Neural Networks in Transport Applications
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adaptive logit models
Ann Model
artificial intelligence modelling
Average Daily Travel
Backpropagation ANNs
Backpropagation Neural Network
Category=JHB
CNN Model
Daily Travel Distances
Data Sets
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Error Propagation Equations
Flow Density Relationships
fuzzy logic applications
Hidden Units
human behaviour
Incident Detection
Incident Detection Algorithms
Inflow Links
Lane Change Decision
Logit Model
LR Value
Multilayered Feedforward Artificial Neural Networks
neural network transport modelling techniques
neural networks
Present Travel Time
public policy
RMS Error
Speed Density Curve
Speed Density Relationship
Speed Flow Density Relationships
traffic flow prediction
Traffic Flow Variables
traffic management
Training Ann Model
travel behaviour analysis
Travel Time Prediction Model
urban mobility research
Product details
- ISBN 9781138334465
- Weight: 880g
- Dimensions: 152 x 219mm
- Publication Date: 23 May 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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First published in 1998, this volume enters the debate on human behaviour in the form of neural networks in a spatial context. As most transportation research techniques had been developed in the 1960s and 1970s, these authors sought to bring that research into the modern era. Featuring 17 articles from 37 contributors, it begins with an overview and proceeds to examine aspects of travel behaviour, traffic flow and traffic management.
Himanen, Veli; Nijkamp, Peter; Reggiani, Aura
