Related papers: Framework for Highway Traffic Profiling using Conn…
Controlling and coordinating urban traffic flow through robot vehicles is emerging as a novel transportation paradigm for the future. While this approach garners growing attention from researchers and practitioners, effectively managing and…
The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the…
Zero Trust is the new cybersecurity model that challenges the traditional one by promoting continuous verification of users, devices, and applications, whatever their position or origin. This model is critical for reducing the attack…
In this paper, we aim at developing new methods to join machine learning techniques and macroscopic differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model-driven approaches have…
With the growing demand of real-time traffic monitoring nowadays, software-based image processing can hardly meet the real-time data processing requirement due to the serial data processing nature. In this paper, the implementation of a…
Connected automated vehicles (CAVs) could potentially be coordinated to safely attain the maximum traffic flow on roadways under dynamic traffic patterns, such as those engendered by the merger of two strings of vehicles due a lane drop.…
With the unprecedented shift towards automated urban environments in recent years, a new paradigm is required to study pedestrian behaviour. Studying pedestrian behaviour in futuristic scenarios requires modern data sources that consider…
Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation…
Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to…
Cooperative vehicle coordination at unsignalized intersections has garnered significant interest from both academia and industry in recent years, highlighting its notable advantages in improving traffic throughput and fuel efficiency.…
The expected low market penetration of connected vehicles (CVs) in the near future could be a constraint in estimating traffic flow parameters, such as average travel speed of a roadway segment and average space headway between vehicles…
Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that…
We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from four multi-lane roads at an intersection where they conflict in terms of safely crossing (including turns) with no collision. The…
Compared with today's 4G wireless communication network, the next generation of wireless system should be able to provide a wider range of services with different QoS requirements. One emerging new service is to exploit cooperative driving…
Nowadays, traffic monitoring systems have access to real time data, e.g. through GPS devices. We propose a new traffic model able to take into account these data and, hence, able to describe the effects of unpredictable accidents. The well…
Connected and automated vehicles (CAVs) have a great potential to improve traffic efficiency in mixed traffic systems, which has been demonstrated by multiple numerical simulations and field experiments. However, some fundamental properties…
Rural roadways often expose Commercial Motor Vehicle (CMV) drivers to hazardous conditions, such as heavy fog, rain, snow, black ice, and flash floods, many of which remain unreported in real time. This lack of timely information, coupled…
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could…
Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of…
Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS)…