Data-Driven Intelligent Transportation Systems: A Survey

By Junping Zhang, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin, Xin Xu, Cheng Chen. IEEE transactions on intelligent transportation systems (Print), 2011

1,648 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/45a33bddf460554b7c1f550aa382d63345a20704

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Yinhai Wang
2. Ruimin Ke
3. Shengna Guo
4. Youfang Lin
5. Huaiyu Wan
6. Gao Cong
7. Xiucheng Li
8. Zhengbing He

In Electrical and Electronic Engineering

1. Khaled Ben Letaief
2. Jun Zhang
3. Sherali Zeadally
4. Halim Yanikomeroglu
5. Xuemin Shen
6. Abbas Yongaçoglu
7. Yong Yuan
8. Feiyue Wang

In Transportation

1. Yinhai Wang
2. Ruimin Ke
3. Shengna Guo
4. Youfang Lin
5. Huaiyu Wan
6. Yunpeng Wang
7. Xiaolei Ma
8. Feiyue Wang

Citations in the corpus

• Improvement CNN Performance by Edge Detection Preprocessing for Vehicle Classification Problem (2018, background): “vehicle classification task in ITS also deals with big data problems [5][6].”
• Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies (2016, background): “Most of this attention is due to its potential to significantly benefit road transportation, including improving traffic efficiency, enhancing road safety, and reducing fuel consumption [1], [2].”
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “Time series analytics have become increasingly crucial in various fields, including but not limited to cloud computing, transportation, energy, finance, social networks, and the Internet-of-Things [8], [9], [10].”
• Towards blockchain-based intelligent transportation systems (2016, background): “…techniques and devices, recent years have witnessed tremendous academic efforts and industry growth in intelligent transportation systems(ITS) [ 1 ] , imposing a profound influence on every aspect of our life with smarter transport facilities and vehicles, as well as safer and more convenient…”
• Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019, background): “Recently, many countries are committed to vigorously develop the Intelligent Transportation System (ITS) (Zhang et al. 2011) to help for efficient traffic management.”
• A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future (2020, background): “Nevertheless, wide-scale data fusion and processing are necessary for such CAV applications [51].”
• Sensor Technologies for Intelligent Transportation Systems (2018, background): “Even though sensors can generate relevant data of transportation systems, the use of additional tools, such as machine learning and data fusion, can generate more data to increase ITS performance, using applications that learn transportation behavior from real-time and historical data [106].”
• Internet of vehicles in big data era (2018, background): “The more data the IoV can handle, the more information that IoV can provide for applications such as ITS and vehicular safety enhancement [146].”
• Capturing Car-Following Behaviors by Deep Learning (2018, background): “However, a question naturally arises along with the development of data-driven technologies [12]–[14]: Is it”
• A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges (2019, background): “To enable smart vehicles, it is essential for vehicles to access the Internet and communicate with each other through smart transportation, which is also known as Intelligent Transportation System (ITS) [161].”
• PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (2018, background): “Vehicle Re-Id can be pervasively applied in intelligent surveillance systems [5], smart transportation [6], and urban computing [7].”
• Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management (2020, background): “However, the physical sensors also have some shortcomings, especially for specific applications [7], [8].”
• Automatic Road Crack Detection and Characterization (2013, background): “, heavy vehicles like tractor trailers) [4], together with the monitoring of traffic intensity [5], for instance, to help identify roads where more frequent pavement status surveys are required.”
• Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving (2019, methodology): “been established with two approaches: rule-based and supervised learning [3], [4].”
• Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, background): “TRAFFIC flow prediction aims at estimating the number of vehicles given a specific region and a time interval, which is an important problem to address in transportation management [21], [34], [36].”