Search

Objective Complete and transparent reporting is of critical importance for randomized controlled trials (RCTs). The present study aimed to determine the reporting quality and methodological quality of RCTs for interventions involving artificial intelligence (AI) and their protocols. Methods We searched MEDLINE (via PubMed), Embase, Web of Science, CBMdisc, Wanfang Data, and CNKI from January 1, 2016, to November 11, 2020, to collect RCTs involving AI. We also extracted the protocol of each included RCT if it could be obtained. CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) statement and Cochrane Collaboration's tool for assessing risk of bias (ROB) were used to evaluate the reporting quality and methodological quality, respectively, and SPIRIT-AI (The Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) statement was used to evaluate the reporting quality of the protocols. The associations of the reporting rate of CONSORT-AI with the publication year, journal's impact factor (IF), number of authors, sample size, and first author's country were analyzed univariately using Pearson's chi-squared test, or Fisher's exact test if the expected values in any of the cells were below 5. The compliance of the retrieved protocols to SPIRIT-AI was presented descriptively. Results Overall, 29 RCTs and three protocols were considered eligible. The CONSORT-AI items “title and abstract” and “interpretation of results” were reported by all RCTs, with the items with the lowest reporting rates being “funding” (0), “implementation” (3.5%), and “harms” (3.5%). The risk of bias was high in 13 (44.8%) RCTs and not clear in 15 (51.7%) RCTs. Only one RCT (3.5%) had a low risk of bias. The compliance was not significantly different in terms of the publication year, journal's IF, number of authors, sample size, or first author's country. Ten of the 35 SPIRIT-AI items (funding, participant timeline, allocation concealment mechanism, implementation, data management, auditing, declaration of interests, access to data, informed consent materials and biological specimens) were not reported by any of the three protocols. Conclusions The reporting and methodological quality of RCTs involving AI need to be improved. Because of the limited availability of protocols, their quality could not be fully judged. Following the CONSORT-AI and SPIRIT-AI statements and with appropriate guidance on the risk of bias when designing and reporting AI-related RCTs can promote standardization and transparency.

Macroscopic fundamental diagram (MFD) has been receiving increasing attention recently due to its potential to describe traffic dynamics and guide the design of traffic control schemes at the network level. Perimeter control and route guidance are two main MFD-based traffic control approaches. However, current MFD-based perimeter control seldom considers travelers’ route choice behavior, while MFD-based route guidance studies usually assume directly that travelers would follow the guidance and neglect the effects of traffic control. This paper aims to integrate the MFD-based perimeter control (i.e., the behavior of a system manager) and the dynamic user equilibrium based route choice behavior (i.e., the behavior of travelers) into one rigorous mathematical framework. Given a traffic network that has been divided into multiple homogeneous regions, we use MFD to describe the dynamics of each region, and use point queue model to capture the dynamics of queues formed at the boundaries. Besides, we model travelers' route choice behavior by the instantaneous dynamic user equilibrium (IDUE) principle, and design an efficient range perimeter control method from the system perspective. We model the interactions between the system manager and the travelers as a non-zero sum, non-cooperative differential game, where the system manager aims to improve the system performance while travelers try to minimize their own travel times. Meanwhile, they share the common constraints (i.e., MFD dynamics and point queue dynamics at boundaries). Mathematically, this leads to a differential complementarity system (DCS). We propose a time-stepping approach to discretize and solve the DCS model, based on which the solution existence and convergence are also established. Numerical results show that the proposed method can limit the vehicle accumulations within the efficient range of each region, which helps improve the network performance. Compared with the condition without perimeter control, the proposed control method can improve network-wide traffic performance up to 14.18%.

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.

Improving the system performance of a traffic network by dynamically controlling the routes of connected and automated vehicles (CAVs) is an appealing profit that CAVs can bring to our society. Considering that there may be a long way to achieve 100% CAV penetration, we discuss in this paper the mixed traffic flow of human driven vehicles (HDVs) and CAVs on a transportation network. We first propose a double queue (DQ) based mixed traffic flow model to describe the link dynamics as well as the flow transitions at junctions. Based on this mixed flow model, we develop a dynamic bi-level framework to capture the behavior and interaction of HDVs and CAVs. This results in an optimal control problem with equilibrium constraints (OCPEC), where HDVs’ route choice behavior is modeled at the lower level by the instantaneous dynamic user equilibrium (IDUE) principle and the CAVs’ route choice is modelled by the dynamic system optimal (DSO) principle at the upper level. We show how to discretize the OCPEC to a mathematical programming with equilibrium constraints (MPEC) and discuss its properties and solution techniques. The non-convex and non-smooth properties of the MPEC make it hard to be efficiently solved. To overcome this disadvantage, we develop a decomposition based heuristic model predictive control (HMPC) method by decomposing the original MPEC problem into two separate problems: one IDUE problem for HDVs and one DSO problem for CAVs. The experiment results show that, compared with the scenario that all vehicles are HDVs, the proposed methods can significantly improve the network performance under the mixed traffic flow of HDVs and CAVs.

Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially under the connected and automated vehicles (CAVs) environment. Traditional model-based Eco-Driving methods usually require sophisticated models and cannot deal with complex driving scenarios. This paper proposes a hybrid reinforcement learning (RL) based Eco-Driving algorithm considering both the longitudinal acceleration/deceleration and the lateral lane-changing operations. A deep deterministic policy gradient (DDPG) algorithm is designed to learn the continuous longitudinal acceleration/deceleration to reduce the fuel consumption as well as to maintain acceptable travel time. Collecting the critic’s value of each single lane from DDPG and integrating the information of adjacent lanes, a deep Q-learning algorithm is developed to make the discrete lane-changing decision. Together, a hybrid deep Q-learning and policy gradient (HDQPG) method is developed for vehicles driving along multi-lane urban signalized corridors. The method can enable the controlled vehicle to learn well-established longitudinal fuel-saving strategies, and to perform appropriate lane-changing operations at proper times to avoid congested lanes. Numerical experiments show that HDQPG can reduce fuel consumption by up to 46% with marginal or no increase of travel times.

A novel method for preparing high porosity and high strength porous YB 4 ceramics was developed combining the in-situ borothermal reaction synthesis and high temperature partial sintering. Since pores could be produced by gasses such as B 2 O 3 and CO generated in the reaction between Y 2 O 3 and B 4 C, high porosity porous YB 4 ceramics were obtained after synthesis at 1650 °C and sintering at 1780 °C for 2 h in vacuum, respectively. Using this new and simple method, porous YB 4 with controllable porosities of 60–70% and compressive strengths of 7.4–19.9 MPa were obtained depending on the green density. The phase composition, porosity, pore size distribution and compressive behavior of the porous YB 4 ceramics were systematically investigated. The effects of vacuum on synthesis of YB 4 and pore forming mechanism were also discussed in detail.