Short-term traffic prediction plays a critical role in many important applications of intelligent transportation systems such as congestion control and smart routing, numerous methods have been proposed to address this issue the literature. However, most, if not all, them suffer from inability fully use rich information data. In paper, we present novel short-term flow approach based on dynamic tensor completion (DTC), which data are represented pattern, is able capture more than traditional...

Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous (SAV). To promote technology, it important understand which factors influence travelers’ intention use AVs and SAVs. This paper collected literature from databases Scopus, Web Science ScienceDirect, made systematic review. study aims explore determinants...

In reality, readings of sensors on highways are usually missing at various unexpected moments due to some sensor or communication errors. These values do not only influence the real-time traffic monitoring but also prevent further data mining. this paper, we propose a multi-view learning method estimate for traffic-related time series data. The model combines data-driven algorithms (long-short term memory and support vector regression) collaborative filtering techniques. It can consider...

Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based conflict point-based approaches, this paper proposes two-stage optimization method based on developed modeling approach. The first stage is timing schedule model, assigning vehicle arrival times at an intersection. Based output stage, second trajectory which gives eco-driving strategies. Moreover, rolling with variable...

The traffic-forecasting model, when considered as a system with inputs of historical and current data outputs future data, behaves in nonlinear fashion varies time day. Traffic are found to change abruptly during the transition times entering leaving peak periods. Accurate real-time models needed approximate time-variant functions between from continuous stream training data. A proposed local linear regression model was applied short-term traffic prediction. performance compared previous...

In response to the need for developing coordinated schemes of autonomous vehicles (AVs) at an intersection. This paper presents a novel coordination method intersection management in connected vehicle environment. The road network is divided into three logical sections, namely, buffer area, core area and free driving area. addition, buffer-assignment mechanism developed cooperatively assign specific crossing span individual AV guide each adjust its entry time corresponding speed A...

The machine learning-based car-following models are widely adopted to control the longitudinal movements of automated vehicles, such as Google Car and Apple Car, by mimicking human drivers' maneuver. However, like drivers, easily produce unsafe maneuvers for vehicles has low robustness, especially in uncommon situations. To improve models, this paper proposes combine learning with kinematics-based that can overcome shortcomings using an optimal combination prediction method, which is called...

The large-scale application of connected automated vehicles (CAVs) provides new opportunities and challenges for the optimization management traffic conflict zones. To improve efficiency zones reduce travel delay fuel consumption CAVs, this paper presents a two-level method scheduling trajectory planning CAVs. At first level, 0–1 mixed-integer linear program (MILP) is proposed entering scheduling. second multi-vehicle optimal control model developed based on vehicle schedule from level....

Abstract This paper proposes a deep reinforcement learning (DRL)‐based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the signal‐interference‐plus‐noise ratio‐based vehicle‐to‐vehicle is incorporated into DRL training environment reproduce realistic time–space varying information flow topologies (IFTs). A dynamic fusion mechanism designed smooth high‐jerk signal caused by IFTs....

To solve the problems of when to set up connected automated vehicles (CAVs) dedicated lanes and how many CAVs under different penetration rates CAVs, this work focuses on modeling fundamental diagram mixed traffic flow with for CAVs. Firstly, car-following modes their proportion without are analyzed. Secondly, is derived based models analyze capacity lanes. Then, relevant properties proposed proved. Finally, sensitivity related parameters (e.g., rate, time headway, free-flow speed) in...

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements communication technology computing power, real-time assessment has become feasible for enhancing traffic safety. In this study, novel approach evaluating driving interaction on freeways presented. The involves integration an model with behavior....