Model predicts motorway journey time reliability

Apr 16, 2008

For car users and drivers of freight vehicles on motorways, being able to rely on the time taken to complete a journey is as important as the actual duration of the trip itself. For that reason the Ministry of Transport, Public Works and Water Management has listed the improvement of the reliability of door-to-door journey times as one of the main objectives in its latest Mobility Memorandum.

PhD candidate Huizhao Tu has developed a model that can calculate the reliability of journey times and the effects traffic measures and the design of roads have on it. Tu was awarded his doctorate on Tuesday 15 April 2008 on this subject at TU Delft in the Netherlands.

Motorway journey times can be rather unpredictable: one day everything will be fine, while the next, the traffic will be rock solid, even though the weather conditions and the quantity of traffic appear to be the same. For commuters and transporters the fact that journey times are so unpredictable is very annoying. Moreover, they factor this into their journey plans, and this leads to even more uncertainty about how long trips will take. Up to now, little was known about the mechanism that caused journey times to be so unreliable or the factors that played a role.

TU Delft PhD candidate Huizhao Tu has analysed journey time information covering several years for various motorways in the Randstad region. He found – naturally enough – that the busier the roads, the more unpredictable the journey time was. This aspect is important even where traffic intensity is far below the capacity of the road. Journey times are also unreliable on motorways with many junctions and on highways with short entrance and exit roads. It goes without saying that this too has an important influence on the predictability of journey times.

Huizhao Tu’s model calculates the effects of traffic measures (such as the closure of certain road sections and the introduction of maximum speed levels) and of the design of motorways (such as the length of entrance and exit roads) on the predictability of journey times. The model can therefore help contribute to improving the predictability of journey times.

Source: Delft University of Technology

Explore further: An eel-lectrifying future for autonomous underwater robots

add to favorites email to friend print save as pdf

Related Stories

Bird brains may help drones fly and avoid crashing

Nov 05, 2014

Imagine a sky full of autonomous flying machines delivering anything from fast-food to important documents, medical supplies or just a surprise gift for someone special. How do you stop them all colliding ...

And now, the volcano forecast

Oct 22, 2014

Scientists are using volcanic gases to understand how volcanoes work, and as the basis of a hazard-warning forecast system.

Ships without skippers

Sep 08, 2014

A 200 metre long vessel moves slowly across the dark sea surface. There is no one at the wheel. It is quiet on the bridge. There are no signs of life in the engine room or on deck. A scene from a horror film ...

Recommended for you

How polymer banknotes were invented

16 hours ago

The Reserve Bank of Australia (RBA) and CSIRO's 20-year "bank project" resulted in the introduction of the polymer banknote – the first ever of its kind, and the most secure form of currency in the world. ...

Enabling the hearing impaired to locate human speakers

17 hours ago

New wireless microphones systems developed at EPFL should allow the hearing impaired to aurally identify, even with closed eyes, the location of the person speaking. This new technology will be used in classrooms ...

Researcher explores drone-driven crop management

Nov 25, 2014

A flock of pigeons flies over the soybean field where J. Craig Williams is standing. He reaches down and rips off a brown pod from one of the withered plants and splits it open. Grabbing a tiny bean between ...

User comments : 0

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.