Real-time travel time prediction framework for departure time and route advice

Real-time travel time prediction framework for departure time and route advice

Calvert, S.C.
Snelder, M.
Bakri, T.
Heijligers, B.
Knoop, V.L.

2015

Heavily used urban networks remain a challenge for travel time prediction because traffic flow is rarely homogeneous and is also subject to a wide variety of disturbances. Various models, some of which use traffic flow theory and some of which are data driven, have been developed to predict traffic flow and travel times. Many of these perform well under set conditions. However, few perform well under all or even most urban traffic conditions. As part of the Amsterdam Practical Trial, a comprehensive field operation test for traffic management, a real-time travel time prediction framework, was developed to make use of an ensemble of traffic modeling techniques to predict travel times with great accuracy for arterial roads as well as urban roads. The various models in the framework include both traffic theoretical models and data-driven approaches, making use of some of the largest real-time traffic data sets currently available to limit errors to less than 20% for any time of day or week. The impending implementation of the framework sets it at the forefront of practical real-time implementation of urban travel time prediction. Copyright © 2015 National Academy of Sciences. All rights reserved.

Urban Mobility & Environment
SMb - Smart Mobility
ELSS - Earth, Life and Social Sciences
Mobility & Logistics
Traffic
Urbanisation
Forecasting
Real time control
Time varying control systems
Traffic control
Travel time
Data-driven approach
Field operation
Real time travel
Real-time implementations
Real-time traffic datum
Traffic flow theory
Traffic management
Travel time prediction
Street traffic control

http://resolver.tudelft.nl/uuid:e4d0b818-9ae0-4fef-80d1-63275151c2de

https://doi.org/10.3141/2490-07

546169

National Research Council

0361-1981

Transportation Research Record, 2490

article