AGNP:Network-wide short-term probabilistic traffic speed prediction and imputation

The data-driven Intelligent Transportation System(ITS)provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems.Hence,network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network.Abundant research works have adopted various approaches for traffic prediction and imputation.However,previous methods ignore the reliability analysis of the predicted/imputed traffic information.Thus,this study originally proposes an attentive graph neural process(AGNP)method for network-level short-term traffic speed prediction and imputation,simultaneously considering reliability.Firstly,the Gaussian process(GP)is used to model the observed traffic speed state.Such a stochastic process is further learned by the proposed AGNP method,which is utilized for inferring the congestion state on the remaining unobserved road segments.Data from a transportation network in Anhui Province,China,is used to conduct three experiments with increasing missing data ratio for model testing.Based on comparisons against other machine learning models,the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance.With the probabilistic confidence provided by the AGNP,reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.

Traffic signal coordination control for arterials with dedicated CAV lanes

Purpose–This study aims to make full use of the advantages of connected and autonomous vehicles(CAVs)and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.Design/methodology/approach–The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming.CAV non-stop constraints are proposed to adapt to the characteristics of CAVs.As it is a continuous problem,various situations that CAVs arrive at intersections are analyzed.The rules are discovered to simplify the problem by discretization method.Findings–A case study is conducted via SUMO traffic simulation program.The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper.At the same time,the progression efficiency of regular vehicles is not affected significantly.It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics.Originality/value–To the best of the authors’knowledge,this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.

UI layers merger: merging UI layers via visual learning and boundary prior

With the fast-growing graphical user interface(GUI)development workload in the Internet industry,some work attempted to generate maintainable front-end code from GUI screenshots.It can be more suitable for using user interface(UI)design drafts that contain UI metadata.However,fragmented layers inevitably appear in the UI design drafts,which greatly reduces the quality of the generated code.None of the existing automated GUI techniques detects and merges the fragmented layers to improve the accessibility of generated code.In this paper,we propose UI layers merger(UILM),a vision-based method that can automatically detect and merge fragmented layers into UI components.Our UILM contains the merging area detector(MAD)and a layer merging algorithm.The MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components.Then,the layer merging algorithm can search for the associated layers within the components’boundaries and merge them into a whole.We present a dynamic data augmentation approach to boost the performance of MAD.We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM.Experimental results show that the proposed method outperforms the best baseline regarding merging area detection and achieves decent layer merging accuracy.A user study on a real application also confirms the effectiveness of our UILM.

Correlation between travel experiences and post-COVID outbound tourism intention:a case study from China

The COVID-19 pandemic has devastated global tourism and recovery is proceeding very slowly.For many countries,tourism served as a major economic sector,so investigating how to recover is essential.As China was the largest source of outbound travelers before the outbreak,study of the factors influencing Chinese intentions to travel overseas in the post-COVID era is revealing.In Apr.2022,among seven provinces(or cities)with the most outbound tourists from 2019 to 2021,2450 individuals responded to a questionnaire on daily mobility,tourism experiences,and the shifts due to the pandemic.Light gradient boosting machine(LightGBM),a robust ensemble learning method,was adopted to quantify and visualize the impact of explanatory factors on outbound travel intention.In addition,the Optuna mechanism and Shapley additive explanation(SHAP)instruments were employed for tuning hyperparameters and interpreting results,respectively.Findings suggest neither one-day nor multi-day tours have resumed to pre-COVID levels.Higher frequency of multi-day tours with further destinations,less car utilization in daily shopping trips,and moderate pandemic restrictions can boost the intention to travel abroad.The concerns and desires of different age groups for overseas travel need different responses.This study reveals the factors affecting Chinese outbound travel intentions and provides suggestions for the recovery of tourism in the post-COVID period.