Future Systems Research Articles

A traffic incident detection method of highway based on spatial-temporal characteristics

In this study, we propose a method for detecting traffic incidents by leveraging tensor decomposition in conjunction with spatial-temporal constraints. The method comprises three main stages. Firstly, we develop an initialization and noise reduction procedure for the traffic data pre-processing model, which includes Tucker decomposition and truncating higher-order Singular Value Decomposition (SVD). Subsequently, we construct spatial and temporal coefficient matrices based on the Pearson test and introduce a Laplace penalty term to quantify the disparity between predicted and actual data. Next, we validate the method using traffic loop detector data and incident records from I90 in Seattle, USA, in 2015, comparing its performance with seven other traffic prediction methods. The results demonstrate the superior prediction accuracy, correct detection rate, and low false alarm rate of our traffic incident detection method. Specifically, the mean square error of speed prediction is 5.22 km / h , the average relative error is 6.88 % , and the detection rate reaches 98.92 % . Our method effectively evaluates the spatial-temporal characteristics of traffic data, enabling accurate prediction and detection of traffic incidents. Its technical applicability holds promise for enhancing the capacity and efficiency of future traffic control systems.

Reconfigurable Homojunction Phototransistor for Near-Zero Power Consumption Artificial Biomimetic Retina Function.

Semiconductor photodetectors integrating preliminary signal-processing functions play a vital role in artificial biomimetic retina systems. Herein, we propose a tungsten diselenide (WSe2) phototransistor with a dual-layer gate dielectric and an asymmetric graphene insert structure. This phototransistor exhibits a bidirectional self-powered photocurrent by controlling the gate voltage via the formation of reconfigurable p+-p and n-p homojunctions in the channel from the asymmetric graphene insert. At the same time, the nonvolatile electron and hole stored in the dual-layer gate dielectric are generated using a temporary gate voltage, which can replace the gate voltage to regulate the channel charge. Moreover, the photocurrent shows a linear relation with the temporary programming gate voltage. The phototransistor exhibits a rectification ratio of >4 orders of magnitude without the gate voltage, indicating its significant capability to operate in a fully self-powered mode with near-zero power consumption. Based on the device characteristics, we successfully simulate the biological functions of the photoreceptor layer and bipolar cell layer in the retinal receptive field. The identification of the object motion direction in the receptive field can be realized by integrating three programmable devices on the chip. Furthermore, edge enhancement of the image is achieved by independently modulating the light response of each pixel in the sensor by varying the programming gate voltage. This study will promote the developing progress of future artificial biomimetic retina systems.

Unmanned Ship Collision Avoidance Action Plan Deduction Method under Man–Machine Interactive Negotiation in Collision Avoidance Scenarios

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships.

A Novel Feature Selection Method for Ultra High Dimensional Survival Data

Finding relevant features in ultra-high dimensional survival data is one of the most important and fundamental objectives in biology discovery and statistical acquisition. Conventional survival regression algorithms are challenged by the exponential increase in raw data. In real-world scenarios, data processing with ultra-high dimensionality has an impact, particularly on two-component structures like the kidneys, lungs, and eyes. Future system stability and the frequency of illness are both affected by gene interactions between two components. The traditional statistical procedures employed for the survival system are restricted to single component. To date, for ultra-high-dimensional survival data with two compartments, no feature selection method is available. Thus, with the goal to determine the optimal methods in this situation, this study suggested and contrasted the performance of ten variable selection approaches for ultra-high dimensional Renal Cell Carcinoma (RCC) survival data containing two compartments. The study attempted to combine Freund’s baseline hazard function as the baseline hazard of Cox model (Lasso Freund, Robust Lasso Freund, Elastic Net Freund) and integrated with sure independence screening (SIS) and iterative sure independence screening (ISIS) (i.e., LF-SIS, RLF-SIS, ENF-SIS, LF-ISIS, RLF-ISIS, ENF-ISIS) in an attempt to tackle this issue. Additionally, two basic approaches, LASSO and EN, were taken into consideration and EN is combined with SIS and ISIS (EN-SIS, EN-ISIS). Result shows that based on the validating model measures, including MSE (340.000), SSE (25300.0) and RMSE (16.490) suggest, the Robust Lasso Freund-Iterative Sure Independence Screening (RLF-ISIS) and Robust Lasso Freund-Sure Independence Screening (RLF-SIS) strategy performs superior to the other suggested approaches in terms of greater precision in picking variables. Though both methods showed lower R2 (0.71) which advocates the presence of the outliers in the dataset. Additionally, the box-plot of some selected predictive genes confirms the presence of outliers. Furthermore, two methods, RLF-ISIS and RLF-SIS, have been used to identify 49 and 68 genes that have both direct and indirect effects on patients with RCC. Finally, it can be concluded that although RLF-SIS and RLF-ISIS outperform other proposed approaches, they may, however, be regarded as a variable selection strategy but they might not be the optimal choice for ultra-high dimensional survival data with outliers. Nevertheless, the study can be expanded in the future by applying competitive risk theory to a sequential and parallel structure, which serves as the basis for most complex mechanical systems found in manufacturing facilities. Notably, no feature selection method is available for ultra-high-dimensional survival data with outliers and two-compartments. Therefore, to address this particular issue, further research should focus on developing an advanced hybrid feature selection approach, with a particular emphasis on deep learning strategies.

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

Systemic Innovation in the European Energy Sector: A Collective Outlook

Applying the principles of systems thinking in the evolution of energy systems is a crucial approach to meet the long-term horizons of goals in the energy sector. Through a participatory approach, this paper outlines what we view as the trends shaping the current development of the energy sector; the challenges we must face as a result, and the transitions required to tackle them. Furthermore, we discuss how systems thinking can address these challenges through changing not the components, but rather their relationships and structures. Contributing to the emerging stream of literature applying more holistic approaches to energy transitions, in contrast to more linear systems modelling. We provide innovative insights into how future energy systems could be designed to become more regenerative, resilient, empowering, whilst technologically- and economically feasible; to overcome the challenges of carbon lock-in effects and carbon capture, the rebound effects of efficiency strategies, and social sustainability for just transitions.

Predictive modeling in high-frequency trading using machine learning

High-frequency trading (HFT) has transformed financial markets by enabling rapid trade execution and exploiting minute market inefficiencies. This study explores the application of machine learning (ML) techniques to predictive modeling in HFT. Four ensemble boosting methodsAdaptive Boosting, Logic Boosting, Robust Boosting, and Random Under-Sampling (RUS) Boostingwere evaluated using order book data from Euronext Paris. The models were trained and validated on data from a single trading day, with performance assessed using precision, recall, ROC curves, and feature importance analysis. Results indicate that Robust Boosting achieves the highest precision (90%), while Adaptive Boosting and RUS Boosting demonstrate higher recall (94% and 93%, respectively). This research highlights the potential of ML in enhancing HFT strategies, with implications for future trading system developments.

Enhancing vertical assembly of Dot-LEDs for display application using metal chelate coordination chemical linkers

The chemical linker assembly technique for dot-LEDs described in this study offers practical solutions to critical challenges in micro-LED display production, such as high costs, low yield in mass transfer processes, and high cost of micro-LED chips. Dot-LEDs feature a cylindrical structure with a diameter ranging from 0.75 to 1.35 μm and an aspect ratio of 1.1∼1.2 for height to diameter. We introduce a viable approach using N-(carboxymethyl)-N-(11-mercaptoundecyl)glycine (gly-thiol) chemical linkers for the face-selective vertical assembly of dot-LEDs. The size scale of dot-LED enables solution assembly processes when manufacturing pixels. By bonding the Au surface of the p-GaN face to the bottom Au electrodes using the chelate bond of Au-gly-thiol-Zn2+-gly-thiol-Au, we intend to enhance the face-selective vertical assembly. The process involves forming two chelate coordination bonds of Zn2+ ion surrounded by a gly-thiol-treated dot and bottom electrodes, achieving over 60 % efficiency in face-selective vertical assembly. We achieved bright and uniform dot-LED electroluminescence devices, with a peak external quantum efficiency (EQE) of 8.1 % at 3.4 V and a luminance of 22,387 cd/m2 at 9.0 V. Our findings suggest substantial improvements in the assembly and efficiency of micro-nano-scale dot-LED displays, providing a display platform technology for their application in future display systems.

IMPLEMENTASI METODE WEBQUAL 4.0 TERHADAP KEPUASAN MAHASISWA PADA PENGGUNAAN SISTEM INFORMASI AKADEMIK (SIAKAD) UNIVERSITAS PRABUMULIH

The rapid development of information and communication technology has driven increased efficiency across various sectors, including academics. The Academic Information System (SIAKAD) at Universitas Prabumulih is an integrated service for lecturers, students, and staff to support academic activities. This study aims to evaluate user satisfaction with SIAKAD using the Webqual 4.0 model, which assesses website quality based on three dimensions: usability quality, information quality, and service interaction quality, and their impact on user satisfaction. The results of this study are expected to provide a comprehensive overview of SIAKAD's strengths and weaknesses, serving as a foundation for future system improvements.Keywords: SIAKAD, User Satisfaction, Webqual 4.0 Method

Evolution and development tendencies of material handling machines

The tools used during mobilities are very diverse, but their operation and structure can be traced back to the same basic principles that created the material handling machines during the previous centuries. Today, knowledge of material handling machines has become a basic necessity not only for industrial and service processes, but also for people's everyday lives, since our lives are almost unimaginably filled with different means of transportation, or without using the vehicles that transport and load our daily consumer goods. As in all areas, material handling is becoming more and more modern, and equipment that serves social and economic needs at a high level is used. One of the most defining characteristics of today's consumer society is extremely rapid change, which also affects our devices, so the design of material handling machines is also constantly changing. An important question of competitive service is how to meet rapidly changing needs. In my article, I review the development of material handling equipment, or the most important handling machines used in modern systems. Based on the outlined processes and technical changes, I will try to show where material handling is headed, what trends prevail today or are expected in server systems of the near future.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Analysis and Suppression of Oscillations in Doubly Fed Variable Speed Pumped Storage Hydropower Plants Considering the Water Conveyance System

The doubly fed variable speed pumped storage (DFVSPS) system is a hydraulically, mechanically, and electrically coupled system, and the characteristics of the components from the water conveyance system to the transmission line need to be fully considered in the oscillation analysis. Hence, the model of the water conveyance system is included to investigate the oscillation characteristics of the DFVSPS connecting to the grid via a series-compensated line. A small-signal state-space model of the DFVSPS system in the generation mode is first established. The oscillation characteristics of the DFVSPS are studied, and the dominant state variables for each oscillation mode are identified. The impact of system parameters on oscillations is further studied, and simulations are carried out to validate the accuracy of the model. The results indicate the oscillation mode of the DFVSPS comprises the electrical sub-synchronous oscillation (SSO) mode and the hydraulically, mechanically coupled low-frequency mechanical oscillation modes. When the series compensation level is high, the SSO becomes divergent, and the system is more likely to be unstable. Optimizing the rotor-side control parameters and the governor control parameters, sub-synchronous and low-frequency oscillations could be effectively suppressed, respectively. This study provides reference suggestions for the development and use of the future DFVSPS system.

A review of neural networks in handwritten character recognition

Handwritten character recognition has been a significant research focus in the fields of pattern recognition and artificial intelligence over the past decade. With the advent of neural networks, particularly deep learning models, the accuracy and efficiency of offline handwritten character recognition have dramatically improved. This paper presents a comprehensive review of recent developments in applying neural networks to handwritten character recognition. The literature review covers studies conducted between 2016 and 2023, providing insights into the methodologies, data processing techniques, and evaluation metrics used. The review spans various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. It categorizes and compares their performance across multiple benchmark datasets, highlighting specific improvements in recognition accuracy and efficiency. Furthermore, the review discusses the challenges faced in large-scale datasets, such as the diversity of handwriting styles and computational cost constraints. Notably, CNNs have shown outstanding performance, but the integration of advanced techniques like transfer learning and Generative Adversarial Networks (GANs) is explored as a potential avenue to enhance future recognition systems.

Smart utilization of bimetallic NiCo alloy supported carbon dots and sulfur quantum dots derived 3D nanocomposite for oxygen evolution reaction

The increasing demand for highly active and stable electrocatalysts for the oxygen evolution reaction (OER) in future energy-conversion systems has driven researchers to explore novel alternatives to Pt-based catalysts. Among these alternatives, zero-dimensional quantum dots have emerged as promising candidates due to their high surface area and intrinsic electrocatalytic activity, which lead to significant breakthroughs in electrocatalytic performance. Herein, we conducted a preliminary investigation into the application of bimetallic NiCo alloy deposited on metal-free quantum dots (carbon dots and sulfur quantum dots) derived nano sponge for the oxygen evolution reaction. The synthesized NiCo-CDs-800 exhibited remarkable electrocatalytic OER activity, achieving a current density of 10 mA cm−2 with only 221 mV overpotential, far surpassing commercial RuO2 electrocatalysts. The excellent electrocatalytic activity can be attributed primarily to high electrochemically active area (168.97 mF cm−2), the presence of large proportion of oxygen vacancies (68.61 %), rich pyridinic nitrogen structure (29.30 %) with strong water adsorption, and synergistic effect of bimetals as supported by density functional theory calculations. Meanwhile, the newly prepared NiCo-SQDs-700 under the similar procedure demonstrate a satisfied OER performance and competing stability when compared to NiCo-CDs. These findings open a new avenue for the application of metal free CDs and SQDs to the growing family of metal@QDs.

An investigation of public trust in autonomous humanoid AI robot doctors: a preparation for our future healthcare system

As a preparation for our future healthcare system with artificial intelligence (AI)-based autonomous robots, this study investigated the level of public trust in autonomous humanoid robot (AHR) doctors that would be enabled by AI technology and introduced to the public for the sake of better healthcare accessibility and services in the future. Employing the most frequently adopted scales in measuring patients’ trust in their primary care physicians (PCPs), this study analyzed 413 survey responses collected from the general public in the United States and found trust in AHR nearly matched the level of trust in human doctors, although it was slightly lower. Based on the results of data analysis, this study provided explanations about the benefits of using AHR doctors and some proactive recommendations in terms of how to develop AHR doctors, how to implement them in actual medical practices, more frequent exposure of humanoid robots to the public, and the need of interdisciplinary collaboration to enhance public trust in AHR doctors. This line of study is urgently demanded because the placement of such advanced robot technology in the healthcare system is unavoidable as the public has experienced it more these days. The limitations arising from the non-experimental design, a voluntary response sampling through social media, and few theories on communication with humanoid robots remain tasks for future studies.

User Experience and the Role of Personalization in Critiquing-Based Conversational Recommendation

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one’s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user’s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized , that recommends any item consistent with the user critiques; and (ii) personalized , which leverages a user’s past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system’s performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems.

StarMalloc: Verifying a Modern, Hardened Memory Allocator

We present StarMalloc, a verified, efficient, security-oriented, and concurrent memory allocator. Using the Steel separation logic framework, we show how to specify and verify a multitude of low-level patterns and delicate security mechanisms, by relying on a combination of dependent types, SMT, and modular abstractions to enable efficient verification. We produce a verified artifact, in C, that implements the entire API surface of an allocator, and as such works as a drop-in replacement for real-world projects, notably the Firefox browser. As part of StarMalloc, we develop several generic datastructures and proof libraries directly reusable in future systems verification projects. We also extend the Steel toolchain to express several low-level idioms that were previously missing. Finally, we show that StarMalloc exhibits competitive performance by evaluating it against 10 state-of-the-art memory allocators, and against a variety of real-world projects, such as Redis, the Lean compiler, and the Z3 SMT solver.

Exploit Vulnerabilities in 4G and 5G Cellular Access Network

Portable gadgets back different specialized highlights and administrations for up-and-coming 2G, 3G, 4G and 5G systems. For illustration, these determinations contain physical layer rate sorts, radio convention data, security calculations, carrier conglomeration groups, and benefit sorts such as GSM-R, Voice over LTE, Within the setting of portable security standardization, these organize determinations and administrations are alluded to as gadget capabilities and are traded with the organize amid the gadget enrolment stage. In this article, we investigate data around indicated gadget capabilities for 4G and 5G gadgets and their part in building up a secure interface between the gadget and the arrange. Our inquire about comes about appear the plausibility of the gadget being traded with the carrier some time recently the confirmation step without any ensures and not affirmed by the carrier. Therefore, we display three unused sorts of assaults that misuse data approximately the capabilities of unprotected gadgets in future 5G systems: Character assaults, sending assaults, and battery deplete assaults against versatile gadgets. Conduct verification of concept assaults utilizing reasonable equipment and computer program arrangements to survey their effect on commercial 4G gadgets and systems. We have detailed the distinguished vulnerabilities to pertinent benchmarks bodies and given countermeasures to relieve assaults against gadget capabilities in up-and-coming 5G systems.

A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation.

To present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues. Electrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies. We conducted intracranial physiology studies on five consecutive patients with Parkinson's disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials. After artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite. The proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.