Related papers: Accelerating Statewide Connected Vehicles Big (Sen…

Temporal Interaction Graphs (TIGs) are widely employed to model intricate real-world systems such as financial systems and social networks. To capture the dynamism and interdependencies of nodes, existing TIG embedding models need to…

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through…

Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the substantial data requirements of deep learning models, a…

Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential.…

Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way…

Real-time estimation of vehicle locations and speeds is crucial for developing many beneficial transportation applications in traffic management and control, e.g., adaptive signal control. Recent advances in communication technologies…

In this paper, we propose a unifying co-simulation framework integrating transportation demand, grid assets, land use, demographics, and emissions to optimally accelerate electric vehicle (EV) development as well as measure the impact of EV…

Existing data collection methods for traffic operations and control usually rely on infrastructure-based loop detectors or probe vehicle trajectories. Connected and automated vehicles (CAVs) not only can report data about themselves but…

Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area.…

In this paper, we present a learning-based framework that accelerates time- and energy-optimal trajectory planning for connected and automated vehicles (CAVs) using graph neural networks (GNNs). We formulate the multi-agent coordination…

As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an…

The emergence of connected and automated vehicles (CAVs) provides an unprecedented opportunity to capitalize on these technologies well beyond their original designed intents. While abundant evidence has been accumulated showing substantial…

Real-time city-scale traffic analytics requires processing 100s-1000s of CCTV streams under strict latency, bandwidth, and compute limits. We present a scalable AI-driven Intelligent Transportation System (AIITS) designed to address…

Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This paper is the first to utilize roadside urban imagery for direct transit travel time prediction. We…

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even…

Over 90% of new vehicles in the United States now collect and transmit telematics data. Similar trends are seen in other developed countries. Transportation planners have previously utilized telematics data in various forms, but its current…

Traffic propagation simulation is crucial for urban planning, enabling congestion analysis, travel time estimation, and route optimization. Traditional micro-simulation frameworks are limited to main roads due to the complexity of urban…

The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainability. However, the integration and analysis of the diverse…

We developed a distributed data mining system to elaborate a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between…

Virtual Traffic Light (VTL) is a traffic control method that does not require traffic signal-related infrastructure for roadway intersections. Connected vehicles (CVs) are given right-of-way based on prevailing traffic conditions, such as…