A Link-Based Day-to-Day Traffic Assignment Model

Existing day-to-day traffic assignment models are all built upon path flow variables. This paper demonstrates two essential shortcomings of these path-based models. One is that their application requires a given initial path flow pattern, which is typically unidentifiable, i.e., mathematically nonunique and practically unobservable. In particular, we show that, for the path-based models, different initial path flow patterns constituting the same link flow pattern generally gives different day-to-day link flow evolutions. The other shortcoming of the path-based models is the path-overlapping problem. That is, the path-based models ignore the interdependence among paths and thus can give very unreasonable results for networks with paths overlapping with each other. These two path-based problems exist for most (if not all) deterministic day-to-day dynamics whose fixed points are the classic Wardrop user equilibrium. To avoid the two path-based problems, we propose a day-to-day traffic assignment model that directly deals with link flow variables. Our link-based model captures travelers\u27 cost-minimization behavior in their path finding as well as their inertia. The fixed point of our link-based dynamical system is the classic Wardrop user equilibrium

A Generalized Flow Splitting Model for Day-to-day Traffic Assignment

AbstractThe splitting rate model proposed by Smith and Mounce (2011) establishes a traffic evolution process on a link-node network representation, which overcomes the difficulties in applying traditional path-based models and provides the ease of implementing controls at nodes. While their model offers a new method for modeling traffic evolution, it contains an ad-hoc step of flow adjustment to preserve the flow conservation. This flow adjustment step leads to difficulties in analyzing the system properties. This paper proposes a generalized flow splitting model for day-to-day traffic assignment based on the concept of splitting flow at nodes. The proposed model preserves the flow conservation endogenously by introducing the inflow variable into the formulation. The generalized formulation provides the ease to construct a variety of day-to-day traffic assignment models, and serves as a framework for analyzing the models’ properties, such as the invariance property and the preservation of the Lipschitz continuity and strong monotonicity. Specifically, a proportional-adjustment model and a projection-type model are developed based on the proposed generalized formulation. A numerical example demonstrates the ease of implementing the proposed generalized model, as well as its convergence to user equilibrium

Subthalamic nucleus dynamics track microlesion effect in Parkinson’s disease

Parkinson’s Disease (PD) is characterized by the temporary alleviation of motor symptoms following electrode implantation (or nucleus destruction), known as the microlesion effect (MLE). Electrophysiological studies have explored different PD stages, but understanding electrophysiological characteristics during the MLE period remains unclear. The objective was to examine the characteristics of local field potential (LFP) signals in the subthalamic nucleus (STN) during the hyperacute period following implantation (within 2 days) and 1 month post-implantation. 15 patients diagnosed with PD were enrolled in this observational study, with seven simultaneous recordings of bilateral STN-LFP signals using wireless sensing technology from an implantable pulse generator. Recordings were made in both on and off medication states over 1 month after implantation. We used a method to parameterize the neuronal power spectrum to separate periodic oscillatory and aperiodic components effectively. Our results showed that beta power exhibited a significant increase in the off medication state 1 month after implantation, compared to the postoperative hyperacute period. Notably, this elevation was effectively attenuated by levodopa administration. Furthermore, both the exponents and offsets displayed a decrease at 1 month postoperatively when compared to the hyperacute postoperative period. Remarkably, levodopa medication exerted a modulatory effect on these aperiodic parameters, restoring them back to levels observed during the hyperacute period. Our findings suggest that both periodic and aperiodic components partially capture distinct electrophysiological characteristics during the MLE. It is crucial to adequately evaluate such discrepancies when exploring the mechanisms of MLE and optimizing adaptive stimulus protocols

Comparative genomic analysis of esophageal squamous cell carcinoma among different geographic regions

IntroductionEsophageal squamous cell carcinoma (ESCC) shows remarkable variation in incidence, survival, and risk factors. Although the genomic characteristics of ESCC have been extensively characterized, the genomic differences between different geographic regions remain unclear.MethodsIn this study, we sequenced 111 patients with ESCC from northern (NC) and southern (SC) China, combined their data with those of 1081 cases from previous reports, and performed a comparative analysis among different regions. In total, 644 ESCC cases were collected from six geographic regions (NC, SC, Xinjiang, China [XJC], Japan [JP], Vietnam [VN], and Europe & America [EA]) as the discovery cohort. Validation cohort 1 included 437 patients with ESCC from the NC region. Validation cohort 2 included 54 and 57 patients from the NC and SC regions, respectively.ResultsPatients with ESCC in different regions had different genomic characteristics, including DNA signatures, tumor mutation burdens, significantly mutated genes (SMGs), altered signaling pathways, and genes associated with clinical features. Based on both the DNA mutation signature and the mutation profile of the most common genes, the NC and SC groups were clustered close together, followed by the JP, XJC, EA, and VN groups. Compared to patients with ESCC from SC, SMGs, including KMT2D, FAT1, and NOTCH1 were more frequently identified in patients with ESCC from NC. Furthermore, some genes (TDG and DNAH8) correlated with overall survival in completely opposite ways in patients with ESCC from different geographical regions.ConclusionsOur study provides insights into genomic differences in ESCC among different regions. These differences may be related to differences in environmental carcinogens, incidence, and survival

Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies

Inverse variational inequalities with projection-based solution methods

An inverse variational inequality is defined as to find a vector , such thatIf an inverse function u = F-1(x) exists, the above inverse variational inequality could be transformed as a regular variational inequality. However, in reality, it is not uncommon that the inverse function of F-1(x) does not have explicit form, although its functional values can be observed. Existing line search algorithms cannot be applied directly to solve such inverse variational inequalities. In this paper, we propose two projection-based methods using the co-coercivity of mapping F. A self-adaptive strategy is developed to determine the step sizes efficiently when the co-coercivity modulus is unknown. The convergence of the proposed methods is proved rigorously.Inverse variational inequality Co-coercivity Projection method Self-adaptive strategy

Global exponential stability of a neural network for inverse variational inequalities

We investigate the convergence properties of a projected neural network for solving inverse variational inequalities. Under standard assumptions, we establish the exponential stability of the proposed neural network. A discrete version of the proposed neural network is considered, leading to a new projection method for solving inverse variational inequalities, for which we obtain the linear convergence. We illustrate the effectiveness of the proposed neural network and its explicit discretization by considering applications in the road pricing problem arising in transportation science. The results obtained in this paper provide a positive answer to a recent open question and improve several recent results in the literature.</p

Recognition of driver's fatigue expression using Local Multiresolution Derivative Pattern

To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver's conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver's fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90 in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver's conditions towards the human-centric driver fatigue monitoring system