Bin Ran

• PhD
• Professor at University of Wisconsin–Madison

Due to the wide range of driving behaviors and varying traffic conditions between mainline and on-ramp traffic, freeway on-ramp areas become complex, high-risk zones for traffic conflicts. Emerging Connected and Automated Vehicles (CAVs) technology is anticipated to significantly improve ramp merging processes, potentially reducing traffic conflict...

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 and voluminous V2X data, including Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) data, prese...

div class="section abstract"> Advanced Driver Assistance Systems (ADAS) have achieved significant progress worldwide, with the primary goals of enhancing driving safety, improving operational efficiency, and supporting vehicle automation. These systems are increasingly dependent on intelligent connected technologies, which enhance drivers' awarenes...

With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The me...

In connected and automated environments, implementing feedback control on selected key connected and automated vehicles within a platoon can indirectly influence the operation of human-driven vehicles, ultimately optimizing overall traffic efficiency and safety. To enhance global traffic flow through centralized control of essential vehicles, an im...

Provided herein is technology relating to a function allocation system (FAS) that deploys artificial intelligence models for a connected automated highway (CAH) system and a connected automated vehicle (CAV) system to distribute driving intelligence between the CAV system and the CAH system. The FAS comprises a communication module, a data module,...

This paper provides a comprehensive analysis of the effects of Connected and Automated Vehicles (CAVs) market penetration rate, spatial distribution, and V2X communication on the stability of heterogeneous traffic flow. We explore stability variations across different scenarios by developing a car-following model and analyzing stability conditions....

The invention provides a roadside computing system (RCS), or an edge computing system, for an autonomous vehicle. The RCS comprises a hierarchy of roadside unit (RSU) and an onboard unit (OBU) in an individual vehicle. The RSU comprises a data processing module and a communication module, and is capable of generating guidance information and target...

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic i...

Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates. This paper presents the Gramian Angular Field Vision Transformer (GAF-ViT) model, specifically designed for analyzing AV driving behavior. The GAF-V...

This invention presents a function allocation system for an autonomous vehicle (AV). During the operations of the AV, some or all of its automated driving capabilities or functions could be downgraded due to long-tail events or malfunctioning. The roadside intelligent infrastructure, or the cloud platform, could supplement some or all of AV's autom...

This invention presents an automated driving system with distributed computing (ADS-DC). During the operation of a connected automated vehicle (CAV), some or all of its automated driving capabilities for sensing, prediction, planning, decision-making, or control may be downgraded due to long-tail events or malfunctions. The intelligent roadside too...

The development of vehicle re-identification technology has significantly enhanced the operational efficiency of intelligent transport systems and smart cities, attributed to the advancement of artificial intelligence technologies such as deep learning and transformer models. By accurately tracking and identify the same vehicle under different came...

Traffic flow analysis largely depends on accurate predictions of microscopic speed. Due to the complexity and stochastic of real-world driving environments, traditional model-driven methods face significant challenges. In recent years, data-driven methods that combine advanced intelligent algorithms have emerged to address the issue of vehicle spee...

The invention provides a vehicle AI computing system (VACS) that supports autonomous driving through an Onboard Unit (OBU) for vehicle-based computing and distributed computing based on vehicle road-cloud. The vehicle-based computing can effectively complete various computational tasks by using onboard computing resources. The distributed computing...

The accurate prediction of traffic conditions is essential for effective and efficient traffic management and control. The dynamic and complex nature of traffic data, characterized by intricate temporal and spatial features, presents significant challenges to accurate traffic forecasting. While previous studies have developed various models with ad...

Transportation Cyber-Physical Systems (T-CPS) are critical in improving traffic safety, reliability, and sustainability by integrating computing, communication, and control in transportation systems. The connected vehicle corridor is at the forefront of this transformation, where Cellular Vehicle-to-Everything (C-V2X) technology facilitates real-ti...

Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments becomes increasingly critical. Among various technologies utilized by CAVs, Vehicle-to-Everything (V2X) commu...

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-te...

Pedestrian trajectory prediction is essential for various applications in active traffic management, urban planning, traffic control, crowd management, and autonomous driving, aiming to enhance traffic safety and efficiency. Accurately predicting pedestrian trajectories requires a deep understanding of individual behaviors, social interactions, and...

The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents considerable challenges in accurately predicting the motion states of vehicles and handling the uncertainty inherent in the predictions. Addressing these challenges requires co...

Accurately predicting the probability of crashes is crucial for preventing traffic crashes and mitigating their impacts. However, the imbalance in crash data, irregular road network structures, and heterogeneity in multi-source data pose significant challenges. To address these issues, this study introduces a spatio-temporal graph convolutional net...

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services t...

Aiming at improving the operation of the bottleneck area of the highway in the environment of a connected and automated vehicle, this paper proposes an integrated lane-level control (ILC) method by combining the variable speed limit control method and lane selection method into a comprehensive framework. A lane-level variable speed limit (LVSL) con...

With the rapid development of Internet of Things and communication technologies, the connected vehicle technology with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is seen as the most promising solution for reducing traffic accidents and alleviating traffic congestion. Roadside Units (RSUs) play a crucial role in enabl...

Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscilla...

In modern transportation systems, network‐wide traffic flow estimation is crucial for informed decision making, strategic infrastructure planning, and effective traffic management. While the limited availability of observed road‐segment traffic flow data presents a significant challenge, the emerging collection of Global Navigation Satellite System...

Advancements in autonomous driving have increasingly focused on end-to-end (E2E) systems that manage the full spectrum of driving tasks, from environmental perception to vehicle navigation and control. This paper introduces V2X-VLM, an innovative E2E vehicle-infrastructure cooperative autonomous driving (VICAD) framework with large vision-language...

A mixed traffic flow with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) will persist for a long time. It is essential to utilize the limited road resources of freeways to improve traffic efficiency in the traffic flow environment. To address this issue, this paper proposes an optimization model for freeway lane management str...

Provided herein is technology related to a distributed driving system (DDS) by using flexible, on-demand, and customized resources and functions from an intelligent roadside toolbox (IRT). These resources comprise computational resources, cloud resources, system security resources, backup and redundancy resources. The functions comprise sensing, tr...

The technology described herein provides Automated Driving System (ADS) methods and systems for coordinating and/or fusing intelligence and functions between Connected Automated Vehicles (CAV) and ADS infrastructure to provide target levels of automated driving. The technology provides systems and methods for function allocation comprising sensing...

This study introduces a novel approach that integrates dynamic Bayesian network with attention based spatio-temporal graph convolutional network to forecast railway train delays, capturing the intricate operation interactions between train events and the dynamic evolution of train delays. Initially, train delay patterns are identified using the $K...

The invention provides systems and methods for an autonomous vehicle and cloud control system comprising an autonomous vehicle (AV) control system and a cloud-based platform, which are two components of an Intelligent Road Infrastructure System (IRIS). This integrated vehicle-cloud system provides sensing, prediction, decision-making, and control i...

The invention provides systems and methods for an autonomous vehicle and cloud control system comprising an autonomous vehicle (AV) control system and a cloud-based platform, which are two components of an Intelligent Road Infrastructure System (IRIS). This integrated vehicle-cloud system provides sensing, prediction, decision-making, and control i...

This paper proposes a framework for deep Long Short-Term Memory (D-LSTM) network embedded model predictive control (MPC) for car-following control of connected automated vehicles (CAVs) in traffic mixed with human-driven vehicles (HDVs) and CAVs. The framework consists of: 1) lead HDV trajectory prediction through D-LSTM; and 2) CAV car-following c...

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditional methods have laid foundational work, modern deep learning techniques, particularly transformer-based models and generative approaches, have significantly improved pr...

High-quality traffic flow data is foundational to the study of traffic issues and practical engineering applications. The development of traffic flow detection methods has greatly facilitated the collection of traffic data; however, anomalies caused by factors such as equipment, networks, and environmental conditions present a common challenge. Tra...

As urbanization advances, cities are expanding, leading to a more decentralized urban structure and longer average commuting durations. The construction of an urban expressway system emerges as a critical strategy to tackle this challenge. However, the traditional link-level network design method faces modeling and solution challenges when dealing...

-Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the interaction between expressways and arterial networks and achieve orderly coordination between them. This...

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible...

In this study, based on the comprehensive analysis of asymptotic stability and damping characteristics for the Intelligent Driver Model (IDM) car-following strategy, we propose a self-adaptive IDM (SA-IDM) car-following strategy, which is specifically designed for adaptive cruise control (ACC) vehicles. Using a coefficient of self-adaption, SA-IDM...

The efficiency of Intelligent Transportation Systems (ITS) in smart cities hinges on the accurate identification of traffic anomalies, with traffic time series data serving as the primary data source. However, the ever-increasing amount of data poses challenges to traffic practitioners in effectively detecting or labeling anomalies, due to the proh...

Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes ‘TrajGAN’, an unsupervised learning approach based on the Gene...

The widespread adoption of emerging connected and automated vehicles (CAVs) highlights the need for identifying the roadway capacity of mixed traffic flow with CAVs and human-driven vehicles (HDVs) for future traffic management. Previous studies focus on analyzing the impacts of CAV technologies on the mixed traffic capacity. However, research on h...

Truck parking on freight corridors faces various challenges, such as insufficient parking spaces and compliance with Hour-of-Service (HOS) regulations. These constraints often result in unauthorized parking practices, causing safety concerns. To enhance the safety of freight operations, providing accurate parking usage prediction proves to be a cos...

Provided herein is a technology for an Autonomous Vehicle Intelligent System (AVIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. The AVIS and related methods provide vehicles with vehicle-specific information for a vehicle to perform driving tasks such as car following, lane changing, and...

Provided herein is a technology for an autonomous vehicle cloud system (AVCS). The AVCS provides sensing, prediction, decision-making, and/or control instructions for specific vehicles at a microscopic level using data from one or more of other vehicles, a roadside unit, cloud-based platform, or traffic control center/traffic control unit. Specific...

Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life. With the development of next-generation information technologies, automated driving technologies have brought new opportunities to improve the performance of traffic systems and the intelligence level o...

Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient operation of autonomous vehicles in complex traffic environments. The application of IoT technology in the collaborative automated driving system (CADS) has established a robust data foundation for vehicle trajectory prediction. Accurate prediction requir...

Predicting vehicle trajectories is crucial to ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable m...

In order to construct the traffic mechanism and control method of the intersection under the mixed traffic of connected and autonomous vehicles (CAV) and human drive vehicles (HDV), the paper proposed a cooperative traffic model for intersections under the conditions of CAV dedicated lanes. First, an intersection layout under the condition of CAV d...

Customizing services for bus travel can bolster its attractiveness, optimize usage, alleviate traffic congestion, and diminish carbon emissions. This potential is realized by harnessing recent advancements in positioning communication facilities, the Internet of Things, and artificial intelligence for feature mining in public transportation. Howeve...

Provided herein is technology related to a distributed driving system (DDS) that provides transportation management and operations and vehicle control for connected and automated vehicles (CAV) and intelligent road infrastructure systems (IRIS) and particularly, but not exclusively, to methods and systems for sending individual vehicles with custom...

Travel well-being is the subjective feeling of satisfaction that people have while traveling. Previous research focused on its determinants and relationships with subjective well-being ignored. But no quantitative study discusses the effect of characteristics like weekly income and travel time on travel well-being. To demonstrate the quantitative i...

Real‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to...

This study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safety and must be tailored to the specific characteristics of the CAV environment. To this end, this paper introdu...

Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable...

Oversaturation has become a serious issue for urban intersections worldwide due to the rapid increase in population and traffic demands. The emergence of connected and automated vehicle (CAV) technologies demonstrates the potential to improve oversaturated arterial traffic. Integrating vehicular control and intersection controller optimization into...

Trajectory optimization for connected automated vehicles (CAVs) is an effective method to improve the overall performance of urban traffic. At the same time, the emergence of deep reinforcement learning (DRL) enables agents to learn the optimal strategy in a complex environment by constantly interacting with the environment. In a connected environm...

To alleviate the lane-changing conflicts between weaving vehicles and enhance the traffic efficiency in the weaving section of urban expressway under the connected autonomous vehicle (CAV) environment, a cooperative lane-changing strategy for CAVs is proposed. The strategy consists of an upper layer of decision making, which determines the lane-cha...

Provided herein is technology related to a distributed driving system (DDS) that provides transportation management and operations and vehicle control for connected and automated vehicles (CAV) and intelligent road infrastructure systems (IRIS) and particularly, but not exclusively, to methods and systems for sending individual vehicles with custom...

The invention provides systems and methods for an Intelligent Road Infrastructure System (IRIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. IRIS systems and methods provide vehicles with individually customized information and real-time control instructions for vehicle to fulfill the dri...

Tribal lands in the United States have consistently exhibited higher crash rates and injury severities compared to other regions. To address this issue, effective data-driven safety analysis methods are essential for resource allocation and tribal safety program development. This study outlines the minimum data requirements and presents a generic t...

In the real word, the periodic disturbance in traffic flow caused by disrupting maneuvers has a huge impact on the car-following stability. As analyzed in this paper, the asymptotic stability of a car-following model, quantified by the coefficient of dynamic responses, is relatively low at low disturbing frequencies, and gradually increases as the...

Truck parking along the Mid-America Association of State Transportation (MAASTO) region’s major freight corridors is complicated by an overall deficit in needed parking spaces, delivery staging at major logistic centers or urban areas, and hour-of-service (HOS) compliance. These constraints lead to unauthorized or potentially unauthorized parking....

Due to uncertain factors such as weather, large-scale flight delays will occur on the ground at the airport. A reasonable and effective flight sequence can make the airport resume regular operation as soon as possible. However, limited shuttle bus resources will limit the execution of the flight sequence plan. In order to provide decision support f...

To solve the problems of when to set up connected automated vehicles (CAVs) dedicated lanes and how many CAVs dedicated lanes to set up under different penetration rates of CAVs, this work focuses on modeling the fundamental diagram of mixed traffic flow with dedicated lanes for CAVs. Firstly, the car-following modes and their proportion of mixed t...

Connected automated vehicles (CAVs) are broadly recognized as next-generation transformative transportation technologies having great potential to improve traffic safety, efficiency, and stability. Efficiently controlling CAVs on two-dimensional curvilinear roadways to follow preceding vehicles is denoted as the two-dimensional car-following proces...

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging risk quantification approach to accommodate the connected and self-driving vehicle environment. Vehicles have different risk impact effects on other road participants in each direction under the influence of road rules. This variability exhibited by vehi...