Work Performed:
In WP1, we developed a novel method termed 'Model Controlled Prediction' that can improve the efficiency and accuracy of electrochemical models [1]. In WP2, we crafted a method known as 'Deep Adaptive Control' which entailed the design of DRL-based adaptive vehicle trajectory control algorithms for different risk levels [7]. We further studied the enhancement of state representation in Multi-Agent RL for Platoon-Following Models, contributing significant insights to this field [8]. In WP3, we developed a personalized recommendation system. This system leveraged users' historical records to predict travel destinations, provided optimal route recommendations to users based on energy consumption and personal preferences [4].
Exploitation and Dissemination of Results:
I hosted a special session as the co-chair at the IEEE ITSC (
https://2023.ieee-itsc.org/special-sessions/(si apre in una nuova finestra)). This session served as a platform to showcase our project's accomplishments to the broader community. Additionally, I hold the position of Associate Editor for IEEE TIV and JICV, and serve as a Young Editor for the Innovation (Cell Press’ flagship general journal, Impact factor = 32.1) and IEEE/CAA JAS. I have been actively promoting our project's outcomes in these journals' activities, including paper submissions, social media dissemination via the journals' Twitter accounts, and during seminars.
[1] Li, S., Liu, Y.*, & Qu, X. (2022). Model controlled prediction: A reciprocal alternative of model predictive control. IEEE/CAA JAS, 9(6), 1107-1110.
[2] Liu, Y., et al. (2022). How machine learning informs ride-hailing services: A survey. COMMTR, 2, 100075.
[3] Liu, Y., et al. (2021). DeepTSP: Deep traffic state prediction model based on large-scale empirical data. COMMTR, 1, 100012.
[4] Wu, F., Lyu, C., & Liu, Y.* (2022). A personalized recommendation system for multi-modal transportation systems. Multimodal Transportation, 1(2), 100016.
[5] Jia, R., Chamoun, R., Wallenbring, A., Advand, M., Yu, S., Liu, Y., & Gao, K. (2023). A spatio-temporal deep learning model for short-term bike-sharing demand prediction. ERA, 31(2), 1031-1047.
[6] Lin, P., He, C., Zhong, L., Pei, M., Zhou, C., & Liu, Y. (2023). Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort. ERA, 31(4), 2315-2336.
Under review
[7] He, Y., Liu, Y.*, & Qu, X., Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels. Submitted to IEEE TIV.
[8] Lin, H., Liu, Y.*, et al., Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models. Submitted to IEEE TIV.
