14-10 Innovative Park-and-Ride Management for Livable Communities

Park-and-ride (P&R) has been recognized as an effective way to tackle the challenge of the last-mile problem in public transportation, i.e., connecting transit stations to final destinations. Although the design and operations of P&R facilities have been extensively investigated, there is a pressing need for a theoretically sound methodology for planning and managing P&R facilities. It is critically important to investigate where P&R facilities should be strategically located and how often transit service should be provided such that the net social benefit can be maximized. This project proposes an integrated planning methodology for locating P&R facilities and designing transit services simultaneously to promote public transportation and reduce traffic externalities in urban areas. The optimal P&R facility and transit service design problem is formulated as a mathematical program with complementarity constraints, and a solution algorithm based on the active-set approach is used to solve the optimal design problem effectively. A numerical example is employed to demonstrate that the optimal design shifts commuters from the automobile mode to transit and P&R modes and, hence, improves the net social benefit dramatically. The study provides a heretofore missing theoretical framework for integrated planning of P&R facilities and transit services

17-08 Transportation Access and Individuals with Disabilities\u27 Community Integration

This study examined the relationship between transit service patterns and the spatial organization of individuals with disabilities’ activities of daily living residing within Utah’s Wasatch Front region to provide recommendations to improve the design, planning, and management of the Utah Transit Authority’s public transportation system. The study objectives included an accessibility Index of Transit Provision to represent fixed-route bus and light-rail service capacity, and an Index of Transit Need representing the spatial organization of individuals with disabilities’ activities of daily living and indicators of transportation disadvantage. The findings suggest that 58.7% of individuals with disabilities living within the Wasatch Front Region do so in areas with greater than average transit disparity. The results identify 26 areas with very high transit disparity. Addressing those areas of higher transit disparity through prioritizing new transit investment or the reallocation of existing transit services will contribute to greater equity in individuals with disabilities’ access to activities of community living across the Wasatch Front Region

A Method of Quantitative Evaluation of Diagenetic Reservoir Facies of Tight Gas Reservoirs With Logging Multi-Parameters: A Case Study in Sulige Area, Northern Ordos Basin, China

Reservoir and flow characteristics of low, ultra-low permeability tight sandstone reservoir were largely controlled by diagenesis in reservoir assessment. In previous studies, diagenesis were researched only by using core analysis data, and it was difficult that diagenetic reservoir facies of the interval and the well without core analysis data were evaluated. Therefore, it was easy and quick that diagenetic reservoir facies were characterized with logging response characteristics which were extracted effectively. Taking tight gas reservoirs for example in Sulige area, northern Ordos Basin, China, logging response characteristics of different classification were analyzed by multiple samples with core analysis data, and the quantitative evaluation index of diagenetic reservoir facies based on logging multi-parameter was set up. A method of quantitative evaluation of diagenetic reservoir facies of tight gas reservoirs with logging multi-parameters was formed in the method of integration of analysis technology of Grey theory, and the accuracy and availability of the method were evaluated. The results shown that non-digitalized problems of diagenetic reservoir facies evaluation was solved by the digitalization method of logging multi-parameters, and the rate of accuracy, of returned classification using methods of mutual test, reached to 91.2%. The results provided a new and effective evaluation approach of low, ultra-low permeability tight sandstone reservoir

Approximate and Weighted Data Reconstruction Attack in Federated Learning

Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model without sharing their private data. Although FL is considered privacy-preserved by design, recent data reconstruction attacks demonstrate that an attacker can recover clients' training data based on the parameters shared in FL. However, most existing methods fail to attack the most widely used horizontal Federated Averaging (FedAvg) scenario, where clients share model parameters after multiple local training steps. To tackle this issue, we propose an interpolation-based approximation method, which makes attacking FedAvg scenarios feasible by generating the intermediate model updates of the clients' local training processes. Then, we design a layer-wise weighted loss function to improve the data quality of reconstruction. We assign different weights to model updates in different layers concerning the neural network structure, with the weights tuned by Bayesian optimization. Finally, experimental results validate the superiority of our proposed approximate and weighted attack (AWA) method over the other state-of-the-art methods, as demonstrated by the substantial improvement in different evaluation metrics for image data reconstructions

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate computational model is built in the low-dimensional feature space by a heteroscedastic Gaussian process. Active learning adaptively guides the surrogate model training toward the critical region that significantly contributes to the failure probability. A critical trait of the proposed method is that the three main ingredients-active subspace, heteroscedastic Gaussian process, and active learning-are coupled to adaptively optimize the feature space mapping in conjunction with the surrogate modeling. This coupling empowers the proposed method to accurately solve nontrivial high-dimensional reliability problems via low-dimensional surrogate modeling. Finally, numerical examples of a high-dimensional nonlinear function and structural engineering applications are investigated to verify the performance of the proposed method