To mitigate car traffic problems, the United Nations Human Settlements Programme (UN-Habitat) issued a document that provides guidelines for sustainable development and the promotion of public transport. The efficiency of the policies and strategies needs to be evaluated to improve the performance of public transportation networks. To assess the performance of a public transport network, it is first necessary to select evaluation criteria. Based on existing indicators, this research proposes a public transport criteria matrix that includes the basic public transport infrastructure level, public transport service level, economic benefit level, and sustainable development level. A public transport criteria matrix AHP model is established to assess the performance of public transport networks. The established model selects appropriate evaluation criteria based on existing performance standards. It is applied to study the Stonnington, Bayswater, and Cockburn public transport network, representing a series of land use and transport policy backgrounds. The local public transport authorities can apply the established transport criteria matrix AHP model to monitor the performance of a public transport network and provide guidance for its improvement.

Multi-criteria decision-making (MCDM) methods have been extensively employed by researchers in the public transport (PT) sector. While many studies utilize a single MCDM method, employing multiple MCDM methods can achieve more robust results. Furthermore, integrated MCDM methods have the potential to address the weaknesses and limitations inherent in single MCDM method. In this study, we address the limitations of single MCDM methods by integrating the analytic hierarchy process (AHP) with the analytic network process (ANP) to evaluate public transport networks performance. Based on the established public transport criteria matrix AHP model, we identify interdependencies among criteria. The AHP-ANP method is then applied to assess public transport networks in three case study cities in Australia. The sensitivity analysis conducted on both the AHP and integrated AHP-ANP approaches highlights the influence of criteria interdependency. This research contributes to mitigating the limitations of AHP method and applies AHP-ANP model to provide decision-makers with insights to enhance PT network performance and achieve smart PT network management.

This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks.

This paper proposes a new optimization algorithm for backpropagation (BP) neural networks by fusing integer-order differentiation and fractional-order differentiation, while fractional-order differentiation has significant advantages in describing complex phenomena with long-term memory effects and nonlocality, its application in neural networks is often limited by a lack of physical interpretability and inconsistencies with traditional models. To address these challenges, we propose a mixed integer-fractional (MIF) gradient descent algorithm for the training of neural networks. Furthermore, a detailed convergence analysis of the proposed algorithm is provided. Finally, numerical experiments illustrate that the new gradient descent algorithm not only speeds up the convergence of the BP neural networks but also increases their classification accuracy.

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation’s environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes.

Purpose: Nighttime construction sites pose challenges for equipment detection due to insufficient lighting, with existing research often lacking applicability to nighttime environments. This paper proposes an innovative approach for excavator recognition based on computer vision technology tailored for night scenes. Design/methodology/approach: The proposed approach consists of two modules: an object detection module and an image lighting enhancement module. You Only Look Once version 7 (YOLOv7) is applied as the detection algorithm in the object detection module and integrates three distinct attention mechanisms (Squeeze-and-Excitation Network, Convolutional Block Attention Module, and efficient channel attention (ECANet)) into YOLOv7 to enhance its nighttime object detection capabilities. The Night-Enhancement (NE) algorithm is utilized in the image enhancement module to boost image brightness and improve object visibility. To validate our framework, we evaluate the model using four construction videos of nighttime scenes under different lighting conditions. Findings: Experimental results demonstrate that the NE algorithm effectively enhances image clarity in low-light environments, increasing the model’s average detection accuracy by 8.85%. Notably, the integration of the ECANet attention mechanism into YOLOv7 significantly enhances nighttime detection performance, achieving an average detection accuracy exceeding 97% and an average accuracy improvement of 8.63% on the test dataset. Despite the added attention mechanisms, YOLOv7 maintains a detection speed above 36 Frames Per Second (FPS) on a standard computer, enabling real-time detection. Furthermore, the object enhancement module effectively increases the visibility of targets in nighttime videos, thereby improving detection performance across most models. Research limitations/implications: A key limitation of this research is that while the illumination enhancement module effectively improves visibility in low-light environments, it slightly reduces image quality in well-lit conditions. Originality/value: This research presents a novel approach to nighttime excavator detection by integrating image lighting enhancement and advanced object detection algorithms.