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With the advent of the information age and rapid population growth, the urban transportation environment is deteriorating. Travel-route planning is a key issue in modern sustainable transportation systems. When conducting route planning, identifying the spatiotemporal disparities between planned routes and the routes chosen by actual drivers, as well as their underlying reasons, is an important method for optimizing route planning. In this study, we explore the spatial–temporal differences between planned routes and actual routes by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car-hailing trajectories. By applying an improved Hidden Markov Model (HMM) map-matching algorithm to the original trajectories, we obtain the Origin-Destination (OD) matrix of vehicle travel and its corresponding actual routes, as well as the planned routes generated by the A* routing algorithm. We utilize the Jaccard index to quantify the similarity between actual and planned routes for the same OD pairs. The causes of PRADs are detected and further analyzed from the perspective of traffic conditions. By analyzing ride-hailing trajectories provided by DiDi, we examine the route behavior of drivers in Wuhan city on weekdays and weekends and discuss the relationship between traffic conditions and PRADs. The results indicate that the average accuracy of GNSS trajectory point-to-road map-matching reaches 88.83%, which is approximately 12% higher than the accuracy achieved by the HMM map-matching method proposed by Hu et al. Furthermore, the analysis of PRAD causes reveals that PRADs occurring on weekdays account for approximately 65% and are significantly associated with traffic congestion and accidents during that time. The findings of this study provide insights for future research on sustainable transportation systems and contribute to the development of improved route-planning strategies. [ABSTRACT FROM AUTHOR]

The Zhaoshan long-baseline Atom Interferometer Gravitation Antenna (ZAIGA) is a new type of underground laser-linked interferometer facility, and is currently under construction. It is in the 200-m-on-average underground of a mountain named Zhaoshan which is about 80 km southeast to Wuhan. ZAIGA will be equipped with long-baseline atom interferometers, high-precision atom clocks, and large-scale gyros. ZAIGA facility will take an equilateral triangle configuration with two 1-km-apart atom interferometers in each arm, a 300-m vertical tunnel with atom fountain and atom clocks mounted, and a tracking-and-ranging 1-km-arm-length prototype with lattice optical clocks linked by locked lasers. The ZAIGA facility will be used for experimental research on gravitation and related problems including gravitational wave detection, high-precision test of the equivalence principle of micro-particles, clock-based gravitational red-shift measurement, rotation measurement and gravitomagnetic effect. [ABSTRACT FROM AUTHOR]

Long-standing attention has been given to urban vitality and its association with the built environment (BE). However, the multiplicity and complex impacts of BE factors that shape urban vitality patterns have not been fully explored. For this purpose, multisource data from 1025 communities in Wuhan, China, were combined to explore the BE vitality nexus. A deep learning method was explored to segment street-view images, on which a composite indicator of urban vitality was developed with social media data. Then, six dimensions of BE factors, neighbourhood attributes, urban form and function, landscape, location, and street configuration, were incorporated into a spatial regression model to systematically examine the composite influences. The results show that population density, community age, open space, the sidewalk ratio, streetlights, shopping and leisure density, integration, and proximity to transportation are positive factors that induce urban vitality, whereas the effects of road density, proximity to parks, and green space have the opposite results. This study contributes to an improved understanding of the BE nexus. Managerial implications for mediating the relationship between planning policies and urban design strategies for the optimization of resource allocation and promotion of sustainable development are discussed. • Explore the BE-vitality nexus by integrating advanced methods and multi-dimensional BE factors. • Combine a rich variety of big-data to clarify the composite effects of BE factors on urban vitality. • Compose a composite indicator to measure urban vitality by joining street movements and social media data. • Integrate spatial regression and GWR models to reduce the bias caused by spatial autocorrelation and heterogeneity. [ABSTRACT FROM AUTHOR]