Over the past decade, the rapid development of e-commerce and express industries in China has resulted in huge environmental costs. Compared with manufacturing industries, the values of green innovation are less recognized in logistics industries. To promote the green practices in logistic enterprises, it is imperative to have a thorough understanding of the determinants of green innovation adoption. To this end, this paper performs an empirical investigation into the intentions to adopt green innovation from 196 Chinese express companies. The determinant variables were constructed from the perspective of technology characteristics (perceived green usefulness and perceived integration ease of use), stakeholder pressure (government, customer, and platform pressures), and social influence. Then, a 20-item scale was designed based on the literature review and expert opinions. The results revealed the significant positive effects of technology characteristics and social influence on the intentions to adopt green innovation. Meanwhile, only the platform pressure was significant with the adopting intentions among the variables from stakeholder pressure. Moreover, variables from technology characteristics were found to have meditation effects between social influence and adopting intentions. Based on the findings, theoretical and practical implications are proposed to promote the green and sustainable development of express companies in China.

The primary objective of this study is to predict the short‐term demand of free‐floating bike sharing (FFBS) using deep learning approach. The FFBS trip data in Shanghai city are collected from the Mobike Company. Other datasets such as weather data and air quality data are also collected. The spatiotemporal patterns of FFBS demand indicates that the weekday rides exhibit an obvious commuting pattern while the weekend rides are usually involved with various trip purposes. Then, a hybrid deep learning neural network (HDL‐net) is developed to predict the short‐term FFBS demand for different time intervals including 15, 20 and 30 min. The proposed HDL‐net exhibits better performance on morning peak than the evening peak and non‐peak hours for all the three time intervals. Moreover, five benchmark methods are also used to compare with the proposed HDL‐net. The results suggest that the proposed hybrid deep learning framework outperform the benchmarks in the prediction performance for all three time intervals. The results of this study could provide insightful suggestions for transportation authorities to develop effective rebalancing strategies and bike lanes planning schemes to promote the service level of cycling in an urban city.

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.