Research Trends and Results

Traffic Volume Survey Using Image Processing Technology
Hirotaka SEKIYA, Hiroyoshi HASHIMOTO, Keiji MOROTA, Susumu TAKAMIYA
Traffic Engineering Division, Road Department(Key words) Road traffic, Traffic volume survey, Image processing technology, International cooperation

1. Introduction
The primary method for measuring traffic volume onarterial roads in Indonesia is the combined use of an
inductive loop and a piezoelectric sensor (LPS) as
shown in Figure 1. However, this method lacks
accuracy. Obviously, an LPS device cannot detectmotorbikes that do not pass over it. Also, whenseveral motorbikes are on the LPS device at the sametime, they are counted as one vehicle or are notcounted at all. In terms of durability, the LPS device
often fails due to damage caused by overloaded
vehicles. The labor and cost for monitoring andmaintaining the sensors are considerable.
In this context, the Institute of Road
Engineering (IRE) in Indonesia and the National
Institute for Land and Infrastructure Management(NILIM) in Japan launched in 2010 a joint study todevise an optimal method for measuring traffic
volume in Indonesia by using image processing
technology (IPT). This is one of the activities based ona memorandum concerning cooperative activities thatwas concluded between the IRE and the NILIM in2009.

Analysis area
2nd check line
1st check line

Inductive loop
Inductive loop
Piezoelectric Sensor

Figure 1 Configuration of LPS
6m

5m

Figure 2 IPT

8m
70 deg.

70 deg.

70 deg.
8m
45 deg.

30 deg.

60 deg.

90 deg.

75 deg.

Figure 3 Camera settings
Error ratio
30%

Motorbikes
20%

10%

Cars

0%

30 deg.

45 deg.

60 deg.

75 deg.

90 deg. Angle

Ncar = 16
Nbike= 59Ncar = 23
Nbike= 43

Ncar = 18
Nbike= 65

Ncar = 16
Nbike= 62

Ncar = 18
Nbike= 70

60 deg.

75 deg.

8m

2.Traffic measurement using IPT
We used an IPT device that involves the spatio
temporal Markov random field model (S-T MRFmodel), which was proposed by Kamijo et al. 1). As
shown in Figure 2, when the device detects a moving
object within the analysis area in the image, the device
starts following it. If the object passes the first checkline and the second check line in this order, it iscounted as a vehicle. The device also measures thesize of the moving object when it reaches the firstcheck line, and can thus classify vehicle types.
3. Analyses and Results
By analyzing the traffic images obtained from fieldsurveys on an arterial road in Bandung, Indonesia
under several traffic conditions and camera settings(Figure 3), we verified the applicability of the IPT
device to traffic volume survey in Indonesia andgained the following knowledge:
 The error ratio of traffic volume measured by theIPT device was on average 5.3% for cars and 13.1%
for motorbikes.
 The error ratio for motorbikes tends to increase by30 deg.

45 deg.

90 deg.

Figure 4 Error ratios with different camera angles

10.8% in congested traffic.
 Installing the camera at a greater height (up to 8 m)
makes it possible to capture a longer movement ofvehicles in an image, which helps reduce the errorratio.
 An angle of 60 degrees from the vertical is optimum
to measure traffic volume accurately underconditions where the camera is 8 m from the groundand the traffic is not congested (Figure 4).
Reference1) Kamijo, S., Matsushita, Y., Ikeuchi, K., Sakauchi,
M. (2000) Occlusion robust vehicle tracking
utilizing the spatio-temporal Markov random fieldmodel. Proceeding of Proceedings 15th
International Conference on Pattern Recognition,
vol.1, pp.140-144.