Stable Matching for Dynamic Ride-sharing Systems

Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide the most convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly-stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles and individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules may substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers

LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification

The fusion of hyperspectral and light detection and range (LiDAR) data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images (HSIs), despite that band selection methods have been intensively studied for HSI processing. This article addresses this significant gap by introducing a cross-attention mechanism from the transformer architecture for the selection of HSI bands guided by LiDAR data. LiDAR provides high-resolution vertical structural information, which can be useful in distinguishing different types of land cover that may have similar spectral signatures but different structural profiles. In our approach, the LiDAR data are used as the “query” to search and identify the “key” from the HSI to choose the most pertinent bands for LiDAR. This method ensures that the selected HSI bands drastically reduce redundancy and computational requirements while working optimally with the LiDAR data. Extensive experiments have been undertaken on three paired HSI and LiDAR datasets: Houston 2013, Trento, and MUUFL. The results highlight the superiority of the cross-attention mechanism, underlining the enhanced classification accuracy of the identified HSI bands when fused with the LiDAR features. The results also show that the use of fewer bands combined with LiDAR surpasses the performance of state-of-the-art fusion models

HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatial analysis. Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers. However, it avoids the associated high computational demands. HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis. This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers. HSIMamba were tested against three widely recognized datasets Houston 2013, Indian Pines, and Pavia University and demonstrated exceptional performance, surpassing existing state-of-the-art models in HSI classification. This method highlights the methodological innovation of HSIMamba and its practical implications, which are particularly valuable in contexts where computational resources are limited. HSIMamba redefines the standards of efficiency and accuracy in HSI classification, thereby enhancing the capabilities of remote sensing applications. Hyperspectral imaging has become a crucial tool for environmental surveillance, agriculture, and other critical areas that require detailed analysis of the Earth surface. Please see our code in HSIMamba for more details.Comment: 11 pages, 2 figures, 8 table

Review of Recent Progress of Plasmonic Materials and Nano-Structures for Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) has demonstrated single-molecule sensitivity and is becoming intensively investigated due to its significant potential in chemical and biomedical applications. SERS sensing is highly dependent on the substrate, where excitation of the localized surface plasmons (LSPs) enhances the Raman scattering signals of proximate analyte molecules. This paper reviews research progress of SERS substrates based on both plasmonic materials and nano-photonic structures. We first discuss basic plasmonic materials, such as metallic nanoparticles and nano-rods prepared by conventional bottom-up chemical synthesis processes. Then, we review rationally-designed plasmonic nano-structures created by top-down approaches or fine-controlled synthesis with high-density hot-spots to provide large SERS enhancement factors (EFs). Finally, we discuss the research progress of hybrid SERS substrates through the integration of plasmonic nano-structures with other nano-photonic devices, such as photonic crystals, bio-enabled nanomaterials, guided-wave systems, micro-fluidics and graphene.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by MDPI. The published article can be found at: http://www.mdpi.com/journal/materialsKeywords: plasmonic materials, surface plasmons, photonic crystals, optical sensors, surface-enhanced Raman scatterin