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Traffic Management for Smart Cities [chapter]

Andreas Allström, Jaume Barceló, Joakim Ekström, Ellen Grumert, David Gundlegård, Clas Rydergren
2016 Designing, Developing, and Facilitating Smart Cities  
Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management  ...  Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction  ...  However, the sensors that enable a bridging between traffic demand models and real-time traffic prediction models are the sensors that maintain a user or vehicle identity over large periods of time and  ... 
doi:10.1007/978-3-319-44924-1_11 fatcat:4m57qtxqvvguxixekhikdg6mve

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

Sanchita Basak, Afiya Ayman, Aron Laszka, Abhishek Dubey, Bruno Leao
2019 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure.  ...  Our focus in this paper is on the failure dynamics of traffic networks.  ...  ACKNOWLEDGEMENTS This research is funded in part by a grant from Siemens, CT and the following grants from National Science Foundation: 1818901 and 1647015.  ... 
doi:10.36001/phmconf.2019.v11i1.861 fatcat:odmkzwc7qvdnpovww7jetuuqiq

Application of Real-time Sensor Data to Evaluation of Performance of Ad Hoc Distributed Traffic Simulation

Wonho Suh
2019 Sensors and materials  
The proposed ad hoc distributed traffic simulation with real-time sensor data was found to be capable of capturing dynamically changing traffic conditions in both the peak traffic and incident scenarios  ...  Onboard sensors on vehicles collect real-time traffic data and simulate traffic states in a distributed fashion.  ...  Although parallel and distributed simulation with real-time sensor data increases the simulation speed in a large network simulation model, it requires simulation time managing processes to synchronize  ... 
doi:10.18494/sam.2019.2265 fatcat:iylb4acrkra65biqtgd3cqhe3i

A Synthesis of emerging data collection technologies and their impact on traffic management applications

Constantinos Antoniou, Ramachandran Balakrishna, Haris N. Koutsopoulos
2011 European Transport Research Review  
Conclusions The current state-of-the-art of traffic modeling is discussed, in the context of using emerging data sources for better planning, operations and dynamic management of road networks.  ...  Furthermore, the fusion of condition information with traffic data can result in better and more responsive dynamic traffic management applications with a richer data background.  ...  dynamic traffic assignment (DTA) concepts, have emerged to support network state estimation and prediction [19, 20, 42] .  ... 
doi:10.1007/s12544-011-0058-1 fatcat:ilxxtyk7uvdojb4k62vbgnzyey

Latent Space Model for Road Networks to Predict Time-Varying Traffic

Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the superiority of our framework for real-time traffic prediction on large road networks over competitors  ...  Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability.  ...  The traffic prediction problem aims to predict the future travel speed of each and every edge of a road network, given the historical speed readings from the sensors on these edges.  ... 
doi:10.1145/2939672.2939860 dblp:conf/kdd/DengSDZYL16 fatcat:xywooi5wnbhavkrfj4csz7uyda

Latent Space Model for Road Networks to Predict Time-Varying Traffic [article]

Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
2016 arXiv   pre-print
By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks  ...  Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability.  ...  The traffic prediction problem is to predict the future travel speed of each and every edge of a road network, given the historical speed readings sensed from the sensors on these edges.  ... 
arXiv:1602.04301v3 fatcat:wxkgr7llenaylj65orwcrjdyju

Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting [article]

Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane Macfarlane
2020 arXiv   pre-print
Furthermore, we demonstrate that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics.  ...  Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic.  ...  We show that DCRNN can be extended for multioutput learning to forecast both flow and speed simultaneously, as opposed to the previous DCRNN implementation that forecast either speed or flow.  ... 
arXiv:1909.11197v4 fatcat:ugwujpeh7rhzdptk3uztjdxopa

The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Liwen Zhang, Chao Zhang, Zhuo Sun, You Dong, Pu Wei
2018 Complexity  
In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model.  ...  The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as  ...  As shown in Figure 7 , the wireless sensor network technology is used to test the dynamic response of the long-span bridge under random traffic flow.  ... 
doi:10.1155/2018/4154673 fatcat:2jntfntorrfdblep72jf6odihq

Speed Profile Prediction in Intelligent Transport Systems Exemplified by Vehicle to Vehicle Interactions

Ivan Bosankic, Lejla Banjanovic-Mehmedovic, Fahrudin Mehmedovic
2015 Cybernetics and Information Technologies  
In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength  ...  Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems.  ...  Acknowledgements: The paper is partially supported by TUD COST TU 1102 "ARTS -Towards Autonomic Road Transport Support Systems" and FP7 Project 316087 ACOMIN "Advance Computing and Innovation".  ... 
doi:10.1515/cait-2015-0017 fatcat:svf52rfeerdo5bd2z3owe2kqx4

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xin He, Yuben Qu, Saeed Amirgholipour, Yue Xi, Priyadarsi Nanda, Xiangjian He
2020 CCF Transactions on Pervasive Computing and Interaction  
In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.  ...  With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control  ...  As a pioneering work in the ITS community, Ma et al. (2017) advocated 'Learning Traffic as Images' and proposed a deep convolutional neural network for speed prediction in large-scale transportation  ... 
doi:10.1007/s42486-020-00039-x fatcat:c3c2b3fvpzdqdlxy2ke7ckxlpu

A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting [article]

Reza Asadi, Amelia Regan
2019 arXiv   pre-print
The experimental results illustrate the model outperforms baseline and state of the art models in a traffic flow prediction dataset.  ...  In this paper, we propose a deep neural network framework for large-scale spatial time series forecasting problems.  ...  A CNN model is used to train from images and forecast speed in large transportation networks.  ... 
arXiv:1902.00636v1 fatcat:5y45pzhzcvfptprelhqncbnckm

Dynamic Data Driven Application Simulation of Surface Transportation Systems [chapter]

R. Fujimoto, R. Guensler, M. Hunter, H. -K. Kim, J. Lee, J. Leonard, M. Palekar, K. Schwan, B. Seshasayee
2006 Lecture Notes in Computer Science  
Building upon activities such as the Vehicle-Infrastructure Integration initiative, a hierarchical DDDAS architecture is presented that includes coupled in-vehicle, roadside, and traffic management center  ...  A project concerned with applying Dynamic Data Driven Application Simulations (DDDAS) to monitor and manage surface transportation systems is described.  ...  The sensor network and distributed simulation system also allows for the implementation of a dynamic emergency response plan.  ... 
doi:10.1007/11758532_57 fatcat:m7ris474ynhj5g7sjlqdngruoq

Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

Liang Ge, Siyu Li, Yaqian Wang, Feng Chang, Kunyan Wu
2020 Applied Sciences  
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS).  ...  To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction.  ...  Meta Learning Network utilizes graph attention network (GAT) and the recurrent neural network (RNN) for traffic prediction. • Figure 8 . 8 Speed prediction in the morning peak hours and weekends of  ... 
doi:10.3390/app10041509 fatcat:53fm6xiirvfbrclimg2367d7ti

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting [article]

Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu
2018 arXiv   pre-print
We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.  ...  for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow.  ...  The goal of traffic forecasting is to predict the future traffic speeds of a sensor network given historic traffic speeds and the underlying road networks. (1) Traffic speed in road 1 are similar to road  ... 
arXiv:1707.01926v3 fatcat:axu646j76ve5pppx5gkmtw2hba

Prediction of traveller information and route choice based on real-time estimated traffic state

Afzal Ahmed, Dong Ngoduy, David Watling
2015 Transportmetrica B: Transport Dynamics  
Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage  ...  The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended Kalman  ...  The EKF is more efficient in computation and can be applied in large scale networks, which is our ongoing work.  ... 
doi:10.1080/21680566.2015.1052110 fatcat:dznxfxfqjbbn5np5qfhfsoq3xa
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