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The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi'an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas. [ABSTRACT FROM AUTHOR]

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems' greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. [ABSTRACT FROM AUTHOR]

Miniature Synthetic Aperture Radar (MiniSAR) has been making breakthroughs to effectively overcome the limitations of time and space, and has exhibited superiority with respect to light weight and low- power consumption as well as high flexibility to achieve high-resolution imaging for the Region Of Interest (ROI). However, imaging signal processing for MiniSAR systems still face several technical challenges such as high-resolution imaging of ground targets under complicated trajectories, refocusing of non-cooperative moving targets, and efficient and real-time processing of echo data. In this paper, a series of imaging signal processing and associated hardware designs using Field-Programmable Gate Array (FPGA) architecture have been proposed to realize MiniSAR ultra-high-resolution imaging and real-time processing. Additionally, experimental results utilizing considerable spotlight and stripmap MiniSAR data have been presented to demonstrate the effectiveness and reliability of the proposed technology.

This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers' sensitivity to travel time, availability of budget, and decision makers' preference on the values of the two objectives are investigated as well., Author(s): Xinhua Mao [1]; Jibiao Zhou (corresponding author) [2]; Changwei Yuan [3]; Dan Liu [1] 1. Introduction Road infrastructure forms the backbone of transport activities, which plays an important role [...]

For real-time autofocus of defocused images produced by Synthetic Aperture Radar (SAR), the twodimensional autofocus approach proposed in this study is used to correct the residual range cell migration and compensate for the phase error. Next, a block-wise Phase Gradient Autofocus (PGA) is used to correct the space-variant phase error. The Field-Programmable Gate Array (FPGA) design procedures, resource utilization, processing speed, accuracy, and autofocus are discussed in detail. The system is able to autofocus an 8K × 8K complex image with single precision within 5.7 s when the FPGA works at 200 MHz. The processing of the measured data verifies the effectiveness and real-time capability of the proposed method.

Network-wide maintenance lacks strong theoretical support and practical cases. However, research on this topic has entered an extensive exploratory stage; for example, new network design methods are being sought, and successful practices from traditional maintenance by line and by profession are being incorporated. This paper proposes a novel set-covering model with the variable coverage radius for the maintenance network of urban rail transit systems in the context of network-wide maintenance. The concepts of network-wide maintenance follow principles that are similar to those of bio-geography-based optimization (BBO), i.e., patterns of migration, variation, and extinction of different populations in different habitats. Therefore, a BBO algorithm is implemented with combinatorial optimization programming. Experiments from Changchun city in China show that the proposed model and algorithm are effective in fulfilling network-wide requirements through a direct tradeoff between the coverage radius and maintenance response time. In addition, the maintenance capacity and variable coverage radius of each maintenance point influence both the maintenance timeliness and resource utilization of maintenance units., 1. Introduction As of December 31, 2018, according to a statistical analysis of urban rail transit lines, 169 urban lines in 32 cities in mainland China were in operation, having [...]