- Original Paper
- Open Access
Model based traffic congestion detection in optical remote sensing imagery
© The Author(s) 2010
- Received: 12 January 2009
- Accepted: 23 March 2010
- Published: 2 April 2010
A new model based approach for the traffic congestion detection in time series of airborne optical digital camera images is proposed.
It is based on the estimation of the average vehicle speed on road segments. The method puts various techniques together: the vehicle detection on road segments by change detection between two images with a short time lag, the usage of a priori information such as road data base, vehicle sizes and road parameters and a simple linear traffic model based on the spacing between vehicles.
The estimated speed profiles from experimental data acquired by an airborne optical sensor - 3K camera system - coincide well with the reference measurements.
Experimental results show the great potential of the proposed method for the detection of traffic congestion on highways in along-track scenes.
- Optical remote sensing
- Image time series
- Traffic congestion
- Change detection
- Traffic model
During the past years, increasing traffic appears to be one of the major problems in urban and sub-urban areas . Traffic congestion and jams are one of the main reasons for immensely increasing transportation costs due to the wasted time and extra fuel. Conventional stationary ground measurement systems such as inductive loops, radar sensors or terrestrial cameras are able to deliver precise traffic data punctually with high temporal resolution, but their spatial distribution is still limited to selected motorways or main roads.
A new type of additional information is needed for a more efficient use of road networks. Sensors installed on aircrafts or satellites enable data collection on a large scale thus allowing wide-area traffic monitoring . Synthetic aperture radar (SAR) sensors due to their all-weather capabilities seem to be well suited for such type of applications. Ground moving target indication approaches based on the Displaced Phase Center Arrays technique are currently under investigation for airborne SAR sensors  and space borne satellites, e.g. TerraSAR-X , but still suffer from the low vehicle detection rate, quite often below 30%. Traffic monitoring from optical satellites is still limited due to the not sufficiently high spatial resolution, but the detection of vehicle queues seems to be promising . As it is shown already in [6, 7] airborne optical remote sensing technology has a great potential in traffic monitoring applications. Several airborne optical remote sensing systems are already in experimental use at the German Aerospace Center DLR, e.g. airborne 3K camera system , consisting of three digital cameras capable of acquiring three images per second, and LUMOS . Automatic detection of vehicles and estimation of their speed in sequences of optical images is still a challenge. Most known approaches are image based and still result in a too low completeness (e.g. less than 70% [10, 11]) thus being not yet suitable e.g. for the estimating of the traffic density.
In this paper we propose a new model based approach and investigate its potential for the congestion detection in airborne optical remote sensing data. Instead of detecting each individual vehicle and then estimating its speed (microscopic model) as e.g. in [10, 11] we exploit a linear vehicle density-speed relationship for a road segment (macroscopic model) to derive vehicle speed from the estimated vehicle densities in an image.
2.1 A priori information
The following a priori information: road data base (routes in two directions) , vehicle sizes for passenger cars and trucks, road parameters (number of lanes, lane width) and solar azimuth and zenith angles is used in the proposed method. The usage of the a priori information is described in the following sections in more details.
2.2 Congested traffic model
How the proposed model is used for the estimation of vehicle density in a real situation see Section 2.4.
2.3 Vehicle detection
Detection of vehicles on the road segments using image sequences is performed in the following way. First, two images with a short time lag usually few seconds (this value is derived from the constraints that a vehicle should not overlap with a previous vehicle and with itself, for more information see Sections 2.4.3 and 2.4.4) are selected, then the region of interest (tube) is defined based on the route middle and the change image is obtained with the MAD algorithm . Finally, the obtained change image—chi squared image of MAD components—is binarized and denoised, e.g. by median filter. For an example of binarized images see the lower images in Fig. 5 (b–e). Now the vehicle density can be estimated for each road segment from the binarized image defined as the ratio of the number of white pixels to the total number of pixels in the road segment.
2.4 Vehicle density estimation
There are some effects influencing the accuracy of vehicle density estimation in time series of images which are explained in more details in the following sections. For the estimation of the model parameter B see the Section 3.3.
2.4.1 Vehicle classes
Classification of all vehicles into two broad vehicle classes: passenger cars and trucks is accounted presently in the proposed approach because of the different sizes, cruising speeds and driver experiences . Proportions of vehicles in the two classes can be estimated empirically from reference data.
2.4.2 Double appearance of vehicles in a binary image
2.4.3 Not overlapping with a previous vehicle
2.4.4 Not overlapping with itself
2.4.5 Vehicle shadows
2.4.6 Lane segment length
2.4.7 Number of lanes
For roads with more than one lane the road vehicle density is a sum of separate lane densities for one road direction.
2.4.8 Halting vehicles
Halting vehicles (vehicles have not moved between two acquisitions) or very slow vehicles are not seen in change detection image and thus significantly reduce the vehicle density. Simple solution of the problem could be to increase the time lag between two acquisitions as much as possible still satisfying the condition (7) thus reducing the probability of halting vehicles. More sophisticated solution could be a combination with other methods, e.g. classification of a single image .
To confirm our idea and to validate the method several flight campaigns with the DLR airborne experimental wide angle optical 3K digital camera system operated on a Do-228 aircraft were performed. In this paper one of such experiments is presented. Since the area covered is quite large, the evaluation is performed for many road segments and can therefore be regarded as representative measures.
3.1 DLR 3K camera system
3.2 Test site and data
The motorway A8 south of Munich is one of the busiest parts of the German motorway network with an average traffic volume of around 100.000 vehicles per day. Test site was a 16 km motorway section between motorway junctions “Hofolding” and “Weyern”. On 2nd Sep. 2006, heavy traffic was expected at this section caused by homebound travelers in the direction of Munich. 3K data were acquired between 14:01 and 15:11 from 2,000 m above ground in three overflights. During each overflight, 22 image bursts were acquired each containing four consecutive images. The time difference within these bursts was 0.7 s, so that each car was monitored at least for 2.1 s. To collect the reference data each lane was manually processed that is all vehicles were detected in the images and their speed measured.
3.3 Estimation of traffic model parameter B
3.4 Congestion detection
Traffic congestion is defined usually using the average speed or the traffic density. Unfortunately, there is no unique definition and it is usually country dependent e.g. see . Having the average vehicle speed for each road segment the congestion detection is a trivial task and can be performed by a simple thresholding. For example, if the congestion is defined for speed up to 50 km/h (for motorways), then the red colored areas in Fig. 5 can be interpreted as congested ones.
First we would like to note some interesting observations when analyzing the curves in Fig. 6. The maximal speed was limited to 120 km/h for free flow traffic, but nevertheless we can see that our model, though optimal for congested conditions, can capture the right speed (see parts of the blue curve from 2,200 m to 2,700 m) because of the presence of some trucks in the images.
The overestimation of the speed in some stages of congestion (parts between 4,000 m–4,300 m and 6,000 m–6,200 m) can be explained by the behavior of the drivers who decelerate just before the congestion and thus increase spacings. The same effect occurs after the passing of congestion when the drivers begin to accelerate (part between 5,400 m and 5,500 m). In latter case, halting vehicles are distorting the estimation additionally.
By setting the speed threshold e.g. to 50 km/h we detect two congested areas (4,300–5,300 and 5,800–6,400). Further we could easily extract the following parameters: the beginning and end of congestion, length of congestion and travel times.
The performance of the proposed method is very dependent on the good quality of the geo-referencing of overlapping images and the quality of the road data base.
A priori information concerning vehicle and road parameters should be adapted very carefully to the regional traffic conditions.
For the accurate vehicle density estimation the time lag between the two image acquisitions should be selected according to the constraints presented in the paper.
Image based methods (microscopic model) perform normally better for a higher resolution (less than 30 cm pixel spacing ), thus the aircraft flight height should be low or equivalently one should take into account the reduced image coverage. It seems that the proposed model based method should not be so sensitive to the resolution because it is working on the macroscopic model level.
To overcome the problem of halting vehicles the investigation of more sophisticated solutions, for example a combination with other methods, e.g. classification of a single image, is planned.
Further experiments are planned to test the approach for off-nadir scenes and in the cities during different environmental conditions.
Another research direction is aiming to derive other traffic parameters such as density and flow.
Information derived from remote sensing sensors about the traffic flow can be used for various monitoring applications e.g. as complimentary information in already existing traffic monitoring systems, extracting information in regions with special interests, emergency situations and so on .
A new traffic congestion detection approach for image time series acquired by the airborne optical 3K camera system is introduced. It allows us to derive one of the main traffic parameters—the average speed—and the vehicle density as an intermediate product. Other parameters such as the beginning and end of congestion, length of congestion and travel times could be derived easily on request. The method is based on the vehicle detection on the road segment by change detection of two images with a short time lag, usage of a priori information and a simple traffic model. Experimental results show the great potential of the proposed method for the detection of traffic congestion on highways in along-track scenes. The estimated speed profiles coincide qualitatively and quantitatively quite well with the reference measurements.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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