𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Short-term prediction of motorway travel time using ANPR and loop data

✍ Scribed by Yanying Li

Publisher
John Wiley and Sons
Year
2008
Tongue
English
Weight
402 KB
Volume
27
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Travel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image‐processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short‐term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Short-term prediction of water flow data
Short-term prediction of water flow data into hydroelectric power stations using local fuzzy reconstruction method
✍ Tadashi Iokibe; Yoshitsugu Yonezawa; Minako Taniguchi 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 174 KB

For predicting the flow into a hydroelectric power station, complex natural phenomena have to be dealt with, so conventional mathematical models based on hydraulics may not produce satisfactory results. When a neural network is used, its construction cannot be easily determined, and extra neural net