical technologies? In addition to collecting and processing live data we have also designed a forensically ready system, and an information system to process the large amounts of data generated by the addition of these technologies into the traffic surveillance processes

A Mission Coordinator Approach for a Fleet of UAVs in Urban Scenarios

Abstract The use of Unmanned Aerial Vehicles (UAVs) is now common, but although they have been for various applications, there are still a lot of challenges that need to be overcome. One key issue is related to standardizing the use of these vehicles in urban environments and guaranteeing a minimum risk level for the population. To rise to these challenges, autonomous strategies that optimize and coordinate vehicles in cooperative missions and avoid human operators should be developed. The novelty of this paper is the development of an autonomous urban mission coordinator, which is responsible for the high-level logistics of a fleet of heterogeneous vehicles. A multi-variable weighted algorithm based on a tree optimization method is also proposed

A Review and Analysis of Traffic Data Sources

Transportation is essential for economic and social development, and vehicle flow data can be used for safety monitoring, pollution analysis, and traffic flow management. Unfortunately, traffic management and control centres do not always comply with codified standards, making it difficult to obtain up-To-date data. This paper analyses open traffic datasets and Italian public traffic data sources available online, providing a knowledge base for transportation managers and researchers. Open traffic datasets are dimensionality-reduced and clustered. An event with 209,135 visitors is used to benchmark the public data sources, the time series of traffic flows are decomposed and a regression tree is used to identify different periods. The results suggest that the available Italian sensor grid is not fine enough to identify all incoming and outgoing traffic, more infrastructure investments are required or the available measurements should be coupled with other evaluation approaches capable of extending the punctual data through mathematical means