NDLI: Deep learning methods in transportation domain: a review

Content Provider	IET Digital Library
Author	Nguyen, Hoang Kieu, Le Minh Wen, Tao Cai, Chen
Abstract	Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state-of-the-art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.
Starting Page	998
Ending Page	1004
Page Count	7
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 9, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.0064
Journal	IET Intelligent Transport Systems
Publisher Date	2018-07-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Vehicle Detection Autonomous Driving Big Data Big Data Generation CCTV Data Handling Technique Deep Learning Method Deep Learning System Driver Behaviours GPS Incident Reports Knowledge Engineering Technique Learning in AI Machine Learning Method Other DBMS Probe Road Sonsor Road Traffic Traffic Data Traffic Engineering Computing Traffic Flow Forecasting Traffic Incident Processing Traffic Signal Control Transportation Data Transportation Domain Transportation Network Representation Travel Demand Prediction
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Deep reinforcement-learning-based driving policy for autonomous road vehicles

A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

Short-term traffic flow prediction of road network based on deep learning

Real-time detection of distracted driving based on deep learning

Lane detection method based on lane structural analysis and CNNs

Driving policies of V2X autonomous vehicles based on reinforcement learning methods

Iterative learning of an unknown road path through cooperative driving of vehicles

Analysis of road travel behaviour based on big trajectory data

Geo-referencing naturalistic driving data using a novel method based on vehicle speed

Deep learning methods in transportation domain: a review

Similar Documents

Deep reinforcement-learning-based driving policy for autonomous road vehicles

A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

Short-term traffic flow prediction of road network based on deep learning

Real-time detection of distracted driving based on deep learning

Lane detection method based on lane structural analysis and CNNs

Driving policies of V2X autonomous vehicles based on reinforcement learning methods

Iterative learning of an unknown road path through cooperative driving of vehicles

Analysis of road travel behaviour based on big trajectory data

Geo-referencing naturalistic driving data using a novel method based on vehicle speed

Deep learning methods in transportation domain: a review