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Dr. Yang Zhou
University of Wisconsin Madison

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0 Machine Learning and Applications
0 traffic flow theory
0 control theory and Application
0 Connected and Automated Vehicles
0 Intelligent Transport Systems (ITS)

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Connected and Automated Vehicles
control theory and Application

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Journal article
Published: 26 July 2021 in Physica A: Statistical Mechanics and its Applications
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Trajectory data serving as an essential data-source, has been widely applied in traffic flow analysis, traffic prediction and transportation management. In real situations, trajectory data is often corrupted with noises, which may introduce estimation bias and control inefficiency to intelligent transportation systems. This paper presents a novel trajectory reconstruction method which is generic for both highway and urban arterial trajectories. The reconstruction method establishes an Empirical Mode Decomposition (EMD) based Butterworth low-pass filter framework to filter the noises and simultaneously maintain physical integrity. The two-stage framework firstly applies the EMD to decompose the original trajectories into components, multiple intrinsic mode functions (IMFs), to find out the main components of different temporal-frequency characteristics. Based on that, an optimal Butterworth-filter is applied on the lower order IMFs to filter the acceleration of an unexpected high-frequency range. To test the effectiveness of our proposed method, multiple resource data-sets are applied. As results indicated that our proposed reconstruction method performs well in terms of physical trajectories integrity, high-frequency noise removal, and measurement error rejection with minimum signal distortion. Further, our method efficiently produces speed and acceleration with higher quality compared with the state-of-the-art methods.

ACS Style

Shuoxuan Dong; Yang Zhou; Tianyi Chen; Shen Li; Qiantong Gao; Bin Ran. An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method. Physica A: Statistical Mechanics and its Applications 2021, 583, 126295 .

AMA Style

Shuoxuan Dong, Yang Zhou, Tianyi Chen, Shen Li, Qiantong Gao, Bin Ran. An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method. Physica A: Statistical Mechanics and its Applications. 2021; 583 ():126295.

Chicago/Turabian Style

Shuoxuan Dong; Yang Zhou; Tianyi Chen; Shen Li; Qiantong Gao; Bin Ran. 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method." Physica A: Statistical Mechanics and its Applications 583, no. : 126295.

Journal article
Published: 01 March 2021 in Transportation Research Part C: Emerging Technologies
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This paper presents an analytic optimal control method for the virtually coupled train set (VCTS) in high-speed railway, aiming at maintaining consistent speed and safe spacing among trains in the VCTS. The proposed control strategy focuses on both local and string stability under variant maneuvers in the high-speed scenarios. Specifically, a state-space model is firstly formulated to describe the virtually coupled train dynamics, based on which an optimal control formulation is then constructed considering constraints of safe spacing, operation limits and train dynamic performance. To solve the proposed constrained optimal control problem, an analytical algorithm is given based on Pontryagin’s maximum principle. Further, local and string stability are analyzed, and sufficient conditions of stability are mathematically derived to guarantee stable control for both homogeneous and heterogenous VCTS. Numerical simulations were conducted to verify the correctness of derived sufficient stability conditions and the effectiveness of the proposed control strategy under variant maneuvers and disturbances.

ACS Style

Yafei Liu; Yang Zhou; Shuai Su; Jing Xun; Tao Tang. An analytical optimal control approach for virtually coupled high-speed trains with local and string stability. Transportation Research Part C: Emerging Technologies 2021, 125, 102886 .

AMA Style

Yafei Liu, Yang Zhou, Shuai Su, Jing Xun, Tao Tang. An analytical optimal control approach for virtually coupled high-speed trains with local and string stability. Transportation Research Part C: Emerging Technologies. 2021; 125 ():102886.

Chicago/Turabian Style

Yafei Liu; Yang Zhou; Shuai Su; Jing Xun; Tao Tang. 2021. "An analytical optimal control approach for virtually coupled high-speed trains with local and string stability." Transportation Research Part C: Emerging Technologies 125, no. : 102886.

Journal article
Published: 01 February 2021 in Transportation Research Part C: Emerging Technologies
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This paper presents a “cooperative vehicle sorting” strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic A* algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step. To improve the algorithm running time and address multiple shortest paths, a distributed stochastic A* algorithm (DSA*) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost). Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA* method. The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA*. In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system.

ACS Style

Jiaming Wu; Soyoung Ahn; Yang Zhou; Pan Liu; Xiaobo Qu. The cooperative sorting strategy for connected and automated vehicle platoons. Transportation Research Part C: Emerging Technologies 2021, 123, 102986 .

AMA Style

Jiaming Wu, Soyoung Ahn, Yang Zhou, Pan Liu, Xiaobo Qu. The cooperative sorting strategy for connected and automated vehicle platoons. Transportation Research Part C: Emerging Technologies. 2021; 123 ():102986.

Chicago/Turabian Style

Jiaming Wu; Soyoung Ahn; Yang Zhou; Pan Liu; Xiaobo Qu. 2021. "The cooperative sorting strategy for connected and automated vehicle platoons." Transportation Research Part C: Emerging Technologies 123, no. : 102986.

Journal article
Published: 11 December 2020 in Transportation Research Part C: Emerging Technologies
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The present study conducted an operational performance analysis of the contraflow left-turn lane (CLL) design considering the influence of the upstream signalized intersection. The arrival distribution was generated using a platoon dispersion model. A stationary condition was defined, in which the performance of the CLL design remained stable in any stationary cycle. It has proved that the CLL system always converges to the stationary condition after a few cycles if the arrival distribution is fixed. In stationary cycles, the CLL design generates either recurrent and constant residual queues or no queues, depending on the arrival distributions of left-turning vehicles. Based on the stationary condition, analytical models were developed to estimate the operational performance for left-turns at signalized intersections with the CLL design. The results show that both the arrival pattern and the length of the contraflow lane can significantly influence the operational performance of the CLL design. The residual queues in the stationary condition could increase control delay significantly, leading to an overlong delay of the left-turning vehicles if the contraflow lane was not carefully designed. To this end, an empirical optimization method was proposed to minimize the control delay by optimizing the length of contraflow lanes and the offset between adjacent intersections. The research results can be directly employed by traffic engineers to optimize the CLL design and to estimate the operational performance of the signalized intersections with the CLL design.

ACS Style

Jiaming Wu; Pan Liu; Yang Zhou; Hao Yu. Stationary condition based performance analysis of the contraflow left-turn lane design considering the influence of the upstream intersection. Transportation Research Part C: Emerging Technologies 2020, 122, 102919 .

AMA Style

Jiaming Wu, Pan Liu, Yang Zhou, Hao Yu. Stationary condition based performance analysis of the contraflow left-turn lane design considering the influence of the upstream intersection. Transportation Research Part C: Emerging Technologies. 2020; 122 ():102919.

Chicago/Turabian Style

Jiaming Wu; Pan Liu; Yang Zhou; Hao Yu. 2020. "Stationary condition based performance analysis of the contraflow left-turn lane design considering the influence of the upstream intersection." Transportation Research Part C: Emerging Technologies 122, no. : 102919.

Journal article
Published: 01 December 2020 in IEEE Transactions on Intelligent Transportation Systems
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Car-following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car-following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car-following models, we proposed a new trajectory generation approach to generate platoon level trajectories given the first leading vehicle's trajectory. In this article, we discussed the temporal and spatial error propagation issue for the traditional approach by a car following block diagram representation. Based on the analysis, we pointed out that error comes from the training method and the model structure. In order to fix that, we adopt two improvements on the basis of the traditional LSTM-based car-following model. We utilized a scheduled sampling technique during the training process to solve the error propagation in the temporal dimension. Furthermore, we developed a unidirectional interconnected LSTM model structure to extract trajectories features from the perspective of the platoon. As indicated by the systematic empirical experiments, the proposed novel structure could efficiently reduce the temporal-spatial error propagation. Compared with the traditional LSTM-based car-following model, the proposed model has almost 40% less error. The findings will benefit the design and analysis of micro-simulation for platoon-level car-following models.

ACS Style

Yangxin Lin; Ping Wang; Yang Zhou; Fan Ding; Chen Wang; Huachun Tan. Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -11.

AMA Style

Yangxin Lin, Ping Wang, Yang Zhou, Fan Ding, Chen Wang, Huachun Tan. Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-11.

Chicago/Turabian Style

Yangxin Lin; Ping Wang; Yang Zhou; Fan Ding; Chen Wang; Huachun Tan. 2020. "Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.

Journal article
Published: 28 November 2020 in Sustainability
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With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

ACS Style

Fan Ding; Jiwan Jiang; Yang Zhou; Ran Yi; Huachun Tan. Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon. Sustainability 2020, 12, 9955 .

AMA Style

Fan Ding, Jiwan Jiang, Yang Zhou, Ran Yi, Huachun Tan. Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon. Sustainability. 2020; 12 (23):9955.

Chicago/Turabian Style

Fan Ding; Jiwan Jiang; Yang Zhou; Ran Yi; Huachun Tan. 2020. "Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon." Sustainability 12, no. 23: 9955.

Journal article
Published: 29 September 2020 in IEEE Intelligent Transportation Systems Magazine
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ACS Style

Xiasen Wang; Yang Zhou; Don MacKenzie; Fan Ding. Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network. IEEE Intelligent Transportation Systems Magazine 2020, 1 .

AMA Style

Xiasen Wang, Yang Zhou, Don MacKenzie, Fan Ding. Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network. IEEE Intelligent Transportation Systems Magazine. 2020; (99):1.

Chicago/Turabian Style

Xiasen Wang; Yang Zhou; Don MacKenzie; Fan Ding. 2020. "Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network." IEEE Intelligent Transportation Systems Magazine , no. 99: 1.

Journal article
Published: 10 August 2020 in IEEE Access
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This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers’ risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers’ preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified.

ACS Style

Jiwan Jiang; Fan Ding; Yang Zhou; Jiaming Wu; Huachun Tan. A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control. IEEE Access 2020, 8, 145056 -145066.

AMA Style

Jiwan Jiang, Fan Ding, Yang Zhou, Jiaming Wu, Huachun Tan. A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control. IEEE Access. 2020; 8 ():145056-145066.

Chicago/Turabian Style

Jiwan Jiang; Fan Ding; Yang Zhou; Jiaming Wu; Huachun Tan. 2020. "A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control." IEEE Access 8, no. : 145056-145066.

Journal article
Published: 16 May 2019 in Transportation Research Part B: Methodological
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This paper presents a robust car-following control strategy under uncertainty for connected and automated vehicles (CAVs). The proposed control is designed as a decentralized linear feedback and feedforward controller with a focus on robust local and string stability under (i) time-varying uncertain vehicle dynamics and (ii) time-varying uncertain communication delay. The former uncertainty is incorporated into the general longitudinal vehicle dynamics (GLVD) equation that regulates the difference between the desired acceleration (prescribed by the control model) and the actual acceleration by compensating for nonlinear vehicle dynamics (e.g., due to aerodynamic drag force). The latter uncertainty is incorporated into acceleration information received from the vehicle immediately ahead. As a primary contribution, this study derives and proves (i) a sufficient and necessary condition for local stability and (ii) sufficient conditions for robust string stability in the frequency domain using the Laplacian transformation. Simulation experiments verify the correctness of the mathematical proofs and demonstrate that the proposed control is effective for ensuring stability against uncertainties.

ACS Style

Yang Zhou; Soyoung Ahn. Robust local and string stability for a decentralized car following control strategy for connected automated vehicles. Transportation Research Part B: Methodological 2019, 125, 175 -196.

AMA Style

Yang Zhou, Soyoung Ahn. Robust local and string stability for a decentralized car following control strategy for connected automated vehicles. Transportation Research Part B: Methodological. 2019; 125 ():175-196.

Chicago/Turabian Style

Yang Zhou; Soyoung Ahn. 2019. "Robust local and string stability for a decentralized car following control strategy for connected automated vehicles." Transportation Research Part B: Methodological 125, no. : 175-196.

Journal article
Published: 20 January 2019 in Sensors
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Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

ACS Style

Fan Ding; Xiaoxuan Chen; Shanglu He; Guangming Shou; Zhen Zhang; Yang Zhou. Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test. Sensors 2019, 19, 409 .

AMA Style

Fan Ding, Xiaoxuan Chen, Shanglu He, Guangming Shou, Zhen Zhang, Yang Zhou. Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test. Sensors. 2019; 19 (2):409.

Chicago/Turabian Style

Fan Ding; Xiaoxuan Chen; Shanglu He; Guangming Shou; Zhen Zhang; Yang Zhou. 2019. "Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test." Sensors 19, no. 2: 409.

Conference paper
Published: 02 July 2018 in CICTP 2018
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Short-term traffic forecasting plays an essential role in proactive traffic management, operation, and control. In this study, long-run equilibrium relationships between traffic speeds was collected and used for short-term forecasting based on a vector error correction (VEC) model. In the proposed forecasting model, the VEC model can capture both the long-run equilibrium relationship and the short-run joint behavior of differenced traffic speed series. The long-run equilibrium relationship of three levels of traffic speed was captured by the cointegration equation. The short-run joint behavior of differenced traffic speed series was characterized by a vector autoregressive (VAR) process. Forecasting performance of the proposed model was evaluated and compared with the VAR model and the univariate autoregressive integrated moving average (ARIMA) model. Results showed that the proposed VEC model produced more accurate and reliable forecasts than VAR and ARIMA.

ACS Style

Qinghui Nie; Yang Zhou; Jingxin Xia; Shejun Deng; Xiaoxuan Chen. Employment of Long-Run Equilibrium Relationships in Multivariate Short-Term Traffic Speed Forecasting. CICTP 2018 2018, 1 .

AMA Style

Qinghui Nie, Yang Zhou, Jingxin Xia, Shejun Deng, Xiaoxuan Chen. Employment of Long-Run Equilibrium Relationships in Multivariate Short-Term Traffic Speed Forecasting. CICTP 2018. 2018; ():1.

Chicago/Turabian Style

Qinghui Nie; Yang Zhou; Jingxin Xia; Shejun Deng; Xiaoxuan Chen. 2018. "Employment of Long-Run Equilibrium Relationships in Multivariate Short-Term Traffic Speed Forecasting." CICTP 2018 , no. : 1.