The vast majority of protocols for connected automated vehicles are based onstatussharing, i.e., communication of the current vehicle state among neighboring vehicles. Only recently the idea ofintentsharing has been put forward, where not only the current state, but also the vehicle intention in the near future can be communicated. In the context of Cooperative Adaptive Cruise Control (CACC), this work provides a system-theoretic framework for intent sharing through the lens of output regulation. We present analytical results showing two fundamental aspects of CACC with intent sharing: a) when vehicle-to-vehicle communication is reliable, intent sharing provides no benefits over status sharing, as both sharing paradigms result in the same protocol; b) intent sharing becomes beneficial when vehicle-to-vehicle communication is unreliable, in which case the latest communicated intent can be used to reconstruct the missing information of the neighboring vehicle in the near future. Together with theoretical analysis, numerical validations with synthetic and real-world data are provided, where the benefits of CACC with the proposed implementation of intent sharing are shown against several state-of-the-art CACC protocols.

Multi-source data enhances demand prediction performance by learning from multiple transport modes simultaneously. However, existing multimodal demand forecasting methods often require direct sharing of raw data, which can be infeasible or at least very difficult, due to privacy concerns or practical constraints posed by ownership of data from different institutions. This study proposes a Multimodal Transport Demand Forecasting model via Federated Learning (FL) to improve forecasting accuracy without the need for direct data sharing. In the model, a processing center is introduced to handle parameters of forecasting models trained by each private dataset, where the exact dataset does not have to be shared and sensitive private information cannot be identified. Specifically, a Fine-grained Graph Convolution Recurrent Network (F-GCRN) is designed to capture spatiotemporal correlations of each dataset, with stronger capabilities to handle dynamic latent dependencies among different demand patterns than existing multimodal demand forecasting models. The processing center distinguishes the importance of parameters sent by different modes based on the Attentive Federated Learning mechanism and returns the processing parameters to each institution. Each institution predicts the demand with the returned parameters. Evaluations on three real-world transport datasets demonstrate that the proposed model outperforms several baselines and state-of-the-art models. Overall, this study addresses the research gap of enhancing multimodal demand forecasting without the dependence on direct data sharing and illustrates that knowledge sharing via parameters sharing by FL can improve multimodal transport demand prediction.

This paper investigates the potential of improving the overall traffic and energy efficiency by properly controlling a proportion of controllable connected and autonomous vehicles (CAVs) in a mixed traffic corridor. Specifically, we develop a control framework that optimizes controllable CAV trajectories taking into account other vehicles for simultaneously improving traffic throughput and reducing the total energy consumption of all vehicles. The property of the control framework is firstly analytically examined in a simplified and tractable scenario where a human-driven vehicle (HV) follows a CAV. We found that the optimal acceleration is larger if one emphasizes more on improving travel distance within the optimization horizon, or smaller when one emphasizes more on saving energy. The continuous-time optimization model formulation is then discretized, which is solved for real-time application in a model predictive control (MPC) fashion. In numerical studies, the proposed method is tested in various scenarios, e.g., with/without an intersection, under different proportions of controllable CAVs, possible vehicle permutations, and varying overall traffic intensities. Numerical results show that the normalized energy consumption can be reduced by up to 45% and the average travel time reduced by 65%, showing a significant improvement in the road throughput. Notably, even with a limited number of controllable CAVs, the proposed method can achieve a promising performance, e.g., about 20% controllable CAVs can achieve half the benefits of a fully controllable CAV environment.

Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. We use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes “all-direction” lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time.

Accurate demand forecasting of different public transport modes (e.g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies significantly, which makes it hard to predict the demand of the modes with insufficient knowledge and sparse station distribution (i.e., station-sparse mode). Intuitively, different public transit modes may exhibit shared demand patterns temporally and spatially in a city. As such, we propose to enhance the demand prediction of station-sparse modes with the data from station-intensive mode and design a Memory-Augmented Multi-task Re current Network (MATURE) to derive the transferable demand patterns from each mode and boost the prediction of station-sparse modes through adapting the relevant patterns from the station-intensive mode. Specifically, MATURE comprises three components: 1) a memory-augmented recurrent network for strengthening the ability to capture the long-short term information and storing temporal knowledge of each transit mode; 2) a knowledge adaption module to adapt the relevant knowledge from a station-intensive source to station-sparse sources; 3) a multi-task learning framework to incorporate all the information and forecast the demand of multiple modes jointly. The experimental results on a real-world dataset covering four public transport modes demonstrate that our model can promote the demand forecasting performance for the station-sparse modes.

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.

Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.