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Topic · Building and Construction

Traffic Prediction and Management Techniques

Traffic Prediction and Management Techniques is a research topic within Building and Construction. Science Explorer counts 38k research works in it since 1953. 18.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of deep learning, neural networks, and spatio-temporal data analysis for traffic flow prediction and forecasting in urban environments. The research covers topics such as short-term forecasting, graph convolutional networks, time series analysis, and the integration of intelligent transportation systems.

  • Deep Learning
  • Traffic Flow
  • Short-Term Forecasting
  • Spatio-Temporal Data
  • Neural Networks
  • Urban Traffic
  • Graph Convolutional Networks
  • Time Series Analysis
  • Intelligent Transportation Systems
  • Probabilistic Forecasting
Research works
38k
fractional, since 1953
In the world top 10%
6.9k
per year above
Top-10% rate
18.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+94%
the tick is no change

Which countries lead Traffic Prediction and Management Techniques research?

By volume, China and India publish the most (5.5k and 1.8k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 5.5k works
  2. 2 India 1.8k works
  3. 3 United States 1.3k works
  4. 4 South Korea 257 works
  5. 5 United Kingdom 255 works
  6. 6 Japan 248 works
  7. 7 Germany 236 works
  8. 8 Canada 236 works
  9. 9 Indonesia 224 works
  10. 10 Italy 203 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 54.1%India: 17.3%United States: 12.4%South Korea: 2.5%6 others listed: 13.7%54%largest
China5,540 · 54.1%India1,775 · 17.3%United States1,271 · 12.4%South Korea257 · 2.5%6 others listed1,402 · 13.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Traffic Prediction and Management Techniques research?

By volume in 2022–2025, Beijing Jiaotong University publishes the most Traffic Prediction and Management Techniques research, followed by Tongji University and Southeast University.

By volume, 2022–2025

  1. 1 Beijing Jiaotong UniversityChina 198 works
  2. 2 Tongji UniversityChina 155 works
  3. 3 Southeast UniversityChina 127 works
  4. 4 Chang'an UniversityChina 109 works
  5. 5 Tsinghua UniversityChina 107 works
  6. 6 Southwest Jiaotong UniversityChina 90 works
  7. 7 Wuhan University of TechnologyChina 75 works
  8. 8 Beihang UniversityChina 73 works
  9. 9 Beijing University of TechnologyChina 73 works
  10. 10 Central South UniversityChina 67 works

Who are the leading researchers in Traffic Prediction and Management Techniques?

The most-cited researchers publishing on Traffic Prediction and Management Techniques include Dragomir Anguelov, Xuemin Shen and MengChu Zhou.

  1. 1 Dragomir Anguelov United States 18k citations
  2. 2 Xuemin Shen Canada 5.1k citations
  3. 3 MengChu Zhou United States 4.4k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Traffic Prediction and Management Techniques research done?

The largest centres of Traffic Prediction and Management Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Xi'an (China).

Largest cities, 2022–2025

  1. 1 Beijing China 1.2k works
  2. 2 Shanghai China 419 works
  3. 3 Nanjing China 366 works
  4. 4 Xi'an China 264 works
  5. 5 Wuhan China 240 works
  6. 6 Chennai India 233 works
  7. 7 Chengdu China 206 works
  8. 8 Guangzhou China 206 works
  9. 9 Chongqing China 180 works
  10. 10 Hangzhou China 173 works
See Traffic Prediction and Management Techniques on the map

Where is the best place to study Traffic Prediction and Management Techniques?

Among universities, judged by research, Southeast University, University of Central Florida and Beijing Jiaotong University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 27.45%fractional works in this node (log) →share in the world top 10% →Southeast University: 33, 9.7%University of Central Florida: 32, 33.5%Beijing Jiaotong University: 198, 17.6%Tongji University: 155, 19.4%University of Technology Sydney: 16, 42.9%Raisoni Group of Institutions: 11, 24.7%University of Hong Kong: 29, 42.0%Nanyang Technological University: 40, 31.4%Southeast University: 127, 20.6%Hong Kong Polytechnic University: 49, 32.7%University of Centra…Tongji UniversityBeijing Jiaotong Uni…Southeast University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Southeast UniversityBangladesh 61.39.7%22.9×33 +153.4%
2 University of Central FloridaUnited States 59.933.5%6.5×32 +185.7%
3 Beijing Jiaotong UniversityChina 57.117.6%24.3×198 -36.0%
4 Tongji UniversityChina 56.119.4%9.1×155 -19.2%
5 University of Technology SydneyAustralia 55.842.9%2.6×16 +1052.5%
6 Raisoni Group of InstitutionsIndia 54.724.7%9.5×11 +258.5%
7 University of Hong KongHong Kong 54.442.0%2.6×29 +90.6%
8 Nanyang Technological UniversitySingapore 53.131.4%3.8×40 +100.7%
9 Southeast UniversityChina 52.120.6%7.1×127 +3.4%
10 Hong Kong Polytechnic UniversityHong Kong 51.932.7%4.2×49 +8.9%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Traffic Prediction and Management Techniques research growing?

Output in 2018–2022 was 94% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Traffic Prediction and Management Techniques.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.