A real world problem: Predicting travel time from Lahti to Heinola

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A real world problem: Predicting travel time from Lahti to Heinola

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A real world problem: Predicting travel time from Lahti to Heinola

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A real world problem: Predicting travel time from Lahti to Heinola

A research was carried out on main road 4 between points A (Lahti) and D (Heinola)

in Southern Finland. The average daily summertime traffic on this 28 km section is about

15100 vehicles per day. The study section AD is divided into three sub-sections AB, BC

and CD with camera stations approximately equally distributed over link AD length and

equipped with an automatic travel time monitoring system.

The system is based on an artificial vision and neural network application, which

automatically reads license plates. Moreover, there is an inductive loop detector on

station C gathering information on traffic volumes and point speeds. A variable message

sign (VMS) at point A gives upper and lower bounds of a forecast about the travel to

the point D. The prediction classes are 20-25 min, 25-30 min, 30-40 min, 40-50 min

and above 50 min. Travel time from point A to point D is

regarded as congested if it is above 25 min.

Point

C

Heinola Point D

Point

B

Lahti

Point A

It will take 20 - 25

minutes to get to

Heinola

The data was collected during the summer 2000 and processed into the

following seven columns form

Typical data sets contained 3700 - 19 000 rows corresponding to 40.000 -

150.000 vehicles

Based on GUHA analysis, the rule base of a Many-valued Similarity -

inference system is the following (unit of measure is min)

IF AD >= 23 AND AB+BC >= 17.5 AND 23 <= AB

THEN PR = > 50 min

IF AD >= 23 AND AB+BC >= 17.5 AND 14 =< AB < 23

THEN PR = 40 - 50 min

IF AD >= 23 AND AB+BC >= 17.5 AND 10.5 <= AB < 14

THEN PR = 30 - 40 min

IF AD >= 23 AND AB+BC >= 17.5 AND 5.58 <= AB <10.5

THEN PR = 30 - 40 min

IF AB+BC+CD >= 21.25 AND BC>=6.3

THEN PR = 25 - 30 min

IF AD >= 35 AND BC>=6.3

THEN PR = 25 - 30 min

ELSE PR = normal (< 25 min)

Results among a typical data:

The present model predicts the travel times right in 95,4% of all cases

Among the congested cases the figure is 32,9%

By GUHA-Similarity model, the travel times are predicted right in 98,8% of all cases. Among the congested cases the figure is 78,2%