A Sustainable Digital Ecosystem: Digital Servitization Transformation and Digital Infrastructure Support

While the notion of digitalization and sustainability has become prominent in current research, more can be done to bridge these two concepts and explain the interaction between them. Plenty of literature has focused on the impact of digital technology applications and business model innovations on environmental performance but has not considered the counterforce of environmental performance on digitalization. We investigated this question from the perspective of digital ecosystem architects to explore more organic relationships. By analyzing data from 1083 listed firms from 2014 to 2019, we found various effective paths for architects to participate in the digital ecosystem and determined that improved environmental performance has led to more efficient convergence. Digital servitization adopted by private architects contributes to financial performance, whereas the addition of digital infrastructure enables public architects to play a greater role. This is reflected in the discovery that firms with “high” environmental performance can improve their financial performance far more significantly with the help of digital servitization compared to other firms. However, digital infrastructure development can benefit all firms almost indiscriminately. We encourage firms and governments to work together to strengthen digital infrastructure, build digital ecosystems, and focus on environmental performance while transitioning to digital servitization

Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method’s effectiveness

ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended

Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives

Clustering framework to identify traffic conflicts and determine thresholds based on trajectory data

Traffic conflict indicators are essential for evaluating traffic safety and analyzing trajectory data, especially in the absence of crash data. Previous studies have used traffic conflict indicators to predict and identify conflicts, including time-to-collision (TTC), proportion of stopping distance (PSD), and deceleration rate to avoid a crash (DRAC). However, limited research is conducted to understand how to set thresholds for these indicators while accounting for traffic flow characteristics at different traffic states. This paper proposes a clustering framework for determining surrogate safety measures (SSM) thresholds and identifying traffic conflicts in different traffic states using high-resolution trajectory data from the Citysim dataset. In this study, unsupervised clustering is employed to identify different traffic states and their transitions under a three-phase theory framework. The resulting clusters can then be utilized in conjunction with surrogate safety measures (SSM) to identify traffic conflicts and assess safety performance in each traffic state. From different perspectives of time, space, and deceleration, we chose three compatible conflict indicators: TTC, DRAC, and PSD, considering functional differences and empirical correlations of different SSMs. A total of three models were chosen by learning these indicators to identify traffic conflict and non-conflict clusters. It is observed that Mclust outperforms the other two. The results show that the distribution of traffic conflicts varies significantly across traffic states. A wide moving jam (J) is found to be the phase with largest amount of conflicts, followed by synchronized flow phase (S) and free flow phase(F). Meanwhile, conflict risk and thresholds exhibit similar levels across transitional states

Exploratory analysis of injury severity under different levels of driving automation (SAE Level 2-5) using multi-source data

Vehicles equipped with automated driving capabilities have shown the potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated driving systems are ongoing, there is limited research investigating the difference between injury severity outcomes of the ADAS and ADS vehicles using real-world crash data. To ensure comprehensive analysis, a multi-source dataset that includes the NHTSA crash database (752 cases), CA DMV crash reports (498 cases), and news outlet data (30 cases) is used. Two random parameters multinomial logit models with heterogeneity in the means and variances are estimated to gain a better understanding of the variables impacting the crash injury severity outcome for the ADAS (SAE Level 2) and ADS (SAE Levels 3-5) vehicles. We found that while 56 percent of crashes involving ADAS vehicles took place on a highway, 84 percent of crashes involving ADS took place in more urban settings. The model estimation results indicate that the weather indicators, traffic incident or work zone indicator, differences in the system sophistication that are captured by both manufacture year and high or low mileage, type of collision, as well as rear and front impact indicators all play a significant role in the crash injury severity. The results offer an exploratory assessment of the safety performance of the ADAS and ADS equipped vehicles in the real-world environment and can be used by the manufacturers and other stakeholder to dictate the direction of their deployment and usage

Template-Based Conjecturing for Automated Induction in Isabelle/HOL

Proof by induction plays a central role in formal verification. However, its automation remains as a formidable challenge in Computer Science. To solve inductive problems, human engineers often have to provide auxiliary lemmas manually. We automate this laborious process with template-based conjecturing, a novel approach to generate auxiliary lemmas and use them to prove final goals. Our evaluation shows that our working prototype, TBC, achieved 40 percentage point improvement of success rates for problems at intermediate difficulty level.Comment: To appear at Fundamentals of Software engineering 2023 (http://fsen.ir/2023/

A Novel Temporal Multi-Gate Mixture-of-Experts Approach for Vehicle Trajectory and Driving Intention Prediction

Accurate Vehicle Trajectory Prediction is critical for automated vehicles and advanced driver assistance systems. Vehicle trajectory prediction consists of two essential tasks, i.e., longitudinal position prediction and lateral position prediction. There is a significant correlation between driving intentions and vehicle motion. In existing work, the three tasks are often conducted separately without considering the relationships between the longitudinal position, lateral position, and driving intention. In this paper, we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention. The proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In the model, the shared layer utilizes Temporal Convolutional Networks (TCN) to extract temporal features. Then the expert layer is built to identify different information according to the three tasks. Moreover, the fully connected layer is used to integrate and export prediction results. To achieve better performance, uncertainty algorithm is used to construct the multi-task loss function. Finally, the publicly available CitySim dataset validates the TMMOE model, demonstrating superior performance compared to the LSTM model, achieving the highest classification and regression results. Keywords: Vehicle trajectory prediction, driving intentions Classification, Multi-tas

Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumption. This also inevitably increases the difficulty of implementation on wearable Artificial Intelligence-of-Things (AIoT) devices with limited resources. In this study, we proposed a universally applicable ultra-lightweight binary neural network(BNN) that is capable of 5-class and 17-class arrhythmia classification based on ECG signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full precision 98.00%) accuracy for 5-class and 17-class classification, respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB). Compared to other binarization works, our approach excels in supporting two multi-classification modes while achieving the smallest known storage space. Moreover, our model achieves optimal accuracy in 17-class classification and boasts an elegantly simple network architecture. The algorithm we use is optimized specifically for hardware implementation. Our research showcases the potential of lightweight deep learning models in the healthcare industry, specifically in wearable medical devices, which hold great promise for improving patient outcomes and quality of life. Code is available on: https://github.com/xpww/ECG_BNN_NetComment: 6 pages, 3 figure

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both.This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1140 minutes of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multi-video stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post-encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations' three-dimensional base maps and signal timings.Comment: Transportation Research Record (2023