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Response to Traffic Incidents",
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@article{bb146601,
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Transportation Systems",
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Relational Analysis",
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@article{bb146603,
        AUTHOR = "Chung, Y. and Recker, W.W.",
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Congested Impact Information on Freeway Accidents",
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@article{bb146604,
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@inproceedings{bb146605,
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@inproceedings{bb146606,
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Surveillance Video",
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@article{bb146607,
        AUTHOR = "Liu, C. and Zhang, S. and Wu, H. and Fu, Q.A.",
        TITLE = "A Dynamic Spatiotemporal Analysis Model for Traffic Incident
Influence Prediction on Urban Road Networks",
        JOURNAL = IJGI,
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        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142375"}

@article{bb146608,
        AUTHOR = "Li, Z. and Liu, P. and Xu, C. and Duan, H. and Wang, W.",
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Reduce Traffic Congestion at Freeway Recurrent Bottlenecks",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142376"}

@article{bb146609,
        AUTHOR = "Li, Z. and Liu, P. and Wang, W. and Xu, C.",
        TITLE = "Development of a Control Strategy of Variable Speed Limits to Reduce
Rear-End Collision Risks Near Freeway Recurrent Bottlenecks",
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@article{bb146610,
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Genetic Algorithms and Cross Entropy",
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@article{bb146613,
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the North Sea",
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Prediction Using the Gradient Boosting Decision Trees Method",
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spatiotemporal congestion on freeway corridors",
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        TITLE = "PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion
Prediction",
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based on sparse detector data",
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@article{bb146620,
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Data-Driven Approaches",
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@article{bb146622,
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        TITLE = "Deep Spatio-Temporal Representation for Detection of Road Accidents
Using Stacked Autoencoder",
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Visual Features and Convolutional Neural Network",
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@article{bb146626,
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@article{bb146627,
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@article{bb146629,
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Connected Vehicles",
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@article{bb146630,
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@article{bb146631,
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@article{bb146632,
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@article{bb146633,
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graph convolutional recurrent neural network and traffic simulation",
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@article{bb146641,
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@article{bb146642,
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floating car data",
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@article{bb146643,
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Adverse Weather Conditions",
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@article{bb146644,
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@article{bb146645,
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Relationships with Land Use: A GIS-Based Geographically Weighted
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@article{bb146646,
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@article{bb146647,
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@article{bb146648,
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Influence of Traffic Congestion on a Hypothetical Scenario of
Electrified Transportation System in New York State",
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@article{bb146649,
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@article{bb146650,
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@article{bb146651,
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@article{bb146652,
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@article{bb146653,
        AUTHOR = "Babu, C.N. and Sure, P. and Bhuma, C.M.",
        TITLE = "Sparse Bayesian Learning Assisted Approaches for Road Network Traffic
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        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142421"}

@article{bb146654,
        AUTHOR = "Parsola, J. and Gangodkar, D. and Mittal, A.",
        TITLE = "Automated system for road extraction and traffic volume estimation for
traffic jam detection",
        JOURNAL = IJCVR,
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@article{bb146655,
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spatio-temporal features",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142423"}

@article{bb146656,
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Networks",
        JOURNAL = IJGI,
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        YEAR = "2021",
        NUMBER = "4",
        PAGES = "xx-yy",
        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142424"}

@article{bb146657,
        AUTHOR = "Wang, C. and Li, S. and Shan, J.",
        TITLE = "Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with
Geographically Weighted Regression",
        JOURNAL = IJGI,
        VOLUME = "10",
        YEAR = "2021",
        NUMBER = "5",
        PAGES = "xx-yy",
        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142425"}

@article{bb146658,
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Traffic Congestion in Expressway Networks Based on Origin-Destination
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        JOURNAL = IJGI,
        VOLUME = "10",
        YEAR = "2021",
        NUMBER = "5",
        PAGES = "xx-yy",
        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142426"}

@article{bb146659,
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in connected vehicle environment: A deep learning approach",
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@article{bb146660,
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traffic crash-prediction",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/motion-f724acc1.html#TT142428"}

@article{bb146661,
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ranking traffic bottlenecks",
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@article{bb146662,
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Neural Networks",
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Discovering Anomalous Events From Intelligent Transportation Systems",
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@article{bb146664,
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Urban Roads",
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@article{bb146665,
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Vehicles Under Mixed Traffic:
An Endogenous Switching Regime Framework",
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@article{bb146670,
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@inproceedings{bb146674,
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@inproceedings{bb146676,
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Last update:Jan 13, 2022 at 22:02:22