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The Effects of Detector Spacing on Traffic Forecasting Performance Using Neural Networks
Author(s) -
Chen Haibo,
Dougherty Mark S.,
Kirby Howard R.
Publication year - 2001
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00244
Subject(s) - detector , artificial neural network , traffic flow (computer networking) , computer science , basis (linear algebra) , pruning , occupancy , simulation , real time computing , data mining , statistics , econometrics , artificial intelligence , mathematics , engineering , telecommunications , computer network , civil engineering , geometry , agronomy , biology
An investigation was made as to how short‐term traffic forecasting on motorways and other trunk roads is related to the density of detectors. Forecasting performances with respect to different detector spaces have been investigated with both simulated data and real data. Pruning techniques to the input variables used for neural networks were applied to the simulated data. The real data were collected from the M25 motorway and included flow, speed, and occupancy. With the data used in our study, the forecasting performances decrease with the increase of detector spaces. However, by taking the assumed costs of detector infrastructure into account, it may be concluded from this study that increasing coverage to a spacing of 500 m gives little extra benefit and may actually be counter productive in certain circumstances. It was concluded that, on the basis of current evidence, a detector spacing of between 1 and 1.5 km might be optimal.