11 research outputs found
Emergency Resource Layout with Multiple Objectives under Complex Disaster Scenarios
Effective placement of emergency rescue resources, particularly with joint
suppliers in complex disaster scenarios, is crucial for ensuring the
reliability, efficiency, and quality of emergency rescue activities. However,
limited research has considered the interaction between different disasters and
material classification, which are highly vital to the emergency rescue. This
study provides a novel and practical framework for reliable strategies of
emergency rescue under complex disaster scenarios. The study employs a
scenario-based approach to represent complex disasters, such as earthquakes,
mudslides, floods, and their interactions. In optimizing the placement of
emergency resources, the study considers government-owned suppliers, framework
agreement suppliers, and existing suppliers collectively supporting emergency
rescue materials. To determine the selection of joint suppliers and their
corresponding optimal material quantities under complex disaster scenarios, the
research proposes a multi-objective model that integrates cost, fairness,
emergency efficiency, and uncertainty into a facility location problem.
Finally, the study develops an NSGA-II-XGB algorithm to solve a disaster-prone
province example and verify the feasibility and effectiveness of the proposed
multi-objective model and solution methods. The results show that the
methodology proposed in this paper can greatly reduce emergency costs, rescue
time, and the difference between demand and suppliers while maximizing the
coverage of rescue resources. More importantly, it can optimize the scale of
resources by determining the location and number of materials provided by joint
suppliers for various kinds of disasters simultaneously. This research
represents a promising step towards making informed configuration decisions in
emergency rescue work
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
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
TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety
Large Language Models (LLMs) have shown remarkable effectiveness in various
general-domain natural language processing (NLP) tasks. However, their
performance in transportation safety domain tasks has been suboptimal,
primarily attributed to the requirement for specialized transportation safety
expertise in generating accurate responses [1]. To address this challenge, we
introduce TrafficSafetyGPT, a novel LLAMA-based model, which has undergone
supervised fine-tuning using TrafficSafety-2K dataset which has human labels
from government produced guiding books and ChatGPT-generated instruction-output
pairs. Our proposed TrafficSafetyGPT model and TrafficSafety-2K train dataset
are accessible at https://github.com/ozheng1993/TrafficSafetyGPT
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
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
The ChatGPT, a lite and conversational variant of Generative Pretrained
Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large
Language Models (LLMs) with billions of parameters. LLMs have stirred up much
interest among researchers and practitioners in their impressive skills in
natural language processing tasks, which profoundly impact various fields. This
paper mainly discusses the future applications of LLMs in dentistry. We
introduce two primary LLM deployment methods in dentistry, including automated
dental diagnosis and cross-modal dental diagnosis, and examine their potential
applications. Especially, equipped with a cross-modal encoder, a single LLM can
manage multi-source data and conduct advanced natural language reasoning to
perform complex clinical operations. We also present cases to demonstrate the
potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical
application. While LLMs offer significant potential benefits, the challenges,
such as data privacy, data quality, and model bias, need further study.
Overall, LLMs have the potential to revolutionize dental diagnosis and
treatment, which indicates a promising avenue for clinical application and
research in dentistry
A matched case-control analysis of autonomous vs human-driven vehicle accidents
Abstract Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles’ levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous’ versus Human-Driven Vehicles’ accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements
Resilient Distributed Coordination of Plug-In Electric Vehicles Charging under Cyber-Attack
The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method
Comparative Analysis of Machine-Learning Models for Recognizing Lane-Change Intention Using Vehicle Trajectory Data
Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using vehicle trajectory data to select a model for identifying vehicle LC intentions. Considering longitudinal and lateral dimensions, the information extracted from vehicle trajectory data includes the interactive effects among target and adjacent vehicles (54 indicators) as input parameters. The LC intention of the target vehicle serves as the output metric. This study compares three widely recognized machine-learning models: support vector machines (SVM), ensemble methods (EM), and long short-term memory (LSTM) networks. The ten-fold cross-validated method was used for model training and evaluation. Classification accuracy and training complexity were used as critical metrics for evaluating model performance. A total of 1023 vehicle trajectories were extracted from the CitySim dataset. The results indicate that, with an input length of 150 frames, the XGBoost and LightGBM models achieve an impressive overall classification performance of 98.4% and 98.3%, respectively. Compared to the LSTM and SVM models, the results show that the two ensemble models reduce the impact of Types I and III errors, with an improved accuracy of approximately 3.0%. Without sacrificing recognition accuracy, the LightGBM model exhibits a sixfold improvement in training efficiency compared to the XGBoost model