For many intelligent transportation applications, traffic congestion prediction is quite essential. If traffic congestion on the road ahead can be accurately and promptly predicted, and routes can be planned reasonably based on the prediction results, traffic congestion can be effectively alleviated. Aiming at the spatio‐temporal correlation and evolution characteristics of traffic flow data, the Conv–BiLSTM module comprising a convolutional neural network (CNN) and a bidirectional long short‐term memory (BiLSTM) is proposed, considering the spatio‐temporal features. Firstly, the obtained traffic speed data is folded according to spatio‐temporal features, and a three‐dimensional matrix is constructed as the input of the prediction network module. After the spatial features are extracted by the CNN, the temporal features and alignment features are extracted by the BiLSTM, followed by which the prediction results are obtained as an output. Prediction and evaluation experiments on the traffic data of the highway in Shanghai prove that the traffic congestion state predicted by this method is largely consistent with the actual state. The results demonstrate that the proposed method has a higher prediction accuracy compared with the conventional and state‐of‐the‐art methods and is an efficient method of traffic congestion prediction.

As a green travel mode, public bikes have positive implications in terms of reducing emissions. Based on the life cycle theory, we calculate the carbon emissions generated and reduced over the entire life cycle of public bikes.The calculation shows that when the average daily turnover rate of public bikes is 1.874 times/ bike, the average daily travel distance is 2.150 km, and the damage rate increases by 2.5% per month, each public bike needs approximately 7 months to reach the carbon balance. After the carbon emission balance is reached, the use of public bikes causes a net reduction in carbon emissions. However, the carbon emissions once again exceed the emission reductions after approximately 29 months of using public bikes. Furthermore, the carbon balance of the 186 stations used in Wenling City is studied.Based on the conclusions obtained, some policy recommendations are made to help public bikes achieve real emissions reduction.

Given the vital role of public transportation in major cities, understanding the influence mechanism of bikesharing use on public transportation is necessary. In this study, we adopt the propensity score matching method to analyze the influence mechanism of bikesharing on the use of public transportation based on a data set in Shanghai. We find no significant influence of bikesharing on the use-frequency of public transportation, but a significant influence on the use-duration of public transportation. A grouping model is established based on gender, physical condition, private bike ownership, private car ownership, private electric bike ownership, and educational background. It is revealed that the use of bikesharing by the group with a private bike or a private electric bike, the group without a bachelor’s degree, the group in physical condition under sub-healthy may increase the use-duration of public transportation.

Predicting the crowd flow in various areas of the city is of strategic importance for traffic control and public safety. In recent years, crowd flow prediction based on spatio-temporal data are gaining more and more attention. In order to better understand the current status of spatio-temporal crowd flow prediction research and global cooperation, we use scientometric methods, social network analysis, and Stochastic Actor-oriented Model (SAOM) to visualize and analyze the source journals, hotspot co-occurrence networks, and national cooperation networks based on the relevant literature included in the Web of Science database, so as to explore the current status and characteristics of related academic research. In addition, this paper constructs a technical framework based on Subject–Action–Object (SAO) structural information, and draws a technical roadmap containing five levels: data, technology, influence factors, objectives, and applications. The visual mapping of the evolutionary path of technology topics based on SAO structure information can assist in analyzing the evolutionary path of technology topics and their development trends. This study contributes to the existing knowledge system of spatio-temporal crowd flow prediction by proposing a new, integrated, and holistic knowledge map.