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Pattern clustering is an effective method for exploring the regularities of human mobility scheduling and daily activities. There still remains the challenge of measuring the similarity between pairs of activity patterns that are in the form of categorical time series sequences. Existing studies measured similarity using binary vector or edit distance, but these methods were insufficient to characterize routine arrangement and time scheduling of daily activities. To address this issue, we cluster daily activities and identify regular patterns using a Markov-chain-based mixture model, which captures features of activity scheduling by Markov transition matrix as well as measures similarity with probability distribution. Logistic regression models are further built to test hypothetical relationships between activity patterns and socio-demographic characteristics. Results show there are three main human activity patterns in terms of daily routine arrangement and activity scheduling: working-education-oriented (WE-oriented), recreation-shopping-oriented (RS-oriented), and schooling-drop-off/pick-up-oriented (SDP-oriented). People in the WE-oriented pattern mainly engage with regular home-based commuting trips, while people in the RS-oriented pattern are involved in home-based shopping and entertainment events. With regard to the SDP-oriented pattern, people plan their trips under a restricted scheduling of schooling pickup/drop-off. Each pattern clearly indicates long-term regularity of daily activity behaviors and corresponds to specific socio-demographics. Distinguishing three categories of residents with distinct life styles, this research would help accommodate travel demand from different groups of people in urban transportation planning.
Yang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society 2021, 24, 102 -112.
AMA StyleYang Zhou, Quan Yuan, Chao Yang, Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society. 2021; 24 ():102-112.
Chicago/Turabian StyleYang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. 2021. "Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model." Travel Behaviour and Society 24, no. : 102-112.
Customized path-based speed prediction is an eventful tool for congestion avoidance, route optimization and travel time prediction for navigation apps, cab-hailing companies and autonomous vehicles. Traditionally, the speed prediction algorithms are based on road segments and can only support several main roads. Path-based speed prediction is very challenging since the speed is always changing in different path locations and is jointly affected by lots of complicated factors. This article presents a novel deep learning framework for customized path-based speed prediction. A Path-based Speed Prediction Neural Network (PSPNN) is designed to achieve speed predictions for a given path and attributes information. A hierarchical Convolutional Neural Network (CNN) and deep Bidirectional Long Short-Term Memory (Bi-LSTM) structure for different kinds of feature extraction are applied for multiple levels: the path cell, sub-path and the whole path. The method narrows down the prediction unit from road segments to customized path cells (mean length: 59.52m) and achieves a mean absolute error (MAE) of 1.94 m/s and Mean Absolute Percentage Error (MAPE) of 18.14%, showing the potential of serving rigorous data-driven applications. So far, PSPNN is the first made-to-order path-based speed prediction algorithm and can help both travelers and managers to obtain large-scale bespoke paths speed information in advance.
Hao Yang; Chenxi Liu; Meixin Zhu; Xuegang Ban; Yinhai Wang. How Fast You Will Drive? Predicting Speed of Customized Paths By Deep Neural Network. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -11.
AMA StyleHao Yang, Chenxi Liu, Meixin Zhu, Xuegang Ban, Yinhai Wang. How Fast You Will Drive? Predicting Speed of Customized Paths By Deep Neural Network. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-11.
Chicago/Turabian StyleHao Yang; Chenxi Liu; Meixin Zhu; Xuegang Ban; Yinhai Wang. 2021. "How Fast You Will Drive? Predicting Speed of Customized Paths By Deep Neural Network." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.
The customized bus (CB) is an innovative type of transit service that can provide a personalized efficient transit services for passengers and environmental friendliness and congestion alleviation in metropolitan areas. This work develops an integrated optimization method for CB stop deployment, route design, and timetable development optimization problems while meeting travel demands as much as possible to obtain system-optimal CB service plans. Through the perspective of space-time network, the CB service design problem (CBSDP) is formulated as an integrated optimization model with the objectives of maximizing passenger accessibility and minimizing operating cost. An inconvenience index of passengers is introduced in the problem to measure the service quality, and the total number of stops for all involved CB routes is set as one of the objectives to optimize the total cost of the CB system. A heuristic approach is applied to generate efficient solutions for the CBSDP. Two types of instance, namely, a numerical experiment and a real-world instance, are implemented to demonstrate the performance of the proposed method. We also conduct a series of sensitive analyses to explore the influences of various parameters on the CB system for capturing the interaction among stops, routes, and timetables. Final results show that the CB plans obtained by the proposed method can provide efficient services by balancing passenger convenience and operating cost.
Xi Chen; Yinhai Wang; Xiaolei Ma. Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -16.
AMA StyleXi Chen, Yinhai Wang, Xiaolei Ma. Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-16.
Chicago/Turabian StyleXi Chen; Yinhai Wang; Xiaolei Ma. 2021. "Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-16.
Increasing concerns about traffic congestion, road safety, greenhouse gas emissions and energy consumption have resulted in people making different transportation choices, which in turn are affecting future mobility patterns. As “sustainable mobility” has become an increasingly popular idea, the focus of recent transportation research has been to identify and build a sustainable transport future. With this in mind, this paper developed an evolutionary trajectory for car sharing developments based on the technological paradigm to achieve sustainable mobility. A data analysis system (DAS) was built to identify the car sharing developments and literature mining and science mapping conducted to develop a new definition for the car sharing technological paradigm (CSTP). In each CSTP phase, some representative car sharing technologies or modes are discussed: single mode in the first competition stage; fusion mode in the second diffusion stage; and the integration of different modes in the third shift stage. Disruption was observed to occur when the first two stages went to the shift stage-integration mode and four disruptive technology-driven trends were identified in the paradigm shift from the existing paradigm to the new paradigm-sustainable mobility. The tech-integrated analyses found that the connected, autonomous, shared, electric (CASE) integration framework was a possible future sustainable mobility trend. The market dynamics analysis found that along with the key technologies, consumers, regulations and economic considerations, CASE was being implemented and deployed in the market. As it was found that policy has the opportunity to affect the future path to be taken, policy implications are outlined for future tech-integrated developments. The results of this study provide the government, the market, and the public, with information and guidance on the application of the proposed framework to ensure a sustainable mobility future.
Meihui Li; Ziqiang Zeng; Yinhai Wang. An innovative car sharing technological paradigm towards sustainable mobility. Journal of Cleaner Production 2020, 288, 125626 .
AMA StyleMeihui Li, Ziqiang Zeng, Yinhai Wang. An innovative car sharing technological paradigm towards sustainable mobility. Journal of Cleaner Production. 2020; 288 ():125626.
Chicago/Turabian StyleMeihui Li; Ziqiang Zeng; Yinhai Wang. 2020. "An innovative car sharing technological paradigm towards sustainable mobility." Journal of Cleaner Production 288, no. : 125626.
It’s often hard for existing mathematical models to unify and optimize all control variables including phase time, signal phase sequence, offset and signal cycle at the same time. An optimization model for regional green wave coordinated control based on Ring-and-Barrier structure under unsaturated signalized network is proposed in this study. The constraints of signal timing parameters, such as signal cycle, signal phase sequence and phase time, are explored for the intersections based on Ring-and-Barrier structure. It is proved that the comprehensive optimization of phase time and signal cycle, signal phase sequence and offset can be achieved. And a three-dimensional time-space diagram is innovatively proposed to display the regional green wave coordinated control effect. The coordination effect of the model proposed in this paper is compared with the coordination effect of Synchro via a case study. It demonstrates that the regional signal timing scheme obtained by this model can lead to a better result in all aspects of each intersection, each artery and the whole control area. In the meanwhile, the proportion of bandwidth at each intersection is wider than 79% while satisfying the minimum demand of traffic flow at each intersection. To sum up, this model can improve the overall optimization effect of regional signal coordination control by optimizing the phase time, and achieve the comprehensive optimization of signal cycle, signal phase sequence, offset and phase time.
Kai Lu; Xin Tian; Shuyan Jiang; Jianmin Xu; Yinhai Wang. Optimization model for regional green wave coordinated control based on ring-and-barrier structure. Journal of Intelligent Transportation Systems 2020, 1 -13.
AMA StyleKai Lu, Xin Tian, Shuyan Jiang, Jianmin Xu, Yinhai Wang. Optimization model for regional green wave coordinated control based on ring-and-barrier structure. Journal of Intelligent Transportation Systems. 2020; ():1-13.
Chicago/Turabian StyleKai Lu; Xin Tian; Shuyan Jiang; Jianmin Xu; Yinhai Wang. 2020. "Optimization model for regional green wave coordinated control based on ring-and-barrier structure." Journal of Intelligent Transportation Systems , no. : 1-13.
The customized bus (CB) is an emerging type of public transportation system, which not only provides a flexible and reliable demand-responsive service, but also reduces the usage of private car to alleviate traffic congestion in metropolitan cities. The customized bus route design problem (CBRDP) is a crucial procedure in the CB service system designing. In this work, we develop a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service, and each vehicle is allowed to perform multiple trips for operational cost savings. To solve this problem, a constructive two-stage heuristic algorithm is presented to obtain the Pareto solution. Taking a benchmark problem and Beijing commuting corridor as case studies, we calculate and compare the monetary and travel costs of CB with other travel modes, and quantitatively confirm that the CB can be a cost-effective choice for passengers.
Xi Chen; Yinhai Wang; Yong Wang; Xiaobo Qu; Xiaolei Ma. Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis. Expert Systems with Applications 2020, 168, 114242 .
AMA StyleXi Chen, Yinhai Wang, Yong Wang, Xiaobo Qu, Xiaolei Ma. Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis. Expert Systems with Applications. 2020; 168 ():114242.
Chicago/Turabian StyleXi Chen; Yinhai Wang; Yong Wang; Xiaobo Qu; Xiaolei Ma. 2020. "Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis." Expert Systems with Applications 168, no. : 114242.
Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.
Jinjun Tang; Yifan Yang; Wei Hao; Fang Liu; Yinhai Wang. A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -13.
AMA StyleJinjun Tang, Yifan Yang, Wei Hao, Fang Liu, Yinhai Wang. A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-13.
Chicago/Turabian StyleJinjun Tang; Yifan Yang; Wei Hao; Fang Liu; Yinhai Wang. 2020. "A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-13.
Traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.
Hong Wang; Meixin Zhu; Wanshi Hong; Chieh Wang; Gang Tao; Yinhai Wang. Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -11.
AMA StyleHong Wang, Meixin Zhu, Wanshi Hong, Chieh Wang, Gang Tao, Yinhai Wang. Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-11.
Chicago/Turabian StyleHong Wang; Meixin Zhu; Wanshi Hong; Chieh Wang; Gang Tao; Yinhai Wang. 2020. "Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.
Real-time public transit ridership flow and Origin-Destination (O-D) information is essential for improving transit service quality and optimizing transit networks in smart cities. The effectiveness and accuracy of the traditional survey-based methods and smart card data-driven methods for O-D information inference have multiple disadvantages in terms of biased results, high latency, insufficient sample size, the high-cost of time and energy. By considering the ubiquity of smart mobile devices in the world, monitoring public transit ridership flow can be accomplished by passively sensing Wi-Fi and Bluetooth (BT) mobile devices of passengers. This study proposed a system for monitoring real-time public transit passenger ridership flow and O-D information based on customized Wi-Fi and BT sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and non-passenger MAC address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O-D information is proposed. The observed ridership flow is used as the ground truth for evaluating the performance of the proposed algorithm. According to the evaluation results, the proposed algorithm outperformed all selected baseline models and the existing filtering methods. The findings of this study can help to provide real-time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.
Ziyuan Pu; Meixin Zhu; Wenxiang Li; Zhiyong Cui; Xiaoyu Guo; Yinhai Wang. Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices. IEEE Internet of Things Journal 2020, 8, 474 -486.
AMA StyleZiyuan Pu, Meixin Zhu, Wenxiang Li, Zhiyong Cui, Xiaoyu Guo, Yinhai Wang. Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices. IEEE Internet of Things Journal. 2020; 8 (1):474-486.
Chicago/Turabian StyleZiyuan Pu; Meixin Zhu; Wenxiang Li; Zhiyong Cui; Xiaoyu Guo; Yinhai Wang. 2020. "Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices." IEEE Internet of Things Journal 8, no. 1: 474-486.
Traffic forecasting is a classical task for traffic management and it plays an important role in intelligent transportation systems. However, since traffic data are mostly collected by traffic sensors or probe vehicles, sensor failures and the lack of probe vehicles will inevitably result in missing values in the collected raw data for some specific links in the traffic network. Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data. As for short-term traffic forecasting, especially under edge computing and online prediction scenarios, traffic forecasting models with the capability of handling missing values are needed. In this study, we consider the traffic network as a graph and define the transition between network-wide traffic states at consecutive time steps as a graph Markov process. In this way, missing traffic states can be inferred step by step and the spatial–temporal relationships among the roadway links can be incorporated. Based on the graph Markov process, we propose a new neural network architecture for spatial–temporal data forecasting, i.e. the graph Markov network (GMN). By incorporating the spectral graph convolution operation, we also propose a spectral graph Markov network (SGMN). The proposed models are compared with baseline models and tested on three real-world traffic state datasets with various missing rates. Experimental results show that the proposed GMN and SGMN can achieve superior prediction performance in terms of both accuracy and efficiency. Besides, the proposed models’ parameters, weights, and predicted results are comprehensively analyzed and visualized.
Zhiyong Cui; Longfei Lin; Ziyuan Pu; Yinhai Wang. Graph Markov network for traffic forecasting with missing data. Transportation Research Part C: Emerging Technologies 2020, 117, 102671 .
AMA StyleZhiyong Cui, Longfei Lin, Ziyuan Pu, Yinhai Wang. Graph Markov network for traffic forecasting with missing data. Transportation Research Part C: Emerging Technologies. 2020; 117 ():102671.
Chicago/Turabian StyleZhiyong Cui; Longfei Lin; Ziyuan Pu; Yinhai Wang. 2020. "Graph Markov network for traffic forecasting with missing data." Transportation Research Part C: Emerging Technologies 117, no. : 102671.
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.
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 StyleZhiyong 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 StyleZhiyong 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.
Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Laboratory-based methods were used in most previous studies related to road surface friction prediction model development which are difficult for practical implementations. Moreover, for the existing studies about data-driven method development, the time-series features of road surface friction have not been considered. Thus, to utilize the time-series features of road surface friction for predictive performance improvements, this study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model. According to the experiment results, the proposed prediction model outperformed the other baseline models in terms of three metrics. The impacts of the number of time-lags, the predicting time interval, and adding other relative variables as training inputs on predictive accuracy were investigated in this research. The findings of this study can support road maintenance strategy development, especially in winter seasons, thus mitigating the impact of inclement road conditions on traffic mobility and safety.
Ziyuan Pu; Chenglong Liu; Xianming Shi; Zhiyong Cui; Yinhai Wang. Road surface friction prediction using long short-term memory neural network based on historical data. Journal of Intelligent Transportation Systems 2020, 1 -12.
AMA StyleZiyuan Pu, Chenglong Liu, Xianming Shi, Zhiyong Cui, Yinhai Wang. Road surface friction prediction using long short-term memory neural network based on historical data. Journal of Intelligent Transportation Systems. 2020; ():1-12.
Chicago/Turabian StyleZiyuan Pu; Chenglong Liu; Xianming Shi; Zhiyong Cui; Yinhai Wang. 2020. "Road surface friction prediction using long short-term memory neural network based on historical data." Journal of Intelligent Transportation Systems , no. : 1-12.
Accurate and real-time traffic and road weather information acquired using connected vehicle (CV) technologies can help commuters perform safe and reliable trips. A nationwide survey of transit operation managers/supervisors was conducted to assess the suitability for CV transit applications in improving the safety and mobility during winter weather. Almost all respondents expressed positive attitudes towards the potential of CV applications in improving winter transit travel and voiced their concerns over the safety consequences of CV equipment failure, potential of increased driver distraction, and reliability of system performance in poor weather. A concept of operations of CV applications for multimodal winter travel was developed. In the conceptual framework, route-specific road weather and traffic flow data will be used by the transit managers/supervisors to obtain real-time operational status, forecast operational routes and schedules, and assess operational performance. Subsequently, multimodal commuters can receive the road-weather and traffic-flow information as well as transit routes and schedule information.
Yaqin He; Tawhidur Rahman; Michelle Akin; Yinhai Wang; Kakan Dey; Xianming Shi. Connected Vehicle Technology for Improved Multimodal Winter Travel: Agency Perspective and a Conceptual Exploration. Sustainability 2020, 12, 5071 .
AMA StyleYaqin He, Tawhidur Rahman, Michelle Akin, Yinhai Wang, Kakan Dey, Xianming Shi. Connected Vehicle Technology for Improved Multimodal Winter Travel: Agency Perspective and a Conceptual Exploration. Sustainability. 2020; 12 (12):5071.
Chicago/Turabian StyleYaqin He; Tawhidur Rahman; Michelle Akin; Yinhai Wang; Kakan Dey; Xianming Shi. 2020. "Connected Vehicle Technology for Improved Multimodal Winter Travel: Agency Perspective and a Conceptual Exploration." Sustainability 12, no. 12: 5071.
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.
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 StyleMeixin 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 StyleMeixin 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.
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.
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 StyleRuimin 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 StyleRuimin 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.
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.
Xiaolei Ma; Houyue Zhong; Yi Li; Junyan Ma; Zhiyong Cui; Yinhai Wang. Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 4813 -4824.
AMA StyleXiaolei Ma, Houyue Zhong, Yi Li, Junyan Ma, Zhiyong Cui, Yinhai Wang. Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):4813-4824.
Chicago/Turabian StyleXiaolei Ma; Houyue Zhong; Yi Li; Junyan Ma; Zhiyong Cui; Yinhai Wang. 2020. "Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 4813-4824.
De-carbonization of the transport sector is an important pathway to climate-change mitigation and presents the potential for future lower emissions. To assess the potential quantitatively under different optimization measures, this paper presents a hybrid model combining an integrated machine learning model with the scenario analysis. We compare the training accuracy of the back-propagation neural networks (BPNN), Gaussian process regression (GPR), and support vector machine (SVM) fitting model with different training datasets. The results indicate that the performance of the SVM model is superior to other methods. And the particle swarm optimization (PSO) algorithm is then used to optimize hyper-parameters of the SVM model. Two scenarios including business as usual (BAU) and best case (BC) are set according to the current trends and target trends of driving factors identified by the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. Finally, to find the de-carbonization potentials in the transport sector, the PSO-SVM model is applied to predict transport emissions from 2015 to 2030 under two scenarios. Results show that transport emissions reduce by about 131.36 million tons during 2015–2020 and 372.86 million tons during 2021–2025 in the BC scenario. The findings can effectively track, test, and predict the achievement of policy goals and provide practical guidance for de-carbonization development.
Luqi Wang; Xiaolong Xue; Zebin Zhao; Yinhai Wang; Ziqiang Zeng. Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model. Environmental Science and Pollution Research 2020, 27, 21762 -21776.
AMA StyleLuqi Wang, Xiaolong Xue, Zebin Zhao, Yinhai Wang, Ziqiang Zeng. Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model. Environmental Science and Pollution Research. 2020; 27 (17):21762-21776.
Chicago/Turabian StyleLuqi Wang; Xiaolong Xue; Zebin Zhao; Yinhai Wang; Ziqiang Zeng. 2020. "Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model." Environmental Science and Pollution Research 27, no. 17: 21762-21776.
Network-wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. With the rise of artificial intelligence, many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance, given the volume and variety of traffic data has been greatly increased. Considering that traffic status on a road segment is highly influenced by the upstream/downstream segments and nearby bottlenecks in the traffic network, extracting well-localized features from these neighboring segments is essential for a traffic prediction model. Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks, the lack of flexibility in the local-feature extraction process is still a big issue. Classical wavelet transform can detect sudden changes and peaks in temporal signals. Analogously, when extending to the graph/spectral domain, graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features. In this study, to capture the complex spatial-temporal dependencies in network-wide traffic data, we learn the traffic network as a graph and propose a graph wavelet gated recurrent (GWGR) neural network. The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model. A gated recurrent structure is employed to learn temporal dependencies in the sequence data. Comparing to baseline models, the proposed model can achieve state-of-the-art prediction performance and training efficiency on two real-world datasets. In addition, experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR.
Zhiyong Cui; Ruimin Ke; Ziyuan Pu; Xiaolei Ma; Yinhai Wang. Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction. Transportation Research Part C: Emerging Technologies 2020, 115, 102620 .
AMA StyleZhiyong Cui, Ruimin Ke, Ziyuan Pu, Xiaolei Ma, Yinhai Wang. Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction. Transportation Research Part C: Emerging Technologies. 2020; 115 ():102620.
Chicago/Turabian StyleZhiyong Cui; Ruimin Ke; Ziyuan Pu; Xiaolei Ma; Yinhai Wang. 2020. "Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction." Transportation Research Part C: Emerging Technologies 115, no. : 102620.
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.
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 StyleRuimin 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 StyleRuimin 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.
The rapid development of traffic theory and information technology has provided diversified and large-scale traffic data resources for traffic research and urban traffic management. At the same time, these data also present many challenges, such as missing data and deviations in data collection. Many researchers have reported that inaccurate or incomplete measurements of traffic variables can be corrected based on either traditional traffic flow theory, which ignores the randomness of traffic, or are performed using machine learning methods, which emphasize data quantity, but do not make effective use of domain knowledge. This paper proposes a Traffic Factor State Network framework defined by traffic factors and their links to represent the relationships between traffic factors; this framework includes not only obvious traffic factors like volume and speed, but also hidden traffic factors such as the environmental impact factor, which is a variable used to represent complex road conditions. This variable is used to describe the influence of non-traffic flow parameters such as road condition and environmental factors, and is estimated by the EM (Expectation Maximization) algorithm based on historical data. This study used a high-order multivariate Markov model to implement the TFSN, which was then used to establish a stochastic model of speed and related factors. A large amount of historical data was used to calculate and calibrate the strength of the links between the model factors. Finally, a stochastic model of speed prediction was established. The verification results compared with actual cases demonstrate the validity and applicability of the proposed model.
Weibin Zhang; Yaoyao Feng; Kai Lu; Yuhang Song; Yinhai Wang. Speed Prediction Based on a Traffic Factor State Network Model. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 3112 -3122.
AMA StyleWeibin Zhang, Yaoyao Feng, Kai Lu, Yuhang Song, Yinhai Wang. Speed Prediction Based on a Traffic Factor State Network Model. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (5):3112-3122.
Chicago/Turabian StyleWeibin Zhang; Yaoyao Feng; Kai Lu; Yuhang Song; Yinhai Wang. 2020. "Speed Prediction Based on a Traffic Factor State Network Model." IEEE Transactions on Intelligent Transportation Systems 22, no. 5: 3112-3122.