Network-level traffic signal control is an effective way to increase throughput and reduce congestion. The max-pressure algorithm, known for maximizing network throughput, has been widely studied. However, it requires accurate queue length and turn ratio measurements, and its theoretical guarantee is limited to feasible demand (i.e., demand within the capacity region) under the assumption of infinite queue capacity. To overcome these limitations, this study proposes a distributed joint admission and signal control algorithm for finite-capacity networks with both connected and regular vehicles. By using feedback from connected vehicles, the algorithm estimates queue lengths and turn ratios, reducing reliance on precise measurements. It also adaptively adjusts input flow rates to prevent oversaturation and ensure demand feasibility, even under high-demand conditions, while optimizing signal phases to ensure analytic performance. Using a Lyapunov drift optimization approach, we analytically prove a$[O(1/V), O(V)]$tradeoff between throughput and delay and establish degradation bounds that quantify the impact of queue length estimation errors on network performance. Simulations in a network with 256 origin-destination pairs show up to a 16.3% increase in throughput and reduced delays, especially in high-demand settings. The method also demonstrates strong resilience to sudden demand changes and incidents, ensuring quick recovery.

It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the limited interpretability of RL. Second, few studies have explored the cooperative safety of multi-CAV platoons, where CAVs can be coordinated to further enhance the system-level safety involving the safety of both CAVs and HDVs. Third, existing work tends to make an unrealistic assumption that the behavior of HDVs and CAVs is publicly known and rational. To bridge the research gaps, we propose a safe MARL framework for mixed-autonomy platoons. Specifically, this framework 1) characterizes cooperative safety by designing a cooperative Control Barrier Function (CBF), enabling CAVs to collaboratively improve the safety of the entire platoon, 2) provides a safety guarantee to the MARL-based controller by integrating the CBF-based safety constraints into MARL through a differentiable quadratic programming (QP) layer, and 3) incorporates a conformal prediction module that enables each CAV to estimate the unknown behaviors of the surrounding vehicles with uncertainty qualification. Simulation results show that our proposed control strategy can effectively enhance the system-level safety through CAV cooperation of a mixed-autonomy platoon with a minimal impact on control performance.

Optimizing mixed vehicle platoons, which consist of connected and automated vehicles (CAVs) with human-driven vehicles (HDVs), is a critical challenge for intelligent transportation systems. While existing predictive control methods have improved modeling accuracy and control robustness, they are often constrained by their reliance on online optimization, limiting their applicability in real-time scenarios. To address this gap, this paper proposes a Robust Explicit Data-Driven Predictive Control (REDDPC) framework designed to provide robust and real-time control for mixed vehicle platoons. The framework begins by utilizing a deep Koopman operator network to learn the nonlinear dynamics of the system. Using this learned representation, the neural network-based control policy is then optimized through backpropagation, eliminating the need for online optimization. To enhance robustness, a reachability-based safety filter is integrated with the learned control policy to dynamically adjust control inputs, ensuring platoon safety under complex conditions. Simulation and experiment results demonstrate that the proposed method achieves superior tracking performance under noise, disturbance, and attack conditions, while significantly reducing online computational time, making it highly suitable for real-world deployment.

A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing approaches often overlook the interactions between AVs and HDVs, assuming that HDV trajectories are not influenced by AV actions. To address this gap, we present a transformer-transfer learning-based interaction-aware trajectory predictor for safe motion planning in autonomous driving, focusing on a vehicle-to-vehicle (V2V) interaction scenario involving an AV and an HDV. Specifically, we construct a transformer-based interaction-aware trajectory predictor using widely available datasets of HDV trajectory data and further transfer the learned predictor using a small set of AV-HDV interaction data. Then, to better incorporate the proposed trajectory predictor into the motion planning module of AVs, we introduce an uncertainty quantification method to characterize the predictor’s errors, which are integrated into the path-planning process. Our experimental results demonstrate the value of explicitly considering interactions and handling uncertainties.

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing system constraints during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during both the training and deployment stages. The lack of safety guarantees impedes the application of RL to real-world problems. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which pose difficulties for MPC design and constraint satisfaction. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm achieves an average improvement of 10.88% in holistic energy efficiency compared to the RMPC technique, while effectively preventing inter-vehicle collisions when compared to the RL algorithm.

Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. However, existing studies on CV-based signal control share a common shortcoming in that they all ignore traffic flow estimation errors in their modeling process, which is inevitable due to the sampling observation nature of CVs. This study proposes a CV data-driven robust optimization framework for traffic signal timing, accounting for both traffic flow variability and estimation errors. First, we propose a general CV data-driven deterministic optimization model (CV-DO) that can be widely applied to various scenarios, including under-/over-saturated and fixed-/real-time signalized intersections. Then, we propose a novel CV data-driven uncertainty set of arrival rates, circumventing the error-prone estimation process and accounting for both traffic flow variability errors. Finally, a CV data-driven robust optimization model (CV-RO) is formulated to explicitly handle arrival rate uncertainties. Employing the robust counterpart approach, this robust optimization problem can be converted to deterministic mixed-integer linear programming problems that can be solved efficiently with exact solutions. The evaluation results at a real-world intersection highlight the superior performance of the CV-RO model compared to the deterministic model and traditional methods across various scenarios. At different levels of traffic flow fluctuations, CV-RO can reduce delays by 5-26% compared to CV-DO at fixed-time signalized intersections with 0.1 CV penetration rate. The results on a real-time signalized network show that CV-RO can reduce 5% delays compared to the CV-DO model and 35.5% delays compared to actuated control at a 0.3 penetration rate.

Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections. This paper proposes a bi-level control approach based on an upper-level deep reinforcement learning controller and a lower-level model predictive controller to coordinate the lane-changings of a mixed fleet of CAVs and human-driven vehicles (HVs) in weaving sections. The upper level represents a roadside controller that collects vehicular information from the entire weaving section and determines the control weights used in the lower-level controller. The lower level is implemented within each CAV, which takes the control weights from the upper-level controller and generates the acceleration and steering angle for individual CAVs based on the local situation. The lower-level controller further incorporates an HV trajectory predictor, which is capable of handling the dynamic topology of vehicles in weaving scenarios with intensive mandatory lane changes. The case study inspired by a real weaving section in Basel, Switzerland, shows that our method consistently outperforms state-of-the-art benchmarks.

In recent years, an increasing number of individuals have turned to traditional Chinese medicine diet therapy as a means to nourish their bodies and mitigate diseases. With the advent of the big data era, knowledge graphs, as powerful analysis tools, can provide more accurate and personalized dietary advice for diet therapy. However, most of the current diet therapy knowledge graphs have imperfections. To address this issue, we construct a diet therapy knowledge graph by utilizing textual data and professional books provided by the Academy of Traditional Chinese Medicine, from which we extract entities and relations. Building upon this foundation, we introduce a text representation technique predicated on contrastive learning, designed to augment the semantic richness of the knowledge graph and enhance the completion of the diet therapy knowledge graph. By conducting experiments on the diet therapy knowledge graph and public datasets, the results show that our method can capture the semantic information in the knowledge graph more efficiently compared to traditional methods. This provides new possibilities for research and practice in the field of traditional Chinese medicine diet therapy. This research opens new avenues for leveraging big data analysis in traditional Chinese medicine diet therapy.

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases’ duration. The performance of a Linear Regression (LR), Random Forest (RF), a light gradient-boosting machine (LightGBM), a bidirectional Long-Short-Term-Memory neural network (BiLSTM) and a Temporal Convolutional Network (TCOV) are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that state of the art machine learning models outperform conventional prediction methods.

Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.

Query compilation and hardware acceleration are important technologies for optimizing the performance of data processing engines. There have been many works on the exploration and adoption of these techniques in recent years. However, a number of engines still refrain from adopting them because of some reasons. One of the common reasons claims that the intricacies of these techniques make engines too complex to maintain. Another major barrier is the lack of widely accepted architectures and libraries of these techniques, which leads to the adoption often starting from scratch with lots of effort. In this paper, we propose Intel Big Data Analytic Toolkit (BDTK), an open-source C++ acceleration toolkit library for analytical data processing engines. BDTK provides lightweight, easy-to-connect, reusable components with interoperable interfaces to support query compilation and hardware accelerators. The query compilation in BDTK leverages vectorized execution and data-centric code generation to achieve high performance. BDTK could be integrated into different engines and helps them to adapt query compilation and hardware accelerators to optimize performance bottlenecks with less engineering effort.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poor-quality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actor-critic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes.

This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widely-adopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. In addition, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation. The proposed methodology is tested with two datasets: the Lankershim data set in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zürich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05 s-1. It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8 s), which is sufficient for most real-time applications.