The ecological functions of core and non-core bacteria are gradually being identified, yet little is known about their responses to environmental changes and assembly processes, especially in urban river ecosystems. Here, we investigated bacterioplankton communities over 1 year in an urban section of the Ganjiang River, China. The results revealed that the alpha- and beta-diversity of bacterioplankton communities had no significant spatial differences along the urbanization gradient, but they presented distinct seasonal variations. The bacterioplankton communities were comprised of a few core taxa (11.8%) and a large number of non-core taxa (88.2%), of which the non-core taxa were the most active component responsible for community dynamics. Most non-core taxa (76.84%) belonged to non-typical freshwater bacteria, implying that they are more likely to derive from allochthonous inputs than the core taxa. Variance partitioning analyses showed that air temperature, flow rate and water chemistry together explained 58.2 and 38.9% of the variations of the core taxa and non-core taxa, respectively. In addition, the relative importance of temperature and water chemistry on the bacterioplankton communities prevailed over that of flow rate alone. This means that deterministic processes and stochastic processes simultaneously control the bacterioplankton community assembly, with deterministic processes contributing more than stochastic processes.

Excessive pilot workload is one of the significant causes of flight accidents. The detection of flight workload can help optimize aircraft crew operation procedures, improve cockpit human–machine interface (HMIs) design, and ultimately reduce the risk of flight accidents. However, traditional detection methods often employ invasive or patch-based devices that can interfere with the pilot’s control. In addition, they generally lack real-time capabilities, while the workload of pilots actually varies continuously. Moreover, most models do not take individual physiological differences into account, leading to the poor performance of new pilots. To address these issues, this study developed a real-time pilot workload detection model based on low-interference devices, including telemetry eye trackers and a pressure-sensing seat cushion. Specifically, the Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model is proposed, combining KNN in the outer layer for identifying the physiological feature cluster with the ensemble classifier corresponding to this cluster in the inner layer. The ensemble model employs random forest, gradient boosting trees, and FCN–Transformer as base learners. It utilizes soft voting for predictions, integrating the strengths of various networks and effectively extracting the sequential features from complex data. Results show that the model achieves a detection accuracy of 82.6% on the cross-pilot testing set, with a runtime of 0.1 s, surpassing most studies that use invasive or patch-based detection devices. Additionally, the model demonstrates high accuracy across different individuals, indicating good generalization. The results are expected to improve flight safety.

Carsharing is becoming an important member of urban mobility systems. The relationship between carsharing and other travel modes, is the base for deciding the proportional role of carsharing. This article defines the relationship, competition or cooperation, based on whether carsharing strengthening or weakening the expected function of other travel modes from the transport system perspective. It proposes the relationship should be studied under the specific characteristics of carsharing trips (e.g., the spatiotemporal distribution), and establishes a method for evaluating the coordination of carsharing with other modes. A survey about carsharing trips was conducted, and valid data generated by 1,330 carsharing users were obtained. The spatiotemporal characteristics of carsharing substituting for subway, buses, taxis, private cars, and nonmotorized vehicles have been analyzed, and the substitution benefits were also evaluated. The results indicate that carsharing in Shanghai was mainly used for long-distance trips in suburbs during off-peak hours, which generally formed a cooperative relationship with public transit, taxis, and nonmotorized vehicles. Phenomenon that carsharing substituting for public transit mainly occurred in long-distance trips in suburbs, with an average 40–50 min travel time saving. Carsharing also substituted for long-distance non-motorized vehicle trips, which improved the mobility of those travelers. To further enhance the coordination, the layout of carsharing stations and operation strategies need to be optimized to reduce the carsharing travel demands during peak hours within urban areas, and to promote carsharing as a substitute for private cars.

Social media has become a valuable platform that enables public and private stakeholders to participate and interact in various transport policies. Using a network-based perspective and a case study of bike-sharing pricing strategies in China, this paper aims to quantitatively characterize the pattern and structure of multi-stakeholders engagement networks. Furthermore, this paper also empirically examines the confirmation bias that might exist among participants. Dataset on retweets from the Chinese Twitter-Sina Weibo is collected. Results reveal two types of important actors with unequal roles in terms of information diffusion: the “network root” and the “network bridge.” The former is mainly comprised of organizations and influential individuals who dominate message sharing, whereas the latter is comprised of the general public with various occupational backgrounds who control the efficiency and the scope of information spreading. The result also reveals a hierarchical structure in both networks and a community gathering like-minded individuals. The empirical result also demonstrates the existence of echo chambers in the transport participation network of governments and enterprises. Most echo chambers operate such that organizations or influential individuals amplify the views of the general public with more critical viewpoints. These findings of this study can assist transport stakeholders in crafting more sustainable strategies based on the understanding of uneven patterns in online public participation. Furthermore, this study sheds insights on how social media could be used to facilitate the collection of diverse people’s opinions and the evaluation of multi-stakeholder engagement for major transport issues.

This paper focuses on the operation changes of taxi systems with the wide-usage of e-hailing apps using the calculated taxi OD pairs data of Shanghai in 2012 and 2015 and applied an evaluation process to analyze the possible impacts from three dimensions, namely, taxi system supply, operation efficiency, and service coverage. The results reveal that the e-hailing apps have partly improved the taxi operation. On the one hand, the supply of later taxi systems has experienced an obvious drop; however, on the other hand, the existing taxi system has overall higher efficiency owing to the decreased percentage of vacant trips. The comparison of taxi trips demonstrates that the taxi system can better satisfy larger-scale and longer-time service demands than before, especially in some low-density areas and during the non-peak hours. The findings provide another perspective for decision-makers to comprehensively understand and management the taxi system or urban transport after e-hailing service.