In recent years, many governments and companies have gradually launched robotaxi projects to help make transportation systems smarter, improve travel efficiency, and reduce travel costs. Robotaxi is a new mode of travel that replaces human driving with machines, freeing up social labour and enriching people’s travel choices. This study employs the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the influencing factors of users’ adoption and usage of robotaxis in China to facilitate the broader integration of robotaxis into urban transportation systems. This study surveyed the preferences of 2048 respondents and analysed the data through structural equation modelling. The results indicate that performance expectancy, hedonic motivation, and price value are the factors influencing users’ behavioural intentions, while effort expectancy and social influence affect use behaviour. In contrast, habit is an important factor that affect both behavioural intention and actual use behaviour. Based on the findings, we have proposed practical strategies to improve robotaxi services and updated the UTAUT2 model in the context of robotaxi. We suggest that robotaxi operators can promote user acceptance and use by reducing the difficulty of use, improving the cost performance and the ride experience, and making appropriate publicity and guidance.

The Ti₃C₂T_x film with metallic conductivity and high pseudo-capacitance holds profound promise in flexible high-rate supercapacitors. However, the restacking of Ti₃C₂T_x sheets hinders ion access to thick film electrodes. Herein, a mild yet green route has been developed to partially oxidize Ti₃C₂T_x to TiO₂/Ti₃C₂T_x by introducing O₂ molecules during refluxing the Ti₃C₂T_x suspension. The subsequent etching away of these TiO₂ nanoparticles by HF leaves behind numerous in-plane nanopores on the Ti₃C₂T_x sheets. Electrochemical impedance spectroscopy shows that longer oxidation time of 40 min yields holey Ti₃C₂T_x (H-Ti₃C₂T_x) with a much shorter relax time constant of 0.85 s at electrode thickness of 25 µm, which is 89 times smaller than that of the pristineTi₃C₂T_x film (75.58 s). Meanwhile, H-Ti₃C₂T_x film with 25 min oxidation exhibits less-dependent capacitive performance in film thickness range of 10–84 µm (1.63–6.41 mg cm⁻²) and maintains around 60% capacitance as the current density increases from 1 to 50 A g⁻¹. The findings clearly demonstrate that in-plane nanopores not only provide more electrochemically active sites, but also offer numerous pathways for rapid ion impregnation across the thick Ti₃C₂T_x film. The method reported herein would pave way for fabricating porous MXene materials toward high-rate flexible supercapacitor applications.

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods motivates us to explore what intrinsic properties/mechanisms facilitate the prediction of temporal networks. We use interpretable learning algorithms, Lasso Regression and Random Forest, to predict, based on the current activities (i.e., connected or not) of all links, the activity of each link at the next time step. From the coefficients learned from each algorithm, we construct the prediction backbone network that presents the influence of all links in determining each links future activity. Analysis of the backbone, its relation to the link activity time series and to the time aggregated network reflects which properties of temporal networks are captured by the learning algorithms. Via six real-world contact networks, we find that the next step activity of a particular link is mainly influenced by (a) its current activity and (b) links strongly correlated in the time series to that particular link and close in distance (in hops) in the aggregated network.