The development of electric vertical takeoff and landing (eVTOL) aircrafts promotes the prosperity of Urban Air Mobility (UAM). While numerous efforts have been made on UAM, little was focusing on the integrated operation of UAM and ground transportation system. This paper thus investigated how to alleviate traffic congestion on an arterial road with a bottleneck via using eVTOL aircrafts to transfer passengers upstream the bottleneck to downstream. The location, opening and closing time of the vertiports and the transfer ratio of the passengers are optimized with the objective of minimizing the average monetary cost, which is an integrated evaluation value converted by the electricity consumption, travel time and vertiport operating cost. Based on this, an optimization model is constructed and Dividing RECTangles algorithm is adopted to solve it using the simulation module as a subroutine. Simulation results show that the proposed method is capable of reducing average monetary cost and average travel time. Moreover, due to the differences in salaries and electricity prices, the departure vertiport in U.S. case should be positioned as close to the upstream as possible, while in China it is the opposite. More importantly, we found that the successful implementation of the proposed method requires the ride-sharing service provider to arrange the travel plan in a unified manner because of the higher cost of transferred passengers. This research pioneers a novel UAM-based method to alleviate traffic congestion on an arterial road, setting the stage for the full exploitation of UAM capabilities to reduce ground traffic congestion.

Most existing reinforcement learning-based traffic signal control approaches overlook vehicle-specific information in the state representation. This study addresses this gap by integrating real-time driving style information into the deep reinforcement learning (DRL) framework. We introduce a model-based framework that captures real-time driving styles and converts them into Intelligent Driver Model (IDM) parameters. Our proposed method demonstrates superior performance across various reinforcement algorithms and traffic flow scenarios, with statistical tests confirming a significant reduction in average queue length. The contributions of this paper can be summarized as follows: 1) proposing a model-based method for real-time driving style recognition, significantly reducing the requirements for trajectory data duration and computational resources, and 2) proposing a new state variable called transformed occupancy (o*) that allows the DRL-based traffic signal controller to be trained with driving style information, thereby enhancing the performance of the traffic signal control system. The proposed framework is so flexible that other car-following models, machine learning algorithms, and various downstream tasks can be incorporated.

In the literature, various models of cooperative adaptive cruise control (CACC) vehicles have been proposed and studied. To design efficient CACC systems, analytical stability analysis is needed to understand the stability properties of CACC models. Recently, a series of experiments on commercial adaptive cruise control (ACC) vehicles have shown that these vehicles can be string stable only to the detriment of a low capacity. As an attempt to solve this issue, this study investigates the stability of multi-anticipative CACC vehicles with Vehicle to Vehicle (V2V) communication delay using the Lyapunov function method and the Lyapunov functional method, and the following results have been obtained: (i) From the methodological perspective, the Lyapunov function method overcomes the difficulty of the transfer function method to derive the stability condition of the multi-anticipative model. The Lyapunov functional method can predict with good accuracy the impact of communication delay. (ii) The multi-anticipative model successfully ensures both traffic stability and high capacity. (iii) The critical wavelength at which traffic becomes unstable follows a power law independently of the model’s parameters when the sensitivity to acceleration is not considered. (iv) The realistic communication delay has only a slight impact on the stability of the CACC system. Our study might represent a reliable reference to improve the stability of CACC vehicles while preserving a high throughput.

This paper presents an improved Intelligent Driving Model (Sigmoid-IDM) to address the issues of excessive acceleration in traffic oscillation and following failure in free flow. The Sigmoid-IDM utilizes a Sigmoid function to enhance the starting-following characteristics, improve the output strategy of the spacing term, and stabilize the steady-state velocity in free flow. Furthermore, the model’s asymmetry is enhanced by introducing cautious following distance, caution driving factor, and segmentation function. The anti-interference ability of the Sigmoid-IDM is demonstrated through local stability and string stability analyses. The model parameters were calibrated using the Hefei dataset and High D data across various traffic scenarios: start-up, stop-go, and free-flow. The Sigmoid-IDM outperforms the IDM by significantly reducing errors and enhancing performance metrics. Specifically, in start-up and stop-go scenarios, the Sigmoid-IDM achieves a 28.57% and 19.04% reduction in Root Mean Square Error (RMSE) for acceleration, respectively. Comfort error during start-up is also lowered by 18.1%. In the free-flow scenario, the RMSE for spacing and velocity decreases by 15.64% and 16.36%, respectively. Furthermore, the Sigmoid-IDM demonstrates a more pronounced asymmetric behavior than the IDM, offering a more accurate representation of human drivers’ following patterns. The model’s efficacy was further validated through circular road simulation and Simulink-Carsim co-simulation, confirming its ability to accurately simulate the transition from synchronized flow to wide moving jams under variable parameters, as well as the traceability of its trajectory planning.

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic assumption, or the ACC system modeled is inaccurate. This paper proposes a general hierarchical control system to model ACC systems with several assumptions based on the deficiencies above. Moreover, a field experiment was conducted, and the corresponding experimental data was used to verify the proposed hierarchical control system and assumptions. In addition, string stability is explored along with sensitivity analyses of control parameters based on an example under the constant time gap policy. The results show that different upper-level controller parameters have different delays, where the delay of the speed is negligible; the introduction of actuator delay and lag in the lower-level controller can significantly improve the model goodness of fit. Furthermore, optimizing the delay and lag in the lower-level controller can significantly enhance the string stability of ACCs than optimizing the control parameters.

Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental data, two new lower-level control models are developed and compared with two widely used ones. The comparison results show that the proposed models outperform the two previous models in capturing the observed actual acceleration, especially the troughs of the acceleration time series. Furthermore, theoretical analysis indicates that comparing with the proposed models, the two previous models significantly overestimate the stability region of the traffic flow of the AVs and the capacity of stable traffic flow. Our study is expected to further shed light on the importance of accurate lower-level control modeling.

Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC. Note to Practitioners—This article presents a new car-following strategy, which avoids the impact of deep deterministic policy gradient (DDPG) performance degradation on the system. In the proposed strategy, DDPG is replaced with cooperative adaptive cruise control (CACC) when the performance of DDPG is worse than that of CACC. Meanwhile, a switching rule is designed to guarantee that the change rate of acceleration is smaller than the threshold. Simulation results show that the performance of hybrid car-following strategy has been improved compared with that of only using CACC or DDPG. Moreover, the proposed strategy has the advantages of low computational burden, high real-time performance, and good scalability.

Vehicles stopping at signalized intersections during a red light is one of the main causes of traffic oscillations. Recently, deep reinforcement learning (DRL) methods have been applied to connected and automated vehicles (CAVs) traffic to reduce the traffic oscillation at signalized intersections. However, these methods do not perform well for mixed traffic flow, including human vehicles (HVs) and CAVs, especially when the CAV rate is low. We found that this was because they did not take into account the HVs stopping at a red light and causing oscillations. If this oscillation is ignored during the speed regulation, CAVs may conflict with the oscillation wave in the future, forcing a sudden and significant speed reduction and triggering the so-called “trajectory jerking” phenomenon. In order to address this problem, this study proposes a trajectory prediction-based DRL method. By introducing the prediction of the downstream vehicle trajectory into the design of the reward function, the leading CAV will adjust its speed in advance to avoid the future oscillation wave caused by HV’s stopping during the red phase. Simulation tests on various penetration rates of CAVs are conducted for the mixed traffic environment to evaluate the performance of the proposed method. The results show that the proposed method has two advantages. Even with low CAV penetration, the oscillations are suppressed remarkably well. And fuel consumption is also significantly reduced. This research provides a new idea to suppress traffic oscillations in a mixed traffic environment.

This paper aims to design speed advisory profile for connected vehicles (CVs) at an isolated signalized intersection in a mixed traffic flow of CVs and human driven vehicles (HDVs). To consider the uncertainty of HDVs, we propose the concept of$\alpha $-percentile trajectory of HDVs. We calculate the advisory speed of a CV that follows a HDV, assuming that the HDV moves along its$\alpha $-percentile trajectory. We use dynamic programming to solve the optimization problem. Simulation experiments show that, roughly speaking, the benefits of fuel consumption and travel time would increase with the increase of inflow rate and market penetration rate (MPR) of CVs. The maximum benefits would be achieved at the smallest value of$\alpha $when the traffic flow is undersaturated. However, when the traffic flow is oversaturated, the maximum benefits would be achieved at intermediate value of$\alpha $provided the MPR of CVs is not large. With the further increase of the MPR of CVs, the benefits would become not so sensitive to$\alpha $in the range$\alpha \le 40$%, but significantly decrease with the increase of$\alpha $when$\alpha >40$%. Finally, the impact of driver heterogeneity, inflow heterogeneity, tracking error and delay has been investigated.