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Traffic Injury Prevention Volume 18, 2017 - Issue 3
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Original Articles

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

Athanasios TheofilatosNational Technical University of Athens, School of Civil Engineering, Department of Transportation Planning and Engineering, Zografou–Athens, GreeceCorrespondence[email protected]
&
George YannisNational Technical University of Athens, School of Civil Engineering, Department of Transportation Planning and Engineering, Zografou–Athens, Greece
Pages 293-298 | Received 28 Feb 2016, Accepted 02 Jun 2016, Published online: 20 Sep 2016

Citations (14)

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Athanasios Theofilatos, George Yannis, Constantinos Antoniou, Antonis Chaziris & Dimitris Sermpis. (2018) Time series and support vector machines to predict powered-two-wheeler accident risk and accident type propensity: A combined approach. Journal of Transportation Safety & Security 10:5, pages 471-490.
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Articles from other publishers (13)

Suvin P. Venthuruthiyil, Shivasai Samalla & Mallikarjuna Chunchu. (2023) Association of crash potential of powered two-wheelers with the state of traffic stream. Safety Science 166, pages 106257.
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Hajar Lazar & Zahi Jarir. (2022) Road traffic accident prediction: a driving behavior approach. Road traffic accident prediction: a driving behavior approach.
Wenhui Zhang, Tuo Liu & Jing Yi. (2022) Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways. Sustainability 14:18, pages 11761.
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Daniel Santos, José Saias, Paulo Quaresma & Vítor Beires Nogueira. (2021) Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers 10:12, pages 157.
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Sławomir Dorocki & Dorota Wantuch-Matla. (2021) Power Two-Wheelers as an Element of Sustainable Urban Mobility in Europe. Land 10:6, pages 618.
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Xuan Zhang, Huiying Wen, Toshiyuki Yamamoto & Qiang Zeng. (2021) Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors. Journal of Safety Research 76, pages 248-255.
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Xuexin Bao, Dan Jiang, Xuefeng Yang & Hongmei Wang. (2021) An improved deep belief network for traffic prediction considering weather factors. Alexandria Engineering Journal 60:1, pages 413-420.
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Sina Sheikholeslami, Amin M. Boroujerdian & Morteza Asadamraji. (2020) A Rural Road Accident Probability Model Based on Single-Vehicle Hazard Properties including Hazard Color and Mobility: A Driving Simulator Study. Journal of Advanced Transportation 2020, pages 1-8.
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Athanasios Theofilatos, Cong Chen & Constantinos Antoniou. (2019) Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction. Transportation Research Record: Journal of the Transportation Research Board 2673:8, pages 169-178.
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Athanasios TheofilatosApostolos Ziakopoulos. (2018) Examining Injury Severity of Moped and Motorcycle Occupants with Real-Time Traffic and Weather Data. Journal of Transportation Engineering, Part A: Systems 144:11.
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Athanasios Theofilatos. (2017) Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. Journal of Safety Research 61, pages 9-21.
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L. Mussone, M. Bassani & P. Masci. (2017) Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis & Prevention 103, pages 112-122.
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Suvin Padinjare Venthuruthiyil, Shivasai Samalla & Mallikarjuna Chunchu. (2022) Association of Crash Potential of Powered Two Wheelers (Ptw) with the State of Traffic Stream. SSRN Electronic Journal.
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