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This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow smoothing technology may improve the user experience and operations of managed lanes or bridges, however it may require external incentives such as reduced tolls to entice the traveling public to use it. This must be matched with other goals such as verifying vehicle occupancy. It might be possible for some hybrid solution that addresses both challenges to provide a way forward. A concept of operations needs to be developed specifically for a target road geometry and a California partner. This concept should benefit from lessons learned from previous pilot projects and will need to be defined so as to achieve both (1) a penetration rate sufficient to achieve measurable effects; and (2) sufficient quality and quantity of data to confirm benefits.

• Randomised controlled trial of acceptance of C-ITS installed in participants' cars. • Both within and between groups methods and counterbalanced C-ITS activation periods. • Participants responded to four questionnaires over nine months. • High acceptance ratings but reduced slightly over time and after C-ITS activated. • Participants perceived some warnings were presented inaccurately. Connected Intelligent Transport Systems (C-ITS) may provide safety and mobility benefits for drivers and other road users by providing timely, safety focused messaging to drivers. However, the knowledge-base regarding drivers' experiences with C-ITS technology is limited given that interactions with these advanced systems are still relatively uncommon and often constrained by time and place. The current study explored participants' acceptance of, and experiences with a Human Machine Interface (HMI) that displayed C-ITS warnings, during nine months of participation. The specific warnings included speed and hazardous driving at signalised intersections, road-works zones, and on highways. Importantly, the HMI was installed in each participant's personal vehicle thereby integrating the C-ITS experience into each participant's daily routine for an extended period. Subjective data were obtained via four questionnaires focused on drivers' acceptance and general experiences with the HMI, as part of a large-scale (n = 325) longitudinal Field Operational Test of C-ITS conducted in Ipswich, Queensland, Australia. Analyses exposed several significant factors that predicted acceptance including HMI activation, age, and technology readiness. Subsequent contrasts revealed that significant, but small decreases in mean acceptance following the activation of warnings (use cases) on the HMI likely due to perceived limitations with respect to timing and accuracy. Still, participants' ratings of the warnings being displayed on the HMI were positive and remained as such throughout the FOT. [ABSTRACT FROM AUTHOR]

The concept for management of directionally unrestricted traffic flows in urban areas called Combined Alternate-Direction Lane Assignment and Reservation-Based Intersection Control (CADLARIC) has been recently developed. CADLARIC concept aims to optimize traffic flow in urban areas by efficiently managing directionally unrestricted traffic flows, thereby reducing congestion and improving overall traffic efficiency. The main idea behind CADLARIC is to organize traffic flow in the connected and automated vehicles environment and distribute vehicular conflicts between links and intersections to prevent intersections from turning into traffic bottlenecks. In the proposed concept, vehicles can use lanes traditionally reserved for the opposite direction of travel. Through its reservation-based algorithm, CADLARIC effectively manages conflicts for through vehicles, ensuring seamless passage and minimizing delays at intersections. However, CADLARIC in its current state is only based on vehicular flow and does not include pedestrians. Adding pedestrians impacts traffic flow efficiency and introduces additional conflicts which are handled on a Pedestrian Priority basis. Pedestrians reserved their path when attempting to cross at intersections, thereby inflicting the behavior of vehicles within the simulation. In this paper we evaluated safety of CADLARIC in comparison with Fully Reservation-Based Intersection Control with conventional lane assignment (FRIC), and Fixed Time Control. The findings indicate that the CADLARIC control strategy demonstrates significant potential for enhancing intersection safety. It surpasses the FRIC control scheme in terms of safety performance, particularly under conditions of increased vehicular and pedestrian demand, as evidenced by improvements in all surrogate safety measures analyzed in this study. [ABSTRACT FROM AUTHOR]

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. [ABSTRACT FROM AUTHOR]

In transportation systems, road users have diverse preferences when planning their trips and responding to traffic conditions in a large city. Connected vehicles can capture the preferences of individual users for trip planning, leading to improved road performance. However, managing a large number of connected vehicles with differing user preferences in a large city is a daunting task. This paper develops an integrated traffic management system with the consideration of user preferences to optimize the performance of each user. In the system, connected vehicles are introduced to estimate traffic conditions and costs associated with different user preferences. The system will utilize the information to search for multi-layer vehicle control instructions that account for user preferences in mobility, energy consumption, and driving comfort. Microscopic simulations were carried out to assess the system's efficacy in mitigating road congestion, reducing fuel consumption, and restricting turns. The results reveal that implementing the system can reduce vehicle delay by up to 32%, fuel consumption by 4%, and left and right turns by 24%. Additionally, the paper evaluates the impact of market shares of connected vehicles with different preferences to analyze their performance at different stages of connected vehicle development. The work can contribute to the development of advanced transportation services in future cities and enhance urban mobility and energy sustainability. [ABSTRACT FROM AUTHOR]

Can we elaborate a traffic-sensitive eco-driving or GLOSA (Green Light Optimal Speed Advice) strategy with a frugal amount of data when approaching an intersection? Here is the purpose of this work, which aims to adapt a traffic-theory-based estimation of the expected queue-length within mixed traffic (Connected and non-Connected Vehicles) in the vicinity of a signalized intersection. While the expected queue-length methodology was developed recently and fits natively with Eulerian traffic indicators resulting from loop sensors or cameras, this paper adapts such a methodology to Lagrangian indicators as the traces produced by any Connected Vehicle, including Floating Car or Probe Data. The main interest of the methodology lies in the frugal amount of data and expenses required to perform the traffic-sensitive speed-advisory at any connected road intersection. The full methodology is developed to extend the SPAT messages broadcast to end-users and take advantage of the Cooperative Awareness Messages (CAM) acting as GPS traces for Connected Vehicles. Contrary to Eulerian-based indicators, no supplementary and costly investment is required to collect the input data and compute the queue-length estimation. However, applying strategies based on Lagrangian indicators will affect the direct traffic observation through these indicators. Therefore, it requires to develop an assessment and predictive framework to estimate the traffic conditions. The performance of the introduced methodology is compared to alternative methods, among other Eulerian-based methods. It results from the analysis that the introduced approach performs almost as well as the ones based on exhaustive, but costly data collections. [ABSTRACT FROM AUTHOR]

Traffic perception is the foundation of intelligent roads, and how to accurately perceive traffic has become a central issue for researchers. With the application of Vehicle-to-Everything communication technology, vehicle IDs, locations, velocities, and accelerations can be obtained by the Roadside Unit (RSU), i.e., trajectory-observed vehicles for the road. Inferring the number of vehicles between trajectory-observed vehicles can make traffic perception more accurate, with which the traffic can be sensed on the whole road. Thus, in the case of mixed traffic flow, a Real-Time Prediction Model was proposed, which is a novel model containing four modules: prior experience of the space headway, linear distribution of velocity and acceleration, identification of traffic shockwave, and filter. The inferred result was calculated in real time. During the test, we used US-101 lane-1 data of the Next Generation Simulation dataset and trajectory-observed vehicles with stochastic distribution for 20% penetration. The length of the study area on the US-101 highway was approximately 2100 feet, which was similar to the communication area of a single RSU. During the evaluation of the model accuracy with the real-world datasets, the error of the inferred vehicle numbers in the study area could be limited to ±5 vehicles almost. Results show that it is feasible to infer the number of vehicles between trajectory-observed vehicles. The model compensates for the shortcomings of traditional models (based on inductive loop, camera, or radar), thus providing a novel method for the traffic perception of intelligent roads. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. This paper discusses applications of physics-integrated ML to advance pavement engineering. To demonstrate an example, a PINN model was pretrained to approximate the simulation of vehicles' suspension responses to longitudinal road profiles. The parameters of the outer layers were finetuned to adapt model output to the standard International Roughness Index (IRI), while keeping the pretrained inner layers to preserve the embedded physical knowledge of the suspension behaviour. The PINN model showed low bias and standard error in predicting IRI values on training, test, and an independent dataset from an autonomous vehicle study by the Ford Motor Company. This approach to reconcile and supplement infrequent survey data with spatiotemporally continuous data (from connected vehicles) can enhance data-driven practices for pavement design, maintenance and asset management. [ABSTRACT FROM AUTHOR]

Abstract The fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions. [ABSTRACT FROM AUTHOR]

Natural disasters like hurricanes, wildfires, and floods pose immediate hazards. Such events often necessitate prompt emergency evacuations to save lives and reduce fatalities, injuries, and property damage. This study focuses on optimizing wildfire evacuations by analyzing the influence of different transportation infrastructures and the penetration of autonomous vehicles (AVs) on a historical wildfire event. The methodology involves modeling various evacuation scenarios and incorporating different intersection traffic controls such as roundabouts and stop signs and an evacuation strategy like lane reversal with various AV penetration rates. The analysis results demonstrate that specific interventions on evacuation routes can significantly reduce travel times during evacuations. Additionally, a comparative analysis across different scenarios shows a promising improvement in travel time with a higher level of AV penetration. These findings advocate for the integration of autonomous technologies as a crucial component of future emergency response strategies, demonstrating the potential for broader applications in disaster management. Future studies can expand on these findings by examining the broader implications of integrating AVs in emergency evacuations. [ABSTRACT FROM AUTHOR]

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles ( σ c ) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types. [ABSTRACT FROM AUTHOR]

The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system (MAS)-based hierarchical architectures and privacy-preserving strategies for mixed-autonomy platoon control, underscore the increasing complexity of privacy management within these environments. Rental cars with infotainment systems pose substantial challenges, as renters often fail to delete their data, leaving it accessible to subsequent renters. This study investigates the risks associated with PII in connected vehicles and emphasizes the necessity of automated solutions to ensure data privacy. We introduce the Vehicle Inactive Profile Remover (VIPR), an innovative automated solution designed to identify and delete PII left on infotainment systems. The efficacy of VIPR is evaluated through surveys, hands-on experiments with rental vehicles, and a controlled laboratory environment. VIPR achieved a 99.5% success rate in removing user profiles, with an average deletion time of 4.8 s or less, demonstrating its effectiveness in mitigating privacy risks. This solution highlights VIPR as a critical tool for enhancing privacy in connected vehicle environments, promoting a safer, more responsible use of connected vehicle technology in society. [ABSTRACT FROM AUTHOR]

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The fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure. [ABSTRACT FROM AUTHOR]

With the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment. [ABSTRACT FROM AUTHOR]

With the development of technology, the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal. The data of ICV (intelligent connected vehicles) is the key to organically maximizing their efficiency. However, in the context of increasingly strict global data security supervision and compliance, numerous problems, including complex types of connected vehicle data, poor data collaboration between the IT (information technology) domain and OT (operation technology) domain, different data format standards, lack of shared trust sources, difficulty in ensuring the quality of shared data, lack of data control rights, as well as difficulty in defining data ownership, make vehicle data sharing face a lot of problems, and data islands are widespread. This study proposes FADSF (Fuzzy Anonymous Data Share Frame), an automobile data sharing scheme based on blockchain. The data holder publishes the shared data information and forms the corresponding label storage on the blockchain. The data demander browses the data directory information to select and purchase data assets and verify them. The data demander selects and purchases data assets and verifies them by browsing the data directory information. Meanwhile, this paper designs a data structure Data Discrimination Bloom Filter (DDBF), making complaints about illegal data. When the number of data complaints reaches the threshold, the audit traceability contract is triggered to punish the illegal data publisher, aiming to improve the data quality and maintain a good data sharing ecology. In this paper, based on Ethereum, the above scheme is tested to demonstrate its feasibility, efficiency and security. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) address contemporary transportation challenges, as Connected Vehicles (CVs) can play a pivotal role in enhancing efficiency and safety. The role of central governing authorities in shaping traffic management policies for CVs influences decision-making processes and system performance. In this work, the role of central governing authorities in the traffic management of a mixed traffic network is examined, integrating System Optimum principles with game theory. More specifically, we introduce and develop a framework that models and analyses the strategic interactions between different stakeholders in a mixed traffic environment, considering central governing authorities with varying levels of control. The results indicate how the various levels of control of a central governing authority may have an impact on the network in terms of traffic measures. Through a strategic games analysis, the trade-offs associated with centralized control mechanisms are demonstrated and recommendations are offered for policymakers and practitioners to optimize traffic management strategies. [ABSTRACT FROM AUTHOR]

Connected Vehicles (CV) technology offers significant potential for managing traffic congestion and improving mobility along transportation corridors. This report presents a novel approach using integrated corridor management (ICM) technology to divert CVs to underutilized park-and-ride facilities where drivers can park their vehicle and access public transportation. Using vehicle-to-infrastructure (V2I) communication protocols, the system collects data on downstream traffic and sends messages regarding available park-and-ride options to upstream traffic. A deep reinforcement learning (DRL) program controls the messaging, with the objective of maximizing traffic throughput and minimizing CO2 emissions and travel time. The ICM strategy is simulated on a realistic model of Interstate 5 using Veins simulation software. The results show marginal improvement in throughput, freeway travel time, and CO2 emissions, but increased travel delay for drivers choosing to divert to a park-and-ride facility to take public transportation for a portion of their travel.

This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These results motivate the need for new methods that more fully utilize the information available on CAVs (e.g., distance to vehicles in front, back, left, and right) to decrease the sensitivity to penetration rate.View the NCST Project Webpage

In order to improve the lane change safety of networked autonomous vehicles in the intelligent networked environment, according to the characteristics of environment perception and real-time communication of networked autonomous vehicles, based on the potential field theory, the differences of risk changes faced by vehicles in different directions were analyzed, the dynamic vehicle spacing was corrected, the vehicle risk potential field model was constructed, and the risks faced by the networked autonomous vehicles in the driving process were quantified. Numerical simulation analysis of the model show that the motion state of the vehicle in this lane and the target lane directly affects the safety distance required when changing lanes. It can be seen that the vehicle needs to adjust its own motion state to change the distribution of the vehicle's risk potential field when changing lanes, so as to avoid conflicts with the risk potential field of other vehicles and affect driving safety. [ABSTRACT FROM AUTHOR]

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm's methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user's ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion. [ABSTRACT FROM AUTHOR]

Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized. [ABSTRACT FROM AUTHOR]

The emergence of connected vehicles has transformed the automotive sector by enhancing the vehicle's functionality, efficiency, and safety. The performance and security of these vehicles significantly rely on the deployment of the over-the-air software update. However, the execution of OTA comes with many challenges, especially with regard to security vulnerabilities and risks. The current paper delves into the complexities of the secure OTA software update for connected vehicles addressing the most critical issues; authentication; encryption and integrity verification, and risk management. Through advanced cryptographic methodologies, stringent authentication processes, and secure communication channels, automotive manufacturers and other service providers can guarantee the integrity and confidentiality of the updates, and consumers' data from malicious attack. Moreover, the paper explores the regulatory and other standards-related matters that control the use of OTA in the automotive sector. An understanding of the secure OTA update mechanisms aids the stakeholders in establishing a resilient connection in connected vehicles boosting consumer trust and the future of the automobiles industry. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs. [ABSTRACT FROM AUTHOR]

Affective human–vehicle interaction of intelligent cockpits is a key factor affecting the acceptance, trust, and experience for intelligent connected vehicles. Driver emotion detection is the premise of realizing affective human–machine interaction. To achieve accurate and robust driver emotion detection, we propose a novel brain-inspired framework for on-road driver emotion detection using facial expressions. Then, we conduct driver emotion data collection in an on-road context. We develop a data annotation tool, annotate the collected data, and obtain the RoadEmo dataset, a dataset of facial expressions and road scenarios under the driver’s emotional driving. Finally, we validate the detection accuracy of the proposed framework. The experiment results show that our proposed framework achieves excellent detection performance in the on-road driver emotion detection task and outperforms existing frameworks. [ABSTRACT FROM AUTHOR]

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain. [ABSTRACT FROM AUTHOR]

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study's central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions' transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis. [ABSTRACT FROM AUTHOR]