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Dr. Ruimin Ke
Department of Civil Engineering, University of Texas at El Paso, El Paso, TX 79968, USA

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Research Keywords & Expertise

0 Artificial Intelligence
0 EDGE COMPUTING
0 intelligent transportation systems
0 intelligent vehicle
0 Smart infrastructure

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Artificial Intelligence
EDGE COMPUTING
intelligent transportation systems

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Journal article
Published: 01 October 2020 in Journal of Transportation Engineering, Part A: Systems
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Vertical curve features on interstate highways greatly affect traffic operations and vehicle performance and, thus, could have an impact on the occurrence of traffic crashes. Most studies to date only considered linear relationships. Though some researchers did consider nonlinearity, the preassumed data distribution may not fit the true distribution perfectly. Thus, the primary objective of this study is to develop a nonparametric algorithm to evaluate the nonlinear correlation between vertical curve features and crash frequency on interstate highways based on a random forest (RF) algorithm. Elevation data along interstate centerlines were extracted from Google Earth for two interstates in Washington State, and 5-year crash data were collected to estimate RF models for crash count prediction. A random effect negative binomial (RENB) model is employed to evaluate predictive performance. Analysis of the variables’ importance shows that the proposed RF models captured the nonlinear correlation between crash count and annual average daily traffic (AADT), the elevation and grade of road segments, median lane width, left shoulder width, ratio of horizontal curve, the standard deviation of grade in 1- and 2-mi road segments, the standard deviation of elevation in 1- and 2-mi road segments, and lane width. Other variables, e.g., right shoulder width and the number of lanes on the highway were also important in the proposed RF models. By better capturing the nonlinearity, the proposed RF model outperformed the baseline model in terms of the predictive performance measurements. The findings of this research can serve to facilitate improvements in highway geometric design and recommend countermeasures to reduce the crash count on interstate highways.

ACS Style

Ziyuan Pu; Zhibin Li; Ruimin Ke; Xuedong Hua; Yinhai Wang. Evaluating the Nonlinear Correlation between Vertical Curve Features and Crash Frequency on Highways Using Random Forests. Journal of Transportation Engineering, Part A: Systems 2020, 146, 04020115 .

AMA Style

Ziyuan Pu, Zhibin Li, Ruimin Ke, Xuedong Hua, Yinhai Wang. Evaluating the Nonlinear Correlation between Vertical Curve Features and Crash Frequency on Highways Using Random Forests. Journal of Transportation Engineering, Part A: Systems. 2020; 146 (10):04020115.

Chicago/Turabian Style

Ziyuan Pu; Zhibin Li; Ruimin Ke; Xuedong Hua; Yinhai Wang. 2020. "Evaluating the Nonlinear Correlation between Vertical Curve Features and Crash Frequency on Highways Using Random Forests." Journal of Transportation Engineering, Part A: Systems 146, no. 10: 04020115.

Journal article
Published: 10 July 2020 in Transportation Research Part C: Emerging Technologies
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Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in terms of the predictive power of spatial–temporal data and the capability of handling missing data. In this paper, we focus on RNN-based models and attempt to reformulate the way to incorporate RNN and its variants into traffic prediction models. A stacked bidirectional and unidirectional LSTM network architecture (SBU-LSTM) is proposed to assist the design of neural network structures for traffic state forecasting. As a key component of the architecture, the bidirectional LSTM (BDLSM) is exploited to capture the forward and backward temporal dependencies in spatiotemporal data. To deal with missing values in spatial–temporal data, we also propose a data imputation mechanism in the LSTM structure (LSTM-I) by designing an imputation unit to infer missing values and assist traffic prediction. The bidirectional version of LSTM-I is incorporated in the SBU-LSTM architecture. Two real-world network-wide traffic state datasets are used to conduct experiments and published to facilitate further traffic prediction research. The prediction performance of multiple types of multi-layer LSTM or BDLSTM models is evaluated. Experimental results indicate that the proposed SBU-LSTM architecture, especially the two-layer BDLSTM network, can achieve superior performance for the network-wide traffic prediction in both accuracy and robustness. Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model’s input data contains different patterns of missing values.

ACS Style

Zhiyong Cui; Ruimin Ke; Ziyuan Pu; Yinhai Wang. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies 2020, 118, 102674 .

AMA Style

Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies. 2020; 118 ():102674.

Chicago/Turabian Style

Zhiyong Cui; Ruimin Ke; Ziyuan Pu; Yinhai Wang. 2020. "Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values." Transportation Research Part C: Emerging Technologies 118, no. : 102674.

Journal article
Published: 07 July 2020 in IEEE Sensors Journal
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Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.

ACS Style

Xinqiang Chen; Shubo Wu; Chaojian Shi; Yanguo Huang; Yongsheng Yang; Ruimin Ke; Jiansen Zhao. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. IEEE Sensors Journal 2020, 20, 14317 -14328.

AMA Style

Xinqiang Chen, Shubo Wu, Chaojian Shi, Yanguo Huang, Yongsheng Yang, Ruimin Ke, Jiansen Zhao. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. IEEE Sensors Journal. 2020; 20 (23):14317-14328.

Chicago/Turabian Style

Xinqiang Chen; Shubo Wu; Chaojian Shi; Yanguo Huang; Yongsheng Yang; Ruimin Ke; Jiansen Zhao. 2020. "Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison." IEEE Sensors Journal 20, no. 23: 14317-14328.

Journal article
Published: 03 July 2020 in IEEE Transactions on Intelligent Transportation Systems
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In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for traffic monitoring and data collection on highways due to its advantage of low cost, high resolution, good flexibility, and wide spatial coverage. Extracting high-resolution vehicle trajectory data from aerial videos taken by a UAV flying over target highway segment becomes a critical research task for traffic flow modeling and analysis. This study aims at proposing a novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos. The method starts by developing an ensemble detector to detect vehicles in the target region. Then, the kernelized correlation filter is applied to track vehicles fast and accurately. After that, a mapping algorithm is proposed to transform vehicle positions from the Cartesian coordinates in image to the Frenet coordinates to extract raw vehicle trajectories along the roadway curves. The data denoising is then performed using a wavelet transform to eliminate the biased vehicle trajectory positions. Our method is tested on two aerial videos taken on different urban expressway segments in both peak and non-peak hours on weekdays. The extracted vehicle trajectories are compared with manual calibrated data to testify the framework performance. The experimental results show that the proposed method successfully extracts vehicle trajectories with a high accuracy: the measurement error of Mean Squared Deviation is 2.301 m, the Root-mean-square deviation is 0.175 m, and the Pearson correlation coefficient is 0.999. The video and trajectory data in this study are publicly accessible for serving as benchmark at https://seutraffic.com.

ACS Style

Xinqiang Chen; Zhibin Li; Yongsheng Yang; Lei Qi; Ruimin Ke. High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 3190 -3202.

AMA Style

Xinqiang Chen, Zhibin Li, Yongsheng Yang, Lei Qi, Ruimin Ke. High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (5):3190-3202.

Chicago/Turabian Style

Xinqiang Chen; Zhibin Li; Yongsheng Yang; Lei Qi; Ruimin Ke. 2020. "High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos." IEEE Transactions on Intelligent Transportation Systems 22, no. 5: 3190-3202.

Journal article
Published: 12 June 2020 in Transportation Research Part C: Emerging Technologies
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A model used for velocity control during car following is proposed based on reinforcement learning (RL). To optimize driving performance, a reward function is developed by referencing human driving data and combining driving features related to safety, efficiency, and comfort. With the developed reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. To avoid potential unsafe actions, the proposed RL model is incorporated with a collision avoidance strategy for safety checks. The safety check strategy is used during both model training and testing phases, which results in faster convergence and zero collisions. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the proposed model. The performance of the proposed model is evaluated by the comparison with empirical NGSIM data and with adaptive cruise control (ACC) algorithm implemented through model predictive control (MPC). The experimental results show that the proposed model demonstrates the capability of safe, efficient, and comfortable velocity control and outperforms human drivers in that it 1) has larger TTC values than those of human drivers, 2) can maintain efficient and safe headways around 1.2s, and 3) can follow the lead vehicle comfortably with smooth acceleration (jerk value is only a third of that of human drivers). Compared with the MPC-based ACC algorithm, the proposed model has better performance in terms of safety, comfort, and especially running speed during testing (more than 200 times faster). The results indicate that the proposed approach could contribute to the development of better autonomous driving systems. Source code of this paper can be found at https://github.com/MeixinZhu/Velocity_control.

ACS Style

Meixin Zhu; Yinhai Wang; Ziyuan Pu; Jingyun Hu; Xuesong Wang; Ruimin Ke. Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transportation Research Part C: Emerging Technologies 2020, 117, 102662 .

AMA Style

Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke. Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transportation Research Part C: Emerging Technologies. 2020; 117 ():102662.

Chicago/Turabian Style

Meixin Zhu; Yinhai Wang; Ziyuan Pu; Jingyun Hu; Xuesong Wang; Ruimin Ke. 2020. "Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving." Transportation Research Part C: Emerging Technologies 117, no. : 102662.

Research article
Published: 16 April 2020 in IET Intelligent Transport Systems
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Unmanned aerial vehicle (UAV) is at the heart of modern traffic sensing research due to its advantages of low cost, high flexibility, and wide view range over traditional traffic sensors. Recently, increasing efforts in UAV-based traffic sensing have been made, and great progress has been achieved on the estimation of aggregated macroscopic traffic parameters. Compared to aggregated macroscopic traffic data, there has been extensive attention on higher-resolution traffic data such as microscopic traffic parameters and lane-level macroscopic traffic parameters since they can help deeply understand traffic patterns and individual vehicle behaviours. However, little existing research can automatically estimate microscopic traffic parameters and lane-level macroscopic traffic parameters using UAV videos with a moving background. In this study, an advanced framework is proposed to bridge the gap. Specifically, three functional modules consisting of multiple processing streams and the interconnections among them are carefully designed with the consideration of UAV video features and traffic flow characteristics. Experimental results on real-world UAV video data demonstrate promising performances of the framework in microscopic and lane-level macroscopic traffic parameters estimation. This research pushes off the boundaries of the applicability of UAVs and has an enormous potential to support advanced traffic sensing and management.

ACS Style

Ruimin Ke; Shuo Feng; Zhiyong Cui; Yinhai Wang. Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video. IET Intelligent Transport Systems 2020, 14, 724 -734.

AMA Style

Ruimin Ke, Shuo Feng, Zhiyong Cui, Yinhai Wang. Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video. IET Intelligent Transport Systems. 2020; 14 (7):724-734.

Chicago/Turabian Style

Ruimin Ke; Shuo Feng; Zhiyong Cui; Yinhai Wang. 2020. "Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video." IET Intelligent Transport Systems 14, no. 7: 724-734.

Journal article
Published: 08 April 2020 in IEEE Transactions on Intelligent Transportation Systems
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Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

ACS Style

Ruimin Ke; Yifan Zhuang; Ziyuan Pu; Yinhai Wang. A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 4962 -4974.

AMA Style

Ruimin Ke, Yifan Zhuang, Ziyuan Pu, Yinhai Wang. A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):4962-4974.

Chicago/Turabian Style

Ruimin Ke; Yifan Zhuang; Ziyuan Pu; Yinhai Wang. 2020. "A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 4962-4974.

Preprint
Published: 01 January 2020
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Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing the data partially or wholly on the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, which is a key component of Smart City. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed on both the edge and the server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system can be a solid foundation for future applications of intelligent transportation systems.

ACS Style

Ruimin Ke; Yifan Zhuang; Ziyuan Pu; Yinhai Wang. A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices. 2020, 1 .

AMA Style

Ruimin Ke, Yifan Zhuang, Ziyuan Pu, Yinhai Wang. A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices. . 2020; ():1.

Chicago/Turabian Style

Ruimin Ke; Yifan Zhuang; Ziyuan Pu; Yinhai Wang. 2020. "A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices." , no. : 1.

Journal article
Published: 28 November 2019 in IEEE Transactions on Intelligent Transportation Systems
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Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

ACS Style

Zhiyong Cui; Kristian Henrickson; Ruimin Ke; Yinhai Wang. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 4883 -4894.

AMA Style

Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (11):4883-4894.

Chicago/Turabian Style

Zhiyong Cui; Kristian Henrickson; Ruimin Ke; Yinhai Wang. 2019. "Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting." IEEE Transactions on Intelligent Transportation Systems 21, no. 11: 4883-4894.

Research article
Published: 26 November 2018 in IET Intelligent Transport Systems
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The quality of traffic data is crucial for modern transportation planning and operations. However, data could be missing for various reasons. Hence, the data imputation approaches which aim at predicting/replacing the missing data or bad data have been considered very important. The traditional traffic data imputation approaches mainly focus on using different probability models or regression methods to impute data, and they only take limited temporal or spatial information as inputs. Thus, they are not very accurate especially for data with a high missing ratio. To overcome the weaknesses of previous approaches, this study proposes an innovative traffic data imputation method, which first transforms the raw data into spatial-temporal images and then implements a deep-learning method on the images. The key idea of this approach is developing a convolutional neural network (CNN)-based context encoder to reconstruct the complete image from the missing source. To the best of the authors’ knowledge, this is the first time a CNN method has been incorporated for traffic data imputation. Experiments are conducted on three months of data from 256 loop detectors. Through comparison with two state-of-the-art approaches, the results indicate that this new approach increases the imputation accuracy greatly and has a stable error distribution.

ACS Style

Yifan Zhuang; Ruimin Ke; Yinhai Wang. Innovative method for traffic data imputation based on convolutional neural network. IET Intelligent Transport Systems 2018, 13, 605 -613.

AMA Style

Yifan Zhuang, Ruimin Ke, Yinhai Wang. Innovative method for traffic data imputation based on convolutional neural network. IET Intelligent Transport Systems. 2018; 13 (4):605-613.

Chicago/Turabian Style

Yifan Zhuang; Ruimin Ke; Yinhai Wang. 2018. "Innovative method for traffic data imputation based on convolutional neural network." IET Intelligent Transport Systems 13, no. 4: 605-613.

Journal article
Published: 01 September 2018 in Journal of Transportation Engineering, Part A: Systems
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As the amount of traffic congestion continues to grow, pinpointing freeway bottleneck locations and quantifying their impacts are crucial activities for traffic management and control. Among the previous bottleneck identification methods, limitations still exist. The first key limitation is that they cannot determine precise breakdown durations at a bottleneck in an objective manner. Second, the input data often needs to be aggregated in an effort to ensure better robustness to noise, which will significantly reduce the time resolution. Wavelet transform, as a powerful and efficient data-processing tool, has already been implemented in some transportation application scenarios to much benefit. However, there is still a wide gap between existing preliminary explorations of wavelet analysis in transportation research and a completely automatic bottleneck identification framework. This paper addresses several key issues in existing bottleneck identification approaches and also fills a gap in transportation-related wavelet applications. The experimental results demonstrate that the proposed method is able to locate the most severe bottlenecks and comprehensively quantify their impacts.

ACS Style

Ruimin Ke; Ziqiang Zeng; Ziyuan Pu; Yinhai Wang. New Framework for Automatic Identification and Quantification of Freeway Bottlenecks Based on Wavelet Analysis. Journal of Transportation Engineering, Part A: Systems 2018, 144, 04018044 .

AMA Style

Ruimin Ke, Ziqiang Zeng, Ziyuan Pu, Yinhai Wang. New Framework for Automatic Identification and Quantification of Freeway Bottlenecks Based on Wavelet Analysis. Journal of Transportation Engineering, Part A: Systems. 2018; 144 (9):04018044.

Chicago/Turabian Style

Ruimin Ke; Ziqiang Zeng; Ziyuan Pu; Yinhai Wang. 2018. "New Framework for Automatic Identification and Quantification of Freeway Bottlenecks Based on Wavelet Analysis." Journal of Transportation Engineering, Part A: Systems 144, no. 9: 04018044.

Journal article
Published: 07 March 2018 in IEEE Transactions on Intelligent Transportation Systems
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Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.

ACS Style

Ruimin Ke; Zhibin Li; Jinjun Tang; Zewen Pan; Yinhai Wang. Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 54 -64.

AMA Style

Ruimin Ke, Zhibin Li, Jinjun Tang, Zewen Pan, Yinhai Wang. Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (1):54-64.

Chicago/Turabian Style

Ruimin Ke; Zhibin Li; Jinjun Tang; Zewen Pan; Yinhai Wang. 2018. "Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow." IEEE Transactions on Intelligent Transportation Systems 20, no. 1: 54-64.

Research article
Published: 03 January 2018 in PLOS ONE
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Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

ACS Style

Wenhui Zhang; Yongmin Su; Ruimin Ke; Xinqiang Chen. Evaluating the influential priority of the factors on insurance loss of public transit. PLOS ONE 2018, 13, e0190103 .

AMA Style

Wenhui Zhang, Yongmin Su, Ruimin Ke, Xinqiang Chen. Evaluating the influential priority of the factors on insurance loss of public transit. PLOS ONE. 2018; 13 (1):e0190103.

Chicago/Turabian Style

Wenhui Zhang; Yongmin Su; Ruimin Ke; Xinqiang Chen. 2018. "Evaluating the influential priority of the factors on insurance loss of public transit." PLOS ONE 13, no. 1: e0190103.

Journal article
Published: 01 January 2018 in Expert Systems with Applications
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ACS Style

Jinjun Tang; Fang Liu; Wenhui Zhang; Ruimin Ke; Yajie Zou. Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications 2018, 91, 452 -463.

AMA Style

Jinjun Tang, Fang Liu, Wenhui Zhang, Ruimin Ke, Yajie Zou. Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications. 2018; 91 ():452-463.

Chicago/Turabian Style

Jinjun Tang; Fang Liu; Wenhui Zhang; Ruimin Ke; Yajie Zou. 2018. "Lane-changes prediction based on adaptive fuzzy neural network." Expert Systems with Applications 91, no. : 452-463.

Journal article
Published: 17 August 2016 in IEEE Transactions on Intelligent Transportation Systems
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Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade-Lucas-Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.

ACS Style

Ruimin Ke; Zhibin Li; Sung Kim; John Ash; Zhiyong Cui; Yinhai Wang. Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos. IEEE Transactions on Intelligent Transportation Systems 2016, 18, 890 -901.

AMA Style

Ruimin Ke, Zhibin Li, Sung Kim, John Ash, Zhiyong Cui, Yinhai Wang. Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos. IEEE Transactions on Intelligent Transportation Systems. 2016; 18 (4):890-901.

Chicago/Turabian Style

Ruimin Ke; Zhibin Li; Sung Kim; John Ash; Zhiyong Cui; Yinhai Wang. 2016. "Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos." IEEE Transactions on Intelligent Transportation Systems 18, no. 4: 890-901.

Conference paper
Published: 01 October 2015 in 2015 IEEE First International Smart Cities Conference (ISC2)
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Existing studies have extensively used temporal-spatial data to mine the mobility patterns of different kinds of travelers. Smart Card Data (SCD) collected by the Automated Fare Collection (AFC) systems can reflect a general view of the mobility pattern of the whole bus and metro riders in urban area. Most existing work focusing on mobility pattern usually ignore a special group of people who travel in abnormal patterns or mechanisms. In this paper, we focus on the evolution extreme transit behaviors of travelers in urban area by using SCD in 2010 and 2014. We have several aspects of descriptive statistics of the SCD with a view to better understanding the dynamic process and evolution of the extreme transit behavior. By combining the SCD's temporal information with the amount of travel behavior, we also propose a concept of Extreme Index (EI) based on the mixture Gaussian model to depict the extreme level of the passengers' travel pattern. According to our analysis, the normal transit behavior of the two years have nearly the same temporal distribution. Although the EI models of the two years have similar distributions, the EI model of 2010 with two peaks is more scattered than that of 2014, which has only one peak. The EI model, which assigns an EI attribute for each SCD, can be applied in further analysis of urban transit or passengers' behavior.

ACS Style

Zhiyong Cui; Ying Long; Ruimin Ke; Yinhai Wang. Characterizing evolution of extreme public transit behavior using smart card data. 2015 IEEE First International Smart Cities Conference (ISC2) 2015, 1 -6.

AMA Style

Zhiyong Cui, Ying Long, Ruimin Ke, Yinhai Wang. Characterizing evolution of extreme public transit behavior using smart card data. 2015 IEEE First International Smart Cities Conference (ISC2). 2015; ():1-6.

Chicago/Turabian Style

Zhiyong Cui; Ying Long; Ruimin Ke; Yinhai Wang. 2015. "Characterizing evolution of extreme public transit behavior using smart card data." 2015 IEEE First International Smart Cities Conference (ISC2) , no. : 1-6.