Shared Autonomous Vehicle (SAV) fleet operations across the Minneapolis-Saint Paul region, with emphasis on empty travel, response times, and no-idling laws over space and time of day

Many well-known enterprises are road-testing fully-automated vehicles (AVs), including General Motors, Waymo, Uber, Tesla, and Apple. Most AVs are expected to be used in shared AV (SAV) fleets initially, for daily trip-by-trip use, as an autonomous ride-hailing service. SAVs will allow savings on vehicle ownership and maintenance costs, parking search time, and parking access times. This study micro-simulates passenger travel throughout the Minneapolis-Saint Paul (MSP) region of Minnesota, when relying on a system of SAVs. The extended region includes 9.5 million person-trips per weekday, 7 counties, 2485 traffic analysis zones (TAZs), and about 42,000 roadway links (obtained using OpenStreetMap). An agent-based toolkit, MATSim, allows tracking of individual travelers throughout the day and across their activity locations. The region's metropolitan planning organization, Metropolitan Council, provided all travelers' itineraries, trip purposes, origins, and destinations, along with land use data (jobs and population counts) by TAZ. To simulate SAV assignments to each traveler requesting a trip, along with traveler wait times and arrival times at their destinations, the code form Hörl (2017) - who extended Bischoff and Maciejewski's (2016) MATSim codes - and MATSim's autonomous mobility-on-demand (AMoD) simulator were used here. The SAV fleet size and starting locations were specified before a typical weekday's simulation for 2015. All travel demands sampled from the MSP population must be met by the SAV fleet if they can be met within a pre-specified max-wait-time duration of 1 hour. Travelers are assumed to cancel their SAV request after waiting more than 1 hour. Finally, special SAV parking lots or waiting areas were created to avoid SAVs idling on busy streets in the downtown and other popular locations, between serving trips, to see how such curb-use policies affect wait times and other fleet performance metrics. Using supercomputers, this work simulated 180,000 person-trips and 450,000 person-trips (2% and 5% of the region's 9.2 million daily person-trips) and 480,000 person-trips for the Twin Cities over a 24-hour weekday. Results suggest that the average SAV in this region can serve at most 30 person-trips per day with less than 5 minutes of average wait time for travelers, thus replacing about 10 household vehicles (assuming no one needs to leave the region) but generating another 13 % vehicle-miles traveled (VMT) each day, thereby adding some congestion to the network. By enabling and encouraging active use of for dynamic ride-sharing (DRS), where strangers share rides together, the SAV fleetwide VMT fell, on average, by 17% - and empty VMT (eVMT) fell by 26%, as compared to scenarios without DRS. Interestingly, the 81% and 84% of TAZs with less than 6 minutes average wait times (in the AM and PM peak periods, respectively) are uniformly distributed over this large, 7-county region, suggesting that MSP residents will enjoy similar SAV service levels everywhere (though response times do rise during peak times of day). For the Twin Cities region, most eVMT emerges in the northern and southern subregions, rather than in the cities' CBDs. eVMT and wait times are relatively high during the AM and PM peak periods (6 am to 9 am and 3 pm to 6 pm) but fall significantly during the PM peak period if DRS is offered and actively used by travelers. When compared to idling-at-curb scenarios, the no-idling-on-busy-downtown-road-segment scenarios (using central SAV parking lots) generated 8% more VMT, while eVMT rose by 9 percentage points on average, across all 4 companion scenarios. This study also estimated various energy and emissions savings of SAVs versus the U.S. status quo. Compared to the average household passenger car (a 4-door sedan), which uses 31 miles per gallon, a fleet of 52 mi/gallon hybrid electric SAVs are estimated here to lower the energy demands by 21% and emission related health costs by roughly 30%, sulfur dioxide (SO₂) by 20%, carbon monoxide (CO) by 46%, oxides of nitrogen (NOx) by 30%, volatile organic compounds (VOC) by 48%, particulate matter that is 10 micrometers or less in effective diameter (PM10) by 20%, carbon dioxide (CO₂) by 20% and methane (CH₄) by 35%. Such fleet shifts would save roughly 30% in emissions-related health costs and 64% in energy use.Civil, Architectural, and Environmental Engineerin

An adaptive method for inertia force identification in in cantilever under moving mass

The present study is concerned with the adaptive method based on wavelet transform to identify the inertia force between moving mass and cantilever. The basic model of cantilever is described and a classical identification method is introduced. Then the approximate equations about the model of cantilever can be obtained by the identification method. However, the order of modal adapted in the identification methods is usually constant which may make the identification results unsatisfied. As is known, the frequency of the highest order of modal is usually higher than the frequency of the input force in forward calculation methods. Therefore, wavelet transform is applied to decompose the data of deflection. The proportion of the low frequency component is chosen as the parameter of a binary function to decide the order of modal. The calculation results show that the adaptive method adapted in this paper is efficient to improve the accuracy of the inertia force between the moving mass and cantilever, and also the relationship between the proportion of low frequency component and the order of modal is indicated

PAGE: Equilibrate Personalization and Generalization in Federated Learning

Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients' current demands and the server's future demand, local model personalization and global model generalization are separately investigated, as the ill-effects of data heterogeneity enforce the community to focus on one over the other. However, these two seemingly competing goals are of equal importance rather than black and white issues, and should be achieved simultaneously. In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. To explore the equilibrium, PAGE further formulates the game as Markov decision processes, and leverages the reinforcement learning algorithm, which simplifies the solving complexity. Extensive experiments on four widespread datasets show that PAGE outperforms state-of-the-art FL baselines in terms of global and local prediction accuracy simultaneously, and the accuracy can be improved by up to 35.20% and 39.91%, respectively. In addition, biased variants of PAGE imply promising adaptiveness to demand shifts in practice

A review on N-doped biochar for oxidative degradation of organic contaminants in wastewater by persulfate activation

The Persulfate-based advanced oxidation process is the most efficient and commonly used technology to remove organic contaminants in wastewater. Due to the large surface area, unique electronic properties, abundant N functional groups, cost-effectiveness, and environmental friendliness, N-doped biochars (NBCs) are widely used as catalysts for persulfate activation. This review focuses on the NBC for oxidative degradation of organics-contaminated wastewater. Firstly, the preparation and modification methods of NBCs were reviewed. Then the catalytic performance of NBCs and modified NBCs on the oxidation degradation of organic contaminants were discussed with an emphasis on the degradation mechanism. We further summarized the detection technologies of activation mechanisms and the structures of NBCs affecting the PS activation, followed by the specific role of the N configuration of the NBC on its catalytic capacity. Finally, several challenges in the treatment of organics-contaminated wastewater by a persulfate-based advanced oxidation process were put forward and the recommendations for future research were proposed for further understanding of the advanced oxidation process activated by the NBC

Molecular dynamics simulation of cathode crater formation in the cathode spot of vacuum arcs

Abstract A three-dimensional model based on molecular dynamics has been developed to describe the formation of a single cathode spot in vacuum arcs. The formation of the cathode spot is assumed to be controlled by the plasma ions, the effect of which is simulated in LAMMPS through the process of ion bombardment. The cathode is represented by structured copper atoms, while the ions are continuously injected into the domain with a certain velocity towards the cathode surface. Ion bombardment leads to the appearance of a crater, which is caused by the accumulation of pressure effect against the relaxation of substrate temperature. The size of the crater is found to be determined by the spatial distribution of the injected ions. The formation of the cathode spot is also scrutinised by electron emission from the cathode surface with variable surface temperature during the cathode spot development process. In addition, the evaporated atoms forming the metal vapour are observed. This study provides a description of the formation of the cathode spot at microscale, which shall be helpful to further studies of the arc rooting and arc contact (electrode) erosion in vacuum environment.</jats:p

Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

When the transformer is running, the vibration which is generated in the core and winding will spread outward through the medium of metal, oil, and air. The magnetic field of the core changes with the variation of the transformer excitation source and the state of the core, so the corresponding vibration and noise change. Therefore, the vibration and noise of the transformer contain a lot of information. If the information can be associated with the fault characteristics of the transformer, it is significant to evaluate the running state of the transformer through the vibration and noise signal, which improve the intelligence, safety, and stability of the transformer operation. Based on this, modeling and simulation of transformer multi-point grounding, DC bias, and short-circuit between silicon steel sheets fault are first carried out in this paper, and vibration and noise distribution of transformer under different faults are given. Second, a fault diagnosis method based on transformer vibration and noise characteristics is proposed. In the process of implementation, vibration and noise signals under multi-point grounding, DC bias, and short-circuit between silicon steel sheets are taken as the sample data, and the probabilistic neural network algorithm is used to effectively predict the transformer fault. Finally, the effectiveness of the proposed scheme is verified by identifying the simulation faults-the proposed fault diagnosis method based on PNN can be effectively applied to transformer