The increasingly wide usage of smart infrastructure and location-aware terminals has helped increase the availability of trajectory data with rich spatiotemporal information. The development of data mining and analysis methods has allowed researchers to use these trajectory datasets to identify urban reality (e.g., citizens’ collective behavior) in order to solve urban problems in transportation, environment, public security, etc. However, existing studies in this field have been relatively isolated, and an integrated and comprehensive review is lacking the problems that have been tackled, methods that have been tested, and services that have been generated from existing research. In this paper, we first discuss the relationships among the prevailing trajectory mining methods and then, classify the applications of trajectory data into three major groups: social dynamics, traffic dynamics, and operational dynamics. Finally, we briefly discuss the services that can be developed from studies in this field. Practical implications are also delivered for participants in trajectory data mining. With a focus on relevance and association, our review is aimed at inspiring researchers to identify gaps among tested methods and guiding data analysts and planners to select the most suitable methods for specific problems.

The paradigms of taxis and ride-hailing, the two major players in the personal mobility market, are compared systematically and empirically in a unified spatial–temporal context. Supported by real field data from Xiamen, China, this research proposes a three-fold analytical framework to compare their mobilities, including (1) the spatial distributions of departures and arrivals by rank–size and odds ratio analysis, (2) the statistical characteristics of trip distances by spatial statistics and considering distance-decay effect, and (3) the meta-patterns inherent in the mobility processes by nonnegative tensor factorization. Our findings suggest that taxis and ride-hailing services share similar spatial patterns in terms of travel demand, but taxi demand heterogenizes more quickly with changes in population density. Additionally, the relative balance between the taxi industry and ride-hailing services shows opposite trends inside and outside Xiamen Island. Although the trip distances have similar statistical properties, the spatial distribution of the median trip distances reflects different urban structures. The meta-patterns detected from the origin–destination-time system via tensor factorization suggest that taxi mobilities feature exclusive nighttime intensities, whereas ride-hailing exhibits more prominent morning peaks on weekdays. Although ride-hailing contributes significantly to cross–strait interactions during daytime, there is a lack of efficient services to maintain such interactions at night.

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces.

Global practices in brownfield redevelopment have yielded valuable legislative and policy frameworks. However, existing studies lack targeted identification and database construction methodologies for Third Line Construction cities in China, which feature clustered industrial heritage and fragmented land rights, leading to a disconnect between general brownfield research and the actual governance needs of such typical industrial cities. This study addresses these gaps by developing a multi-source data integrated technical framework for systematic brownfield identification and standardized database construction, taking Mianyang—a typical Third Line Construction city—as the case. The framework prioritizes planning data, takes Point of Interest (POI) data as the core supplement, and innovatively integrates satellite imagery and industrial statistics via cross-year POI superimposition-deduplication, coordinate reverse geocoding verification, and ArcGIS-based polygon boundary delineation, effectively compensating for traditional method deficiencies and realizing standardized attribute-spatial data binding. Applying this framework, 599 brownfield sites (9234.83 ha) in Mianyang were identified and classified into four functional categories. Spatial statistical analysis reveals their concentration in Fucheng District and beyond the Second Ring Road, dominance of large-scale plots, and functional differentiation with nearly half remaining polluting industrial land. This study establishes a standardized, replicable process for Third Line Construction cities and a quantitative paradigm for brownfield precise governance.

The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.