TRAFFIC CONGESTION PARAMETER ESTIMATION IN TIME SERIES OF AIRBORNE OPTICAL REMOTE SENSING IMAGES

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1 TRAFFIC CONGESTION PARAMETER ESTIMATION IN TIME SERIES OF AIRBORNE OPTICAL REMOTE SENSING IMAGES G. Palubnskas *, F. Kurz, P. Renartz German Aerospace Center DLR, 8 Wesslng, Germany Gntautas.Palubnskas@dlr.de KEY WORDS: Traffc congeston, traffc model, change detecton, mage tme seres, optcal remote sensng ABSTRACT In ths paper we propose a new model based traffc parameter estmaton approach n congested stuatons n tme seres of arborne optcal remote sensng data. The proposed approach s based on the combnaton of varous technques: change detecton, mage processng and ncorporaton of a pror nformaton such as road network, nformaton about vehcles and roads and fnally a traffc model. The change detecton n two mages wth a short tme lag of several seconds s mplemented usng the multvarate alteraton detecton method resultng n a change mage where the movng vehcles on the roads are hghlghted. Further, mage processng technques are appled to derve the vehcle densty n the bnarzed change mage. Fnally, ths estmated vehcle densty s related to the vehcle densty, acqured by modellng the traffc flow for a road segment. The model s derved from a pror nformaton about the vehcle szes and road parameters, the road network and the spacng between the vehcles. Then, the modelled vehcle densty s drectly related to the average vehcle velocty on the road segment and thus the nformaton about the traffc stuaton can be derved. To confrm our dea and to valdate the method several flght campagns wth the DLR arborne expermental wde angle optcal K dgtal camera system operated on a Do-8 arcraft were performed. Experments are performed to analyse the performance of the proposed traffc parameter estmaton method for hghways and man streets n the ctes. The estmated velocty profles concde qualtatvely and quanttatvely qute well wth the reference measurements.. INTRODUCTION Durng the past years, ncreasng traffc appears to be one of the major problems n urban and sub-urban areas. Traffc congeston and jams are one of the man reasons for mmensely ncreasng transportaton costs due to the wasted tme and extra fuel. Conventonal statonary ground measurement systems such as nductve loops, radar sensors or terrestral cameras are able to delver precse traffc data punctually wth hgh temporal resoluton, but ther spatal dstrbuton s stll lmted to selected motorways or man roads. A new type of nformaton s needed for a more effcent use of road networks. Remote sensng sensors nstalled on arcrafts or satelltes enable data collecton on a large scale thus allowng wde-area traffc montorng. Synthetc aperture radar (SAR) sensors due to ther all-weather capabltes seem to be well suted for such type of applcatons. Ground movng target ndcaton approaches based on the Dsplaced Phase Center Arrays technque are currently under nvestgaton for arborne SAR sensors and space borne satelltes, e.g. TerraSAR-X, but stll suffer from the low vehcle detecton rate, qute often below 0% (Meyer 007). Traffc montorng from optcal satelltes s stll lmted due to the not suffcently hgh spatal resoluton, but the detecton of vehcle queues seems to be promsng (Letloff 006). As t s shown already n (Renartz 006, Hnz 008) arborne optcal remote sensng technology has a great potental n traffc montorng applcatons. Several arborne optcal remote sensng systems are already n expermental use at the German Aerospace Center DLR, e.g. arborne K camera system, consstng of three dgtal cameras capable of acqurng three mages per second (Kurz 007), and LUMOS (Ernst 00). Automatc detecton of vehcles and estmaton of ther veloctes n sequences of optcal mages s stll a challenge. Most known approaches are mage based and stll result n a too low completeness (e.g. less than 70%) thus beng not yet sutable e.g. for the estmatng of the traffc densty and flow (Rosenbaum 008). In ths paper we propose a new model based approach and nvestgate ts potental for the traffc parameter estmaton n congested stuatons n sequences of arborne optcal remote sensng data. Instead of detectng each ndvdual vehcle and then estmatng ts velocty (mcroscopc model) as e.g. n (Rosenbaum 008) we explot a lnear vehcle densty-speed relatonshp for a road segment (macroscopc model) to derve vehcle veloctes from the estmated vehcle denstes n an mage. The paper s organzed as follows: frst, the proposed method s descrbed n the Secton, then, the results of experments and dscussons are presented n Secton, followed by conclusons and references.. APPROACH Our approach for the traffc parameter estmaton n sequences of optcal mages s based on the modellng of traffc flow on the road segments and thus allows the drect dervaton of the requred traffc parameters from the data, such as the vehcle densty and average velocty. Further, other traffc nformaton, lke the exstence of congeston, the begnnng and end of congeston, the length of congeston, actual travel tmes, and so on can be easly extracted. The proposed method s based on the combnaton of varous technques: change detecton, mage processng and ncorporaton of a pror nformaton such as road network, nformaton about vehcles and roads and fnally a traffc model. The change detecton n two mages wth a short tme lag s mplemented usng the Multvarate alteraton * Correspondng author.

2 detecton (MAD) method (Nelsen 007) resultng n a change mage where the movng vehcles on the roads are hghlghted. Image processng technques can be appled to derve the vehcle densty n the bnarzed change mage. Ths estmated vehcle densty can be related to the vehcle densty, acqured by modellng the traffc flow for a road segment. The model s derved from a pror nformaton about the vehcle szes and road parameters, the road network and the spacng between the vehcles.. Congested traffc model Numerous nvestgatons on real traffc data show that under congested condtons the followng assumpton s true: a class of vehcle s spacng s a lnear functon of the speed of all vehcles S = B g( v) + L, () where spacng S s the front-to-front vehcle dstance n meter, B s a dmensonless parameter of the model, functon g(v) transforms velocty (km/h) nto meters, e.g. g(00 km/h) = 00 m, L s the vehcle length n meter and s the vehcle class, e.g. passenger car or truck. Parameter B can be nterpreted n the followng way: for B=0.5 and L=0 the formula () means for all drvers a well-known rule of thumb safe dstance = half speedometer readng n metres. As already mentoned ths model well descrbes a congested traffc. The B value s rangng normally between 0.5 and.0 (for more detals see (Palubnskas 009)). Now the traffc densty can be calculated as km D(# vehcles per km) =, () S where S = p S p =, p s a proporton of vehcle class and. Thus for densty calculaton n () the weghted mean value of vehcle spacngs s used. The modelled vehcle densty s drectly related to the average vehcle velocty on the road segment and thus the nformaton about the traffc stuaton can be derved. The flow dagram of the proposed method s shown n Fg.. Detecton of vehcles on road segment A pror nformaton Traffc model d=f(v) Model Inverson Estmated vehcle densty d Average velocty per road segment v Fgure. Flow dagram of the proposed traffc parameter extracton method Detaled descrpton of the proposed method s provded n (Palubnskas 009).. EXPERIMENTS To confrm our dea and to valdate the method several flght campagns wth the DLR arborne expermental wde angle optcal K dgtal camera system operated on a Do-8 arcraft were performed. In ths paper one of such experments s presented. Snce the area covered s qute large, the evaluaton s performed for many road segments and can therefore be regarded as representatve measures.. DLR K camera system The K camera system ( Kopf = head ) conssts of three non-metrc off-the-shelf cameras (Canon EOS Ds Mark II, 6 MPxel). The cameras are arranged n a mount wth one camera lookng n nadr drecton and two n oblque sdeward drecton, whch leads to an ncreased FOV of max 0 / n across track/flght drecton. The camera system s coupled to a GPS/IMU navgaton system, whch enables the drect georeferencng of the K optcal mages. Based on the use of 50 mm Canon lenses, the pxel sze at a flght heght of 000 m above ground s 5 cm and the mage array covers.8 km n wdth. The pxel sze ncreases up to 50 cm and the swath wdth up to 8 km for a flght heght of km.. Test ste and data The motorway A8 south of Munch s one of the busest parts of the German motorway network wth an average load of around vehcles per day. Test ste was a 6 km motorway secton between motorway junctons Hofoldng and Weyarn. On nd Sep. 006, heavy traffc was expected at ths secton caused by homebound travellers n the drecton of Munch. Three K data takes were acqured between :0 and 5: from 000m above ground n three over flghts. Durng each over flght, mage bursts were acqured each contanng four consecutve mages. The tme dfference wthn these bursts was 0.7 s, so that each car was montored at least for. s. To collect the reference data each lane was manually processed, that means all vehcles were detected n the mages by vsual nterpretaton and ther veloctes measured.. Estmaton of traffc model parameter B The unknown model parameter B (under assumpton of a mean vehcle length of.7 m for passenger cars) was analyzed for each lane and drecton separately thus resultng n a total evaluaton length of 5 km. Due to the tme of mage acquston on Saturday afternoon there were very few trucks n ths part of the road so ther nfluence was neglected. The estmated values for the traffc model parameter B are plotted n Fg. separately for the rght and left lanes for the three lane road. The analyss resulted n an average B = 0.76 (wth standard devaton (STD) equal 0.8) for the left lane and the average B =.8 (STD = 0.88) for the rght lane and total B s approxmately equal to (STD = 0.6). From Fg. t can be deduced that a constant value for B can be assumed except for very low veloctes under 5 km/h (n ths case the outlers from our model are most probable but are nsgnfcant because of the usage of other approaches (Palubnskas 009)). Parameter values for the low densty traffc must be treated very carefully because our model s desgned for the congested traffc. For free flow traffc the values for B can be larger.

3 . Traffc parameter extracton Results of the traffc parameter extracton on the test ste are shown n Fg.. In ths fgure, (a) s the orgnal mosac mage wth reference velocty measurements plotted. It s dvded nto four parts marked n blue coloured frames. These mage frames are dsplayed from left to rght n the followng fgures (b-e): the orgnal mage (upper mage) and velocty profles for separate road drectons plotted on the change detecton mage (lower mage). Traffc congeston s defned usually usng the average velocty or the traffc densty. Unfortunately, there s no unque defnton and t s usually country dependent. Havng the average vehcle velocty for each road segment the congeston detecton s a trval task and can be performed by a smple threshold. For example, f the congeston s defned for veloctes up to 50 km/h, then the red coloured areas n Fg. can be nterpreted as congested ones. For the quanttatve nterpretaton velocty profles estmated wth our approach are compared graphcally to the reference measurements n Fg...5 Dscussons Frst we would lke to note some nterestng observatons when analyzng the curves n Fg. The maxmal velocty was lmted to 0 km/h for free flow traffc, but nevertheless we can see that our model, though optmal for congested condtons, can capture the rght velocty (see parts of the blue curve from 00 m to 700 m) because of the presence of some trucks n the mages. The overestmaton of the velocty n some stages of congeston (parts between 000 m - 00 m and 6000 m m) can be explaned by the behavour of the drvers who decelerate just before the congeston and thus ncrease the spacng. The same effect occurs after end of congeston when the drvers begn to accelerate (part between 500 m and 5500 m). Moreover, haltng vehcles are dstortng the estmaton addtonally. By settng the velocty threshold e.g. to 50 km/h we detect two congested areas ( and ). Further we could easly extract the followng parameters: the begnnng and end of congeston, length of congeston and travel tmes. The performance of the proposed method s very dependent on the good qualty of the geo-referencng of overlappng mages and the qualty of the road data base. A pror nformaton concernng vehcle and road parameters should be adapted very carefully to the regonal traffc condtons. For the accurate vehcle densty estmaton the tme lag between the two mage acqustons should be selected accordng to the constrants presented n the paper. Image based methods (mcroscopc model) perform normally better for a hgher resoluton (less than 0 cm pxel spacng (Rosenbaum 008)), thus the arcraft flght heght should be low or equvalently one should take nto account the reduced mage coverage. It seems that the proposed model based method s not very senstve to the resoluton because t s workng on the macroscopc model level. To overcome the problem of haltng vehcles the nvestgaton of more sophstcated solutons, for example a combnaton wth other methods, e.g. classfcaton of a sngle mage, s planned. Further experments are planned to test the approach for offnadr scenes and n the ctes durng dfferent envronmental condtons. Another research drecton s amng to derve other traffc parameters such as traffc densty and traffc flow.. CONCLUSIONS A new traffc congeston detecton approach for mage tme seres acqured by the arborne optcal K camera system s ntroduced. It allows us to derve one of the man traffc parameters - the average velocty - and the vehcle densty as an ntermedate product. Other parameters such as the begnnng and end of congeston, length of congeston and travel tmes could be derved easly on request. The method s based on the vehcle detecton on the road segment by change detecton of two mages wth a short tme lag, usage of a pror nformaton and a smple traffc model. Expermental results show the great potental of the proposed method for the extracton of traffc parameters on hghways n along-track scenes. The estmated velocty profles concde qualtatvely and quanttatvely qute well wth the reference measurements. 5. ACKNOWLEGMENT The authors would lke to thank colleagues Erch Bogner and Rolf Stätter for ther efforts n plannng the flght campagn, data acquston and data processng. Specal thanks to our tranee Mantas Palubnskas for the mplementaton and valdaton of the traffc model. 6. REFERENCES Ernst, I., Sujew, S., Thessenhusen, K.U., Hetscher, M., Raßmann, S. and Ruhé, M., 00. LUMOS - arborne traffc montorng system. In: Proceedngs of 6th IEEE Internatonal Conference on Intellgent Transportaton Systems, Shangha, Chna, 00. Hnz, S., Lenhart, D. and Letloff, J., 008. Traffc extracton and charactersaton from optcal remote sensng data. The Photogrammetrc Record, (), pp. -0. Kurz, F., Müller, R., Stephan, M., Renartz, P. and Schroeder, M., 007. Calbraton of a wde-angle dgtal camera system for near real tme scenaros. In: Proc. ISPRS Hannover Workshop Hgh Resoluton Earth Imagng for Geospatal Informaton, Hannover, Germany, May 9 June, 007. Letloff, J., Hnz., S. and Stlla, U., 006. Detecton of vehcle queues n Quckbrd magery of cty areas. Photogrammtre Fernerkundung Geonformaton,, pp Meyer, F., Hnz, S., Müller, R., Palubnskas, G., Laux, C. and Runge, H., 007. Towards traffc montorng wth TerraSAR-X. Canadan Journal of Remote Sensng, (), pp Nelsen, A.A., 007. The Regularzed Iteratvely Reweghted MAD Method for Change Detecton n Mult- and Hyperspectral Data. IEEE Transactons on Image Processng, 6(), pp Palubnskas, G., Kurz, F. and Renartz, P., 009. Model based traffc congeston detecton n optcal remote sensng magery. European Transport Research Revew, submtted. Renartz, P., Lachase, M., Schmeer, E., Krauß, T. and Runge, H., 006. Traffc Montorng wth Seral Images from Arborne

4 Cameras. ISPRS Journal of Photogrammetry and Remote Sensng, 6, pp an arborne wde angle camera system. European Transport Research Revew, pages. Rosenbaum, D., Kurz, F., Thomas, U., Sur, S. and Renartz, P., 008. Towards automatc near real-tme traffc montorng wth Estmated values for the traffc model parameter B Rght lane Left lane Parameter B Velocty km/h Fgure. Estmated values for the traffc model parameter B for left and rght lanes separately Velocty (km/h) Velocty profles for one drecton of A8 hghway rangng from Weyarn to Holzkrchen left lane (reference) rght lane (reference) one drecton (estmated) Dstance n meter Fgure. Velocty profles estmated wth the proposed approach (blue color) vs the reference measurements (red left lane, green rght lane). Plot parts separated by dashed vertcal lnes (numbered) correspond to mage parts shown n Fg.

5 (a) (b) (c) (d) (e) 80 km/h Fluent flow Fgure. Example of the traffc congeston detecton on A8 hghway between Munch and Salzburg for K sensor data acqured durng ADAC flght campagn on.9.006: (a) the orgnal mosac mage wth reference velocty measurements plotted and (b-e) correspondng blue coloured frames of the orgnal mosac mage (upper mage) and velocty profles for separate road drectons plotted on the change detecton mage (lower mage)