65 results found
Yang H, Bao Y, Huo J, et al., 2022, Impact of road features on shared e-scooter trip volume: A study based on multiple membership multilevel model, Travel Behaviour and Society, Vol: 28, Pages: 204-213, ISSN: 2214-367X
Liu W, Sun H, Lai D, et al., 2022, Performance of fast fluid dynamics with a semi-Lagrangian scheme and an implicit upwind scheme in simulating indoor/outdoor airflow, Building and Environment, Vol: 207, ISSN: 0360-1323
Computational fluid dynamics can be time consuming for predicting indoor airflows and pollutant transport in large-scale problems or emergency management. Fast fluid dynamics (FFD) is able to accomplish efficient and accurate simulation of indoor/outdoor airflow. FFD solves the advection term of the Navier–Stokes equations either by a semi-Lagrangian (SL) scheme or an implicit upwind (IU) scheme. The SL scheme can be highly efficient, but its first-order version is not conservative and introduces significant numerical diffusion. To improve its accuracy, a high-order temporal and interpolation scheme that not only reduces dissipation and dispersion errors but also guarantees the convergence speed should be applied. Otherwise, an IU scheme instead could be used to solve the advection term. The IU scheme is conservative and introduces minor numerical diffusion, but it may increase the computation time. Therefore, this study investigated the performance of FFD with SL scheme using high-order temporal and interpolation schemes and that with IU scheme. The comparisons used experimental data of two indoor airflows and one outdoor airflow. The results showed that FFD with IU scheme was overall more accurate than FFD with SL scheme. In simulating indoor airflow, both methods were robust and the predictions were independent of time step sizes if the Courant number was less than or equal to one. In simulating the outdoor airflow, the FFD with SL scheme performed better than the FFD with IU scheme for large time step sizes. The FFD with IU scheme consumed 44%–61% computing time of the FFD with SL scheme.
Wang Y, Zhao M, Yu X, et al., 2022, Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview, methods, and case studies, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 134, ISSN: 0968-090X
Hu S, Shu S, Bishop J, et al., 2022, Vehicle telematics data for urban freight environmental impact analysis, Transportation Research Part D: Transport and Environment, Vol: 102, Pages: 103121-103121, ISSN: 1361-9209
Road freight transport is one of the major contributors to greenhouse gas and air pollutantemissions. Hence, it is increasingly regulated in urban areas to reduce its impact on theenvironment and human health. The rich data available from telematics has the potentialto provide high-resolution information, yet research has not been conducted to understand,evaluate, and ultimately improve the operation and impacts of urban road freight. This paperdemonstrates the role of vehicle telematics data in enabling quantitative assessment of theimpacts of urban freight transport for the effective management of relevant policies. We presenta comprehensive data-driven approach that provides a robust quantitative evaluation andapplies it to a case study of the London Lorry Control Scheme (LLCS) policy in UK. We showthat, for the studied freight operator, the LLCS policy affects their drivers’ route choice bothinside and outside the restricted hours. The spatio-temporal distributions of different parametersincluding traffic speeds, fuel economy and emissions at different times of the day are comparedand analyzed. The results indicate that the unintended consequences of urban freight transportpolicies can include an extra 15% vehicle-km traveled per trip and 12% liter of fuel consumedper trip
Zhou C, Xiao D, Hu J, et al., 2022, An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results, Lecture Notes in Civil Engineering, Publisher: Springer International Publishing, Pages: 1134-1143, ISBN: 9783030918767
Li Y, Lv Q, Zhu H, et al., 2022, Variable Time Headway Policy Based Platoon Control for Heterogeneous Connected Vehicles With External Disturbances, IEEE Transactions on Intelligent Transportation Systems, Pages: 1-11, ISSN: 1524-9050
Liu X, Zheng R, Wang H, et al., 2021, A Knowledge Management Framework for Vehicle Hazard Analysis, 2021 IEEE International Conference on e-Business Engineering (ICEBE), Publisher: IEEE
Li J, Zhang K, Shen L, et al., 2021, A Domain Adaptation Framework for Short-term Traffic Prediction, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE
Ma Y, Wang L, Wang Y, et al., 2021, Developing Smart Lane-changing Strategies for CAVs on Freeways based on MOBIL and Reinforcement Learning, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE
Hu Y, Wang Y, Jin X, et al., 2021, Urban Eco-driving of Connected and Automated Vehicles in Traffic-Mixed and Power-heterogeneous Conditions, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE
Chen C, Hu S, Ochieng WY, et al., 2021, Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach, JOURNAL OF ADVANCED TRANSPORTATION, Vol: 2021, ISSN: 0197-6729
Liu Q, Hu S, Angeloudis P, et al., 2021, Dynamic wireless power transfer system for electric-powered connected and autonomous vehicle on urban road network, IET INTELLIGENT TRANSPORT SYSTEMS, Vol: 15, Pages: 1153-1166, ISSN: 1751-956X
Tang C, Hu W, Hu S, et al., 2021, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 2956-2968, ISSN: 1524-9050
With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance promises to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city implies that traffic scenarios in real systems are diverse. Therefore, if a route guidance method cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee optimal results under all traffic scenarios. Thus, ideal dynamic route guidance methods should have a highly adaptive learning ability under diverse traffic scenarios so as to have extensive improvement capabilities for different traffic scenarios. In this study, an A* trajectory rejection method based on multi-agent reinforcement learning (A*R²) is proposed; the method integrates both system and user perspectives to mitigate traffic congestion and reduce travel time (TT) and travel distance (TD). First, owing to its adaptive learning ability, the A*R² can comprehensively analyze the traffic conditions for different traffic scenarios and intelligently evaluate the road congestion index from a system perspective. Then, the A*R² determines the routes for all vehicles from user perspective according to the road network congestion index. An extensive set of simulation experiments reveal that, under various traffic scenarios, the A*R² can rely on its adaptive learning ability to achieve better traffic efficiency. Moreover, even in cases where many drivers are not fully compliant with the route guidance, the traffic efficiency can still be improved significantly by A*R².
Yu J, Mo D, Xie N, et al., 2021, Exploring multi-homing behavior of ride-sourcing drivers via real-world multiple platforms data, TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, Vol: 80, Pages: 61-78, ISSN: 1369-8478
Qian G, Guo M, Zhang L, et al., 2021, Traffic scheduling and control in fully connected and automated networks, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 126, ISSN: 0968-090X
Wang Y, Yu X, Zhang S, et al., 2021, Freeway Traffic Control in Presence of Capacity Drop, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 22, Pages: 1497-1516, ISSN: 1524-9050
Zhou Q, Mohammadi R, Zhao W, et al., 2021, Queue Profile Identification at Signalized Intersections with High-Resolution Data from Drones, 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Publisher: IEEE
Li Y, Chen B, Zhao H, et al., 2021, A Car-Following Model for Connected and Automated Vehicles With Heterogeneous Time Delays Under Fixed and Switching Communication Topologies, IEEE Transactions on Intelligent Transportation Systems, Pages: 1-13, ISSN: 1524-9050
Taleongpong P, Hu S, Jiang Z, et al., 2020, Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network, Journal of Intelligent Transportation Systems: technology, planning, and operations, Vol: 2020, Pages: 1-19, ISSN: 1547-2450
Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques alongside data mining processes to create a framework that predicts key performance indicators (KPIs), reactionary arrival delay, reactionary departure delay, dwell time and travel time. The frameworks in this paper provide greater accuracy in predicting KPIs through state-of-the-art ML models compared to existing delay prediction systems. Further discussion on the improvements, applicability and scalability of this framework are also provided in this paper.
Li J, Guo F, Wang Y, et al., 2020, Short-term traffic prediction with deep neural networks and adaptive transfer learning, 23rd International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 1-6
A key problem in short-term traffic prediction is the prevailing data missing scenarios across the entire traffic network. To address this challenge, a transfer learning framework is currently used in the literature, which could improve the prediction accuracy on the target link that suffers severe data missing problems by using information from source links with sufficient historical data. However, one of the limitations in these transfer-learning based models is their high dependency on the consistency between datasets and the complex data selection process, which brings heavy computation burden and human efforts. In this paper, we propose an adaptive transfer learning method in short-term traffic flow prediction model to alleviate the complex data selection process. Specifically, a self-adaptive neural network with a novel domain adaptation loss is developed. The domain adaptation loss is able to calculate the distance between the source data and the corresponding target data in each training batch, which can help the network to adaptively filter inconsistent source data and learn target link related information in each training batch. The Maximum Mean Discrepancy (MMD) measurement, which has been fully validated and applied in transfer learning research, is used in combination with the Gaussian kernel to measure the distance between datasets in each training batch. A series of experiments are designed and conducted using 15-minute interval traffic flow data from the Highways England, UK. The results have demonstrated that the proposed adaptive transfer learning method is less affected by the inconsistency between datasets and provides more accurate short-term traffic flow prediction.
Wu C, Hu S, Lee C-H, et al., 2020, Multi-platform data collection for public service with Pay-by-Data, MULTIMEDIA TOOLS AND APPLICATIONS, Vol: 79, Pages: 33503-33518, ISSN: 1380-7501
Liu W, van Hooff T, An Y, et al., 2020, Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method, Building and Environment, Vol: 186, Pages: 1-11, ISSN: 0360-1323
It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model.
Wu C, Wang G, Hu S, et al., 2020, A data driven methodology for social science research with left-behind children as a case study, PLOS ONE, Vol: 15, ISSN: 1932-6203
Wu C, Zheng P, Xu X, et al., 2020, Discovery of the Environmental Factors Affecting Urban Dwellers' Mental Health: A Data-Driven Approach, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 17
Wu C, Wang Z, Hu S, et al., 2020, An automated machine-learning approach for road pothole detection using smartphone sensor data, Sensors, Vol: 20, Pages: 1-23, ISSN: 1424-8220
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
Song J, Hu S, Han K, et al., 2020, Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation, NETWORKS & SPATIAL ECONOMICS, Vol: 20, Pages: 675-702, ISSN: 1566-113X
Yu J, Stettler MEJ, Angeloudis P, et al., 2020, Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces, Transportation Research Part C: Emerging Technologies, Vol: 112, Pages: 136-152, ISSN: 0968-090X
The ability to obtain accurate estimates of city-wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms. This paper focuses on the network-wide traffic speed estimation, using trajectory data generated by a city-wide fleet of ride-sourcing vehicles equipped with GPS-capable smartphones. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. Data limitations are addressed using the Schatten p-norm matrix completion algorithm, which can minimize speed estimation errors even with high rates of data unavailability. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.
Cao J, Hu Y, Diamantis M, et al., 2020, A Max Pressure Approach to Urban Network Signal Control with Queue Estimation using Connected Vehicle Data, 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, ISSN: 2153-0009
Heglund JSW, Taleongpong P, Hu S, et al., 2020, Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks, 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, ISSN: 2153-0009
Li Y, Zhong Z, Song Y, et al., 2020, Longitudinal Platoon Control of Connected Vehicles: Analysis and Verification, IEEE Transactions on Intelligent Transportation Systems, ISSN: 1524-9050
This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles.
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