Effects of Moving Bottlenecks on Traffic Operations on Four-lane Level Freeway Segments

The Highway Capacity Manual (HCM) was developed to provide capacity and level of service analyses for roadway facilities. Trucks may adversely affect the quality of traffic flow on a roadway. In HCM, the passenger car equivalent (PCE) of a truck, which represents the number of passenger cars that have an equivalent effect on traffic flow, is used to account for the impacts of trucks. However, in the past ten years rural freeways in the western rural U.S. have experienced conditions that lie outside the standard HCM conditions. Also, the current HCM truck PCEs may not be appropriate for the western rural U.S. This is because, the interstates in the western rural U.S. consistently experience truck percentages in an excess of 25 percent, but the highest truck percentage published in current HCM is 25 percent. Additionally, there are large free-flow speed differences between heavy trucks and passenger cars in western rural U.S., however, the current HCM estimates the PCEs under the assumption that trucks maintain the same speed as passenger cars on level terrain. Compounding the above two issues, trucks passing other trucks at low speed differentials may cause moving bottlenecks. This dissertation proposed a definition, developed identification methods for the moving bottlenecks on four-lane freeway segments, and developed metrics for measuring their effects. Then, this dissertation calculated PCEs under western rural U.S. traffic flow conditions with localized congestion caused by moving bottlenecks, by equal-density and equal-capacity method. Finally, this dissertation explored the impacts of changes in speed limits, truck passing restriction and data aggregation interval on PCEs. The results demonstrate moving bottlenecks have an adverse effect on vehicles on the freeway. It was found that the PCE values in the HCM 2010 and HCM 2016 underestimate the effect of heavy trucks on level terrain freeways that experience high truck percentage, and where different vehicle types have large differences in average free-flow speeds. The results also show that speed limits, percentage of truck passing restriction, and data aggregation interval significantly affect the PCEs. The results will be helpful in understanding how trucks affect passenger cars in moving bottlenecks. Advisor: Laurence R. Rilett (Chair), Elizabeth G. Jones (Co-chair

Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.Comment: Accepted at ICML 202

A Linear Fitting Density Peaks Clustering Algorithm for Image Segmentation

Clustering by fast search and finding of density peaks algorithm (DPC) is a recently developed method and can obtain promising results. However, DPC needs users to determine the number of clusters in advance, thus the clustering results are unstable and deeply influenced by the number of clusters. To address this issue, we proposed a novel algorithm, namely LDPC (Linear fitting Density Peaks Clustering algorithm). LDPC uses a novel linear fitting method to choose cluster centres automatically. In the experiments, we use public datasets to access the effectiveness of LDPC. Especially, we applied LDPC to image segmentation tasks. The experimental results show that LDPC can obtain competitive results compared with other clustering algorithms

Message Passing Algorithm for Solving QBF Using More Reasoning

We present a novel solver for solving Quantified Boolean Formulae problem (QBF). In order to improve the performance, we introduce some reasoning rules into the message passing algorithm for solving QBF. When preprocessing the formulae, the solver incorporates the equality reduction and the hyperbinary resolution. Further, the solver employs the message passing method to obtain more information when selecting branches. By using the unit propagation, conflict driven learning, and satisfiability directed implication and learning, the solver handles the branches. The experimental results also show that the solver can solve QBF problem efficiently

MathAttack: Attacking Large Language Models Towards Math Solving Ability

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the security of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. We will release our code and dataset.Comment: 11 pages, 6 figure

Graph Neural Networks for Job Shop Scheduling Problems:A Survey

Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain’s state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development