IEEE Transactions on Intelligent Transportation Systems

Top Research Topics at IEEE Transactions on Intelligent Transportation Systems?

The journal mainly tackles studies in Artificial intelligence, Intelligent transportation system, Computer vision, Real-time computing and Simulation. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Machine learning and Pattern recognition. The Intelligent transportation system study tackled is a key component of adjacent topics in the area of Data mining.

The Computer vision study featured in it draws connections with the study of Robustness (computer science). The study on Real-time computing presented is investigated in conjunction with research in Global Positioning System.

• Artificial intelligence (21.60%)
• Intelligent transportation system (12.28%)
• Computer vision (11.66%)

What are the most cited papers published in the journal?

• Traffic Flow Prediction With Big Data: A Deep Learning Approach (1512 citations)
• Detecting stress during real-world driving tasks using physiological sensors (1269 citations)
• The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics (884 citations)

Research areas of the most cited articles at IEEE Transactions on Intelligent Transportation Systems:

The journal articles investigate areas of study like Artificial intelligence, Computer vision, Intelligent transportation system, Simulation and Control engineering. The most cited papers address concerns in Artificial intelligence which are intertwined with other disciplines, such as Machine learning and Pattern recognition. The most cited papers explore research in Real-time computing and overlapping concepts in Global Positioning System to expand the discourse in Simulation.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Computer network

The previous edition focused in particular on these issues:

The journal mainly deals with areas of study such as Artificial intelligence, Real-time computing, Intelligent transportation system, Deep learning and Control theory. Topics in Artificial intelligence were tackled in line with various other fields like Machine learning, Computer vision and Pattern recognition. Intelligent transportation system research discussed connects with the study of Computer network.

Edge computing and Vehicular ad hoc network are some topics wherein Computer network research discussed in IEEE Transactions on Intelligent Transportation Systems have an impact. While Deep learning is the key highlight in the journal, it also covered some subjects on Artificial neural network and Data mining. As a part of IEEE Transactions on Intelligent Transportation Systems, discussions in Control theory involve topics like Control theory and Vehicle dynamics.

The most cited articles from the last journal are:

• Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (192 citations)
• Deep Reinforcement Learning for Autonomous Driving: A Survey (86 citations)
• A Survey of Deep Learning Applications to Autonomous Vehicle Control (84 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Transactions on Intelligent Transportation Systems (based on the number of publications) are:

• MengChu Zhou (52 papers) published 12 papers at the last edition, 6 more than at the previous edition,
• Fei-Yue Wang (50 papers) published 11 papers at the last edition, 5 more than at the previous edition,
• Tao Tang (36 papers) published 3 papers at the last edition, 1 less than at the previous edition,
• Mohan M. Trivedi (31 papers) absent at the last edition,
• Long Chen (26 papers) published 9 papers at the last edition, 2 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Transactions on Intelligent Transportation Systems (based on the number of publications) are:

• Beijing Jiaotong University (193 papers) published 43 papers at the last edition, 17 more than at the previous edition,
• Tsinghua University (133 papers) published 37 papers at the last edition, 16 more than at the previous edition,
• Chinese Academy of Sciences (115 papers) published 29 papers at the last edition, 12 more than at the previous edition,
• University of Michigan (93 papers) published 23 papers at the last edition, 5 more than at the previous edition,
• Delft University of Technology (93 papers) published 16 papers at the last edition, 3 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 18.20% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.29% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.17% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.72% of all publications and 47.81% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities in Intelligent Transportation Systems

As research continues to unfold in the domain of Intelligent Transportation Systems, numerous career opportunities are emerging for those interested in this sector. These range from engineers and developers to educators in this field, like preschool teachers who can mold young minds about the importance of intelligent transportation systems. Opportunities also exist for researchers looking to contribute to one of the many subfields such as Artificial Intelligence, Machine Learning, Computer Vision, etc.

Those interested in a career as a developer or engineer in Intelligent Transportation Systems have several pathways to consider. They typically require a background in computer science or engineering and may involve working on exciting projects such as designing intelligent traffic management systems, developing algorithms and systems for self-driving cars, or creating advanced data analysis tools for transport data.

For those who are enthusiastic about sharing the importance and intricacies of Intelligent Transportation Systems with younger generations, a career in education is a promising route. For instance, becoming a preschool teacher in related areas can be a rewarding decision. It provides an opportunity to lay the foundational understanding of such complex concepts at an early age. If you are considering this path, knowing how to become a preschool teacher in North Dakota can be a useful starting point.

Ultimately, the field of Intelligent Transportation Systems offers a wide array of promising career choices, benefiting not just individuals but society at large through the development of more effective, efficient, and sustainable transportation solutions.