Application of Deep Learning in Intelligent Transportation Systems
| dc.contributor.author | Dabiri, Sina | en |
| dc.contributor.committeechair | Heaslip, Kevin Patrick | en |
| dc.contributor.committeemember | Gerdes, Ryan M. | en |
| dc.contributor.committeemember | Ramakrishnan, Naren | en |
| dc.contributor.committeemember | Abbas, Montasir M. | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2019-02-02T09:01:49Z | en |
| dc.date.available | 2019-02-02T09:01:49Z | en |
| dc.date.issued | 2019-02-01 | en |
| dc.description.abstract | The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application. | en |
| dc.description.abstractgeneral | The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. Furthermore, the recent advances in positioning tools (e.g., GPS sensors) and ever-popularity of social media networks have enabled generation of massive spatiotemporal and crowdsourced data. This dissertation aims to leverage the advances in artificial intelligence so as to unlock the rick knowledge in the recorded data and in turn, optimizing the transportation systems in a cost-effective way. In particular, this dissertation seeks for proposing end-to-end frameworks based on deep learning models, as an advanced branch of artificial intelligence, as well as spatiotemporal and crowdsourced datasets (e.g., GPS trajectory and social media) for improving three transportation problems. (1) Travel Mode Detection, which is defined as identifying users’ transportation mode(s) (e.g., walk, bike, bus, car, and train) when traveling around the traffic network. (2) Vehicle Classification, which is defined as identifying the vehicle’s type (e.g., passenger car and truck) while moving in a traffic network. (3) traffic information system based on social media networks, which is defined as detecting traffic events (e.g., crash) and capturing traffic information (e.g., traffic congestion) on a real-time basis from users’ tweets. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application. | en |
| dc.description.degree | PHD | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:18657 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/87409 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Deep learning (Machine learning) | en |
| dc.subject | Intelligent Transportation Systems | en |
| dc.subject | GPS Data | en |
| dc.subject | Twitter Data | en |
| dc.subject | Travel Mode Detection | en |
| dc.subject | Vehicle Classification | en |
| dc.subject | Traffic Information System | en |
| dc.subject | Machine learning | en |
| dc.subject | Natural Language Processing | en |
| dc.title | Application of Deep Learning in Intelligent Transportation Systems | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | PHD | en |