Traffic Demand Forecasting
Traffic Demand Forecasting is a systematic analytical process within transportation planning that estimates the future volume and spatial distribution of vehicular movements across a highway network based on socioeconomic and land-use projections, travel behavior models, and network characteristics.
1.7K
Publications
83.2K
Citations
5K
Authors
1.3K
Institutions
Publications per year
2017–2026
870
Variants
Travel Demand Forecasting
Parents
Children
Autoregressive Integrated Moving Average ForecastingDeep Learning-based ForecastingDeterministic Traffic ForecastingHoliday Traffic ForecastingIncident Impact ForecastingLong-term Traffic ForecastingMachine Learning-based ForecastingMultistep Traffic ForecastingOrigin-destination Demand EstimationPeak Hour Demand Forecasting
1.7K
Driving with knowledge from the physical world
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5K
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9
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Hong Kong University of Science and Technology, Hong Kong SAR China
8 publications · 1.3K citations
8
h-index
Hong Kong Polytechnic University, Hong Kong SAR China
8 publications · 713 citations
8
h-index
8
h-index
1–5 of 5K
1.3K
Tsinghua University
Beijing, China
25 h-index
99 publications
6.3K citations

Beihang University
Beijing, China
23 h-index
86 publications
4.7K citations

Southeast University
Nanjing, China
22 h-index
99 publications
5.2K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
21 h-index
49 publications
3.4K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
19 h-index
55 publications
8.7K citations
University of California, Berkeley
Berkeley, United States
18 h-index
39 publications
2K citations

Zhejiang University
Hangzhou, China
18 h-index
69 publications
6.7K citations

University of Minnesota
Minneapolis, United States
17 h-index
29 publications
1.2K citations
1–8 of 1.3K
65 publications · 7.6K citations
39
h-index
59 publications · 4.5K citations
33
h-index
TRR · 108 publications · 3.5K citations
32
h-index
32 publications · 1.8K citations
21
h-index
31 publications · 1.1K citations
21
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