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Investigation into traffic flows on high intensity streets of Vilnius City.

1. Introduction

Road transport is one of the essential factors to determine the social and economic development of Vilnius and the whole Lithuania. Vilnius city is and will remain the largest attraction point and a source of urban, suburban and international transportation. In recent years, the major problems of road maintenance and condition have been related to an increase in a number of transportations by heavy vehicles and insufficient financing allocated for the road sector and the required road maintenance and repair programs. The earlier built roads and streets are not able to carry increased loads and growing traffic volume. In Vilnius city, traffic counts are carried out every year. For this purpose, stationery and mobile automatic counters are used.

Vehicles are divided into 6 classes:

--motorcycles;

--passenger cars, minibuses;

--passenger cars with a trailer;

--light trucks, heavy weight vehicles without trailers or semi-trailers (three-axle);

--heavy weight vehicles (four-axle, five-axle);

--buses.

The division of vehicles is made according to the number of vehicle axles and the distance between the axles which is not a very accurate process.

The main factor of vehicle traffic is definitely vehicle wheel loads directly affecting road pavement and street/road pavement structure (Cygas et al. 2008; Sivilevicius and Sukevicius 2007; Gopalakrishnan 2008; Gopalakrishnan and Khaitan 2010). This impact depends on vehicle weight, the number of axles, a type of wheels, tire pressure and even on a type of suspension. The dynamics of vehicle-generated loads on the pavement and their distribution are determined by traffic volume, traffic flow composition, its speed and the distribution of vehicles in time (Cygas et al. 2008; Lee et al. 2005; Hugo et al. 2007; Zavadskas et al. 2008; Beljatynskij et al. 2009; Junevicius and Bogdevicius 2007, 2009; Gopalakrishnan 2008; Paslawski 2008; Sliupas 2009; Gopalakrishnan and Khaitan 2010).

Vehicle load directly transferred to the road pavement depends on:

--axle load,

--the number of wheels on the axle (usually--2 or 4),

--air pressure in tires.

The axle load of an unloaded vehicle depends on the mark of a vehicle and its weight, the weight of transported goods, the number of axles and configuration. In Lithuania, the maximum permissible axle load is 11.5 tonnes and is regulated by the Law on the Financing of Road Maintenance and Development Program. However, in reality, due to a lack of effective control, heavy weight vehicles are frequently overloaded; axle load is exceeded and can reach even 20 tons or more.

Single wheels make a larger negative effect on the road pavement compared to double wheels. Based on certain estimations, this effect is larger by 1.2 to 4 times. Air pressure in vehicle tires can vary within large limits (can reach up to 950 kPa). The higher is air pressure the higher is wheel pressure transferred to the road pavement, the stronger is an impact on the road pavement (Salama et al. 2006) and the faster is pavement degradation.

One of the main criteria describing the impact of a vehicle on the road pavement is the number of equivalent standard axle loads (ESALs) which is one of the basic indices when applying the road pavement management model used for the economic justification of roads.

Pavement degradation processes are especially accelerated by the traffic-related factors:

--for earlier designed pavements, lower limit axle loads were applied which do not already meet the axle loads of heavy weight vehicles;

--the number of heavy weight vehicles within traffic flow has increased;

--axle loads of heavy weight vehicles have been continuously increasing;

--heavy weight vehicles are used to be overloaded.

2. Methods for Measuring Traffic Volume

Traffic volume on city streets is measured by the following State Enterprise Transport and Road Research Institute (TRRI) created methods:

--visually;

--with the help of measuring devices (loop and tube detectors, counters and video detectors (Ozkurt and Camci 2009), see Fig. 1.

[FIGURE 1 OMITTED]

A visual traffic volume determination method is the most accurate way enabling to attribute each passing vehicle to the appropriate vehicle class. This method is also the cheapest one since it is not necessary to buy or install any additional equipment to carry out measurements. However, visual recording of vehicles is not convenient at high-speed on the main streets of Vilnius and other large Lithuanian cities with the most intensive traffic flows. Vehicles are classified into 11 classes.

When the measurements of traffic intensity are in short-term variations (individual or periodic measurements), annual average daily traffic intensity (AADTI) is calculated applying the methodology of data on the annual average daily traffic intensity of short-term measurement (TRRI methodology). This methodology is used when a single measurement period is not longer than 12 hours or when measurement time is one week. Having traffic intensity data on short-term measurements, we can calculate the traffic intensity of the day, the traffic intensity of the week and annual average daily traffic intensity. The daily traffic intensity (DTI) is calculated by Eq (1):

[I.sub.D] = N x [K.sub.D], (1)

where: [I.sub.D] is the DTI of the measured day, car/day; N is the number of cars moved during the measured period, car; [K.sub.D] is the coefficient of the measured day traffic intensity.

If traffic intensity was measured over a week without interruption, then, average weekly daily traffic intensity (AWDTI) is calculated by Eq (2):

[I.sub.W] = 1/7 [7.summation over (i=1)] [I.sub.Di], (2)

where: [I.sub.W] is AWDTI, car/day; [I.sub.Di] is the traffic intensity of the i-th day of the measured week, car/day.

If traffic intensity lasted less than one week, then, AWDTI is calculated by Eq (3):

[I.sub.W] = 1/n [n.summation over (i=1)] [I.sub.Di] x [K.sub.Wi], (3)

where: [I.sub.Di] is the traffic intensity of the i-th day of the week, car/day; [K.sub.Wi]--the coefficient of week daily traffic intensity, n--the number of the measured days.

Annual average daily traffic intensity is calculated by Eq (4):

[I.sub.Y] = 1/n [n.summation over (i=1)] [I.sub.Wi] x [K.sub.Yi], (4)

where: [I.sub.Y] is AADTI, car/day; [I.sub.Wi] is week average daily traffic intensity of the i-th measured week, car/day; [K.sub.Yi] is the coefficient of the traffic intensity of the week in a year, n is the number of the measured weeks during the year.

A loop detector is an inductive loop buried in a 6 mm wide and 40-70 mm deep groove cut into asphalt concrete pavement and covered with bitumen. During operation, a frequency signal of 100 kHz is transferred through the loop. With any vehicle passing over the loop, it functions as a metal cord of the loop and, thus, produces changes in the inductive resistance of the loop. Change in resistance is a signal for the detector about the passing vehicle. In order to detect not only a number of vehicles but also their speed and driving direction within vehicle classification regime, it is necessary to install 2 loops in each traffic lane at a certain fixed distance (usually 2 m) from each other. The use of loop detectors requires stationery traffic counting sites. At present, the counters with inductive loops 'Marksman 660' dividing vehicles into 6 classes (EUR6) and 'C&A loop profiler' with signal form analysis dividing vehicles into 10 classes are used for traffic count.

A rubber tube is an elastic one laid across the street. One of his ends is blocked, whereas the other is put on a metal tube taken out in front of the device. With a vehicle passing over the tube, containing air pressure increases and this is the signal for a counter to register the passing vehicle. Counters with rubber tubes are usually used as easily transported devices for a short-term traffic count. Rubber tubes give a possibility of dividing vehicles into 13 classes according to GRO3--EUR13 classification the criteria of which include the length of a vehicle, the distance between the axles and the number of axles. At present, traffic counts are carried out using 'Marksman 400' counter-classifier dividing vehicles into 13 classes.

Another group of the currently used counters is those with microwave sensors. These are mobile counters used on the sites making difficulties with installing other types of counters and traffic count is carried out for more than 1 day. They are mainly used on regional and low-volume national roads. A counter sends a microwave beam across the road and receives its reflections from vehicles. A microwave counter is installed close to the road at a height of 1.5-6 m above the pavement on a round post (usually on traffic sign). At present, traffic counts are carried out by SDR microwave counter.

Video detectors (Fig 1 c) are mainly installed and used at street intersections. Using video detectors makes possible not only to detect traffic volume but also by monitoring a real situation to analyze flows, their speed and loading of traffic lanes (Gao et al. 2009; Ozkurt and Camci 2009). Traffic flows are recorded using a method of video detection or laying induction loops on the asphalt pavement. Video detectors are mounted at a 6-8 m height over the carriageway. Using special software with the help of a personal computer, virtual loops are created on traffic lanes where the passing vehicles are detected and summed up. Virtual loops are created on each lane, i.e. 8 virtual loops are created on 4 traffic lanes. Information about traffic flows is collected 24 hours a day.

3. Receiving and Processing Data on Traffic Intensity

To collect data on transport intensity applying the visual method, each vehicle is recorded at its predefined class. Using the visual method, transport is classified into 11 classes. All cars crossing a measuring post are noted and later summed up calculating daily traffic intensity (DTI) Eq (1), average weekly daily traffic volume (AWDTI) Eq (2, 3) or average annual daily traffic intensity (AADTI) Eq (4).

While using loop or tube detectors, data on transport intensity are fixed and recorded into the memory of an automatic counter-classifier connected to the detector. These devices (counters) work on an electronic chip inside a microprocessor with a specific program. Every crossed car is not only taken over the counter but also put into a certain class. Classification criteria cover length bands and chassis level (the height between the pavement surface and the vehicle floor). Vehicle classification into classes (6 classes, 11 classes, 13 classes) depends on the use of a counter (Chapter 2). All management of information determination for a counter-classifier is done connecting a personal computer, because the counter-classifier has no any controls. After scanning data on transport intensity from the counter, they must be processed, summed up and only then used for the needed purpose (in a road maintenance program, for calculating and assessing air pollution, traffic safety by directing traffic, etc.).

When using video cameras, data on the intensity of the crossed transport is send to the central computer where all information is summed up. In this way, recorded passing traffic is not classified receiving only vehicle intensity.

4. Measurements of Traffic Volume on High-Speed and Main Streets of Vilnius City and in Vilnius Region

Traffic volume on all main, national and partly regional Lithuanian roads is counted every year. For this purpose, stationary or mobile automatic counters are used. With the increasing number of the registered vehicles in the Republic of Lithuania, traffic volume on the roads of national significance has been also increasing. Traffic counts on the roads of national significance are carried out by the TRRI. Accounting for traffic intensity on the main, national and regional roads was carried out in stationary permanent measurement posts (continuous mode) and stationary periodic measurement posts (for 1 week 2 times a year, sometimes for 1 week 4 times a year, for 3 hours 2 times a year or once a year for 3 hours).

Fig. 2 provides changes in traffic volume on the roads of national significance since 2000. The graph shows that for the period from 2000 to 2008, the volume of traffic was growing. In 2009, it became lower, though, if compared to 2000, has increased by 59%.

Data on traffic volume in Vilnius region (Fig. 3), i.e. on roads A1 Vilnius-Kaunas-Klaipeda (the distance of 15.10 km was measured), A2 Vilnius-Panevezys (the distance of 8.05 km was measured), A3 Vilnius-Minsk (the distance of 7.91 km was measured), A4 Vilnius-Varena-Hrodna (the distance of 14.50 km was measured), A14 Vilnius-Utena (the distance of 10.86 km was measured), A15 Vilnius-Lida (the distance of 10.01 km was measured), A16 Vilnius-Prienai-Marijampole (the distance of 16.37 km was measured), 101 Vilnius-Shumsk (the distance of 8.09 km was measured), 102 Vilnius-Svencionys-Zarasai (the distance of 16.80 km was measured), 103 Vilnius-Polotsk (the distance of 15.23 km was measured) is collected by TRRI.

The measurements of traffic volume in the above mentioned measuring stations showed that for the period 2005-2009, the total average traffic volume was increasing on all roads as indicated in Fig. 4.

Variation in traffic intensity measuring posts during the measured period in Vilnius region is indicated in Table 1.

In 2006-2007, Road Research Laboratory of the Road Department of Vilnius Gediminas Technical University installed stationery traffic volume measuring stations on 11 high-speed and main streets of Vilnius city. In total, 12 permanent traffic volume measuring stations were installed (Fig. 5). The stations were erected on 2 sections in Laisves avenue.

Measurements were started straight after their installation on 25 April 2007. Counter-classifier 'Marksman 660' is maintained for one week in each stationary installed measuring post. After a week of collecting data about passing transport vehicles, they are loaded into a personal computer and then a counter-classifier transports them into another stationary installed measuring post. Data on traffic intensity in one stationary post (there are 12 stationary posts in Vilnius city) is measured 4 times per year (once per season). The results of measurements carried out in 2007-2009 are given in Fig. 6.

[FIGURE 3 OMITTED]

Estimations have showed that on average traffic volume has not increased in all street sections as in some sections it has significantly decreased Table 2.

Investigations by (Seo and Kang 2006; Park et al. 2005, 2008; Sivilevicius and Petkevicius 2002; Romero and Lozano 2006; Butkevicius and Petkevicius 2005; Salama et al. 2006) have showed that vehicle wheel pressure on the road pavement and pavement structure is the most important load to be considered in road design. Calculating traffic intensity in this article, truck intensity is separated and summed up due to their higher axle loads. Data on truck intensity is used for making tests on truck impact on the road surface and its construction. Fig. 7 shows that for the period 2005-2009, truck intensity amounted from 3% to 19% of total traffic intensity in stationary posts in Vilnius region and in Vilnius city stationary posts. Fig. 8 indicates truck intensity for the period 2007-2009 varying from 7% to 23%.

[FIGURE 5 OMITTED]

The estimation and analysis of truck intensity shows it is higher on the streets of Vilnius city than that measured in the stations on the roads of Vilnius region. The researched situation of traffic intensity in Vilnius is higher due to the increased frequency of public transport (busses, trolley busses) which is included in the total amount of heavy traffic intensity. When choosing road A1, Vilnius city can be accessed through Savanoriu ave. During the measured periods in 2007-2009, truck intensity in the station on Savanoriu ave was 43% higher in 2007, 40% in 2008 and 56% in 2009 than that recorded in the station of road A1 (installed in 15.10 km). When driving on road A2, Vilnius city can be reached through Ukmerges str. where truck intensity in the post was 48% higher in 2007, 46% in 2008 and 52% in 2009 rather than that recorded in the station of road A2 (installed in 8.05 km). When driving on road A3, Vilnius city can be reached through Minsk Road where truck intensity in the post were 27% higher in 2008 and 41% in 2009 than that recorded in the station of road A3 (installed in 7.91 km). If driving on road A14, Vilnius city can be reached through Gelezinio Vilko str. where truck intensity in the post were 62% higher in 2007, 80% in 2008 and 51% in 2009 rather than that recorded in the station of road A14 (installed in 10.86 km). While driving on road No.102 Vilnius city can be reached through Kareiviu and Antakalnio streets where average truck intensity in the posts were 77% higher in 2007, 81% in 2008, 80% in 2009 than that recorded the station of road No.102 (installed in 16.80 km).

5. Conclusions

Data about traffic volume and traffic composition is very important for the design, estimation and determination of repair priorities of city street pavements, and therefore must be as accurate as possible. Since traffic volume on Vilnius streets is very different, it is not recommended to use only one of the above described methods for measuring traffic volume. A visual measuring method is not suitable for the streets with a high traffic volume as it would be too expensive to install loop detectors and to erect stationery measuring stations on all city streets. Therefore, for the measurements of traffic volume on the streets of Vilnius city, all measuring methods are used.

The analysis of investigations on traffic volume showed that the total traffic volume in 2000-2008 was increasing in the whole territory of Lithuania as well as in Vilnius region (on the roads entering Vilnius city). However, in 2009 compared to 2008, traffic volume was decreasing on the roads of national significance, in Vilnius region and Vilnius city. The conducted investigations showed that in 2007-2009, changes in traffic volume on Vilnius city streets were very different: on some streets it decreased from 1.28% to 23.13% (Ukmerges str., Laisves ave (2), Olandu str., Kareiviu str., Gelezinio Vilko str., Oslo str.), whereas on the other streets, it increased from 2.46% to 36.29% (Antakalnio str., Minsk Road, Savanoriu ave, Laisves ave (1), Dariaus and Gireno str., T. Narbuto str.). Changes in traffic volume in Vilnius region were not that big: traffic volume on roads No.102, A4, A16, A14, A1, A15, No. 101 decreased from 1.00% to 6.61% and on roads A3, No.103, A2, increased from 4.05% to 143.61%. The total decrease of traffic volume was influenced by the continuing economic crisis and changed directions to transit and local traffic flows.

Since truck traffic causes degradation on the road pavement and its structure because of repetitive high axle loads, traffic intensity was analyzed as a separate case. The analysis of truck intensity showed it was higher in measuring posts on the streets of Vilnius city rather than in the posts on the roads of Vilnius region in 2007-2009. However, while examining truck intensity in separate measuring posts for the same period (on the roads of Vilnius region and streets of Vilnius city), truck intensity decreased. Changes in traffic volume in Vilnius region were not that big: traffic volume in the post on road A1 decreased to 29% and on Gelezinio Vilko str. (in Vilnius city)--to 26%. The total decrease of truck traffic volume like total traffic was influenced by the same reasons--continuing economic crisis and changed directions to transit and local traffic flows.

doi: 10.3846/transport.2010.30

Received 26 May 2010; accepted 19 July 2010

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Laura Ziliute (1), Alfredas Laurinavicius (2), Audrius Vaitkus (3)

Dept of Roads, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

E-mails: (1) laura.ziliute@vgtu.lt; (2) alfredas.laurinavicius@vgtu.lt; (3) akml@vgtu.lt
Table 1. Variation in traffic volume for the
period 2005-2009 on the roads of Vilnius regio

Roads of Vilnius zone                 Consideration period 2005-2009

                                      Increase, %       Decrease, %

A1 (measuring post in 15.10 km)          14.15              --
A2 (measuring post in 8.05 km)          228.29              --
A3 (measuring post in 7.91 km)           41.25              --
A4 (measuring post in 14.50 km)          19.77              --
A14 (measuring post in 10.86 km)         18.29              --
A15 (measuring post in 10.01 km)         32.02              --
A16 (measuring post in 16.37 km)         29.94              --
No.101 (measuring post in 8.09 km)       30.94              --
No.102 (measuring post in 16.80 km)      14.80              --
No.103 (measuring post in 15.23 km)     324.66              --

Roads of Vilnius zone                 Consideration period 2007-2009

                                      Increase, %       Decrease, %

A1 (measuring post in 15.10 km)           --               5.63
A2 (measuring post in 8.05 km)          143.61              --
A3 (measuring post in 7.91 km)           4.05               --
A4 (measuring post in 14.50 km)           --               2.68
A14 (measuring post in 10.86 km)          --               3.93
A15 (measuring post in 10.01 km)          --               6.42
A16 (measuring post in 16.37 km)          --               3.24
No.101 (measuring post in 8.09 km)        --               6.61
No.102 (measuring post in 16.80 km)       --               1.00
No.103 (measuring post in 15.23 km)      9.80               --

Table 2. Variation in traffic volume for the period
2007-2009 on the streets of Vilnius city

                          Consideration period 2007-2009

Streets of Vilnius city   Increase, %       Decrease, %

Kareiviu str.                 --               10.45
Olandu str.                   --               9.62
Laisves ave (1)              10.12              --
Laisves ave (2)               --               5.07
Oslo str.                     --               23.13
Gelezinio Vilko str.          --               11.73
Dariaus and Gireno str.      10.18              --
T. Narbuto str.              36.29              --
Savanoriu ave                9.71               --
Ukmerges str.                 --               1.28
Antakalnio str.              2.46               --
Minsk Road                   6.52               --

Fig. 2. The dynamics of changes in traffic volume on all main, national
and partly regional Lithuanian roads for the period 2000-2009

year     %

2000   100
2001   105
2002   112
2003   115
2004   127
2005   137
2006   148
2007   173
2008   175
2009   159

Note: Table made from line graph.

Fig. 4. Traffic volume measured for the period 2005-2009 in the
stationery stations installed in Vilnius region

Traffic volume during measured period, car

Road   2005 year   2006 year   2007 year   2008 year   2009 year

A1        200697      206486      242767      243152      229089
A2         71939       79849       96943      248311      236166
A3         56105       73094       76160       79268       79247
A4        122374      142331      150605      155302      146573
A14       100338      114296      123543      130137      118692
A15        51870       63847       73178       76580       68481
A16        66360       81158       89110       94472       86226
101        16856       20104       23638       23338       22071
102        55986       57372       64925       67970       64274
103         7721        8337       29862       30163       32788

Note: Table made from bar graph.

Fig. 6. Traffic volume measured in 2007-2009 in the stationery stations
installed on high-speed and main streets of Vilnius city: Kareiviu
str., Olandu str., Laisves ave (1), Laisves ave (2), Oslo str.,
Gelezinio Vilko str., Dariaus and Gireno str., T. Narbuto str.,
Savanoriu ave, Ukmerges str., Antakalnio str., Minsk Road

Traffic volume during measured period, car

Street                   2007 year   2008 year   2009 year

Kareiviu str.               251588      228889      225289
Olandu str.                 280176      270613      253221
Laisves ave (1)             188474      192346      207539
Laisves ave (2)                         229987      218324
Oslo str.                   342319      326533      263143
Gelezinio Vilko str.        251466      422623      221963
Dariaus ir Gireno str.      252719      300113      278434
T. Narbuto str.             215601      323533      293838
Savanoriu ave               310601      303536      340752
Ukmerges str.               396268      382312      391188
Antakalnio str.              25789       87293       87897
Minsk Road                              117817      125497

Note: Table made from bar graph.

Fig. 7. Truck intensity in stationary posts in Vilnius region for the
period 2005-2009

Traffic volume during measured period, trucks

Road   2005 year   2006 year   2007 year   2008 year   2009 year

A1         21238       27356       32844       30464       23296
A2          6818       10031       18599       18123       13545
A3          4585       10514       10864       11522        8897
A4          8911       13384       15939       17017       11809
A14         5530        8540        9233        9765        8904
A15         2898        4522        5166        4494        3815
A16         3360       12467        8764        9751        6965
101          588        1022        1337        1484        1407
102         2821        4200        4697        3850        3633
103          378         511        3318        3353        3472

Note: Table made from bar graph.

Fig. 8. Truck intensity in stationary posts on the streets of Vilnius
city for the period 2007-2009

Traffic volume during measured period, trucks

Street                      2007 year   2008 year   2009 year

Kareiviu str.                   22999       20093       17193
Olandu str.                     34105       32067       24525
Laisves ave (1)                 25672       24002       18904
Laisves ave (2)                             24859       19892
Oslo str.                       32262       25437       20308
Gelezinio Vilko str.            24300       48747       18049
Dariaus ir T. Gireno str.       42023       47986       38680
Narbuto str.                    25481       36991       28567
Savanoriu ave                   57693       51033       53366
Ukmerges str.                   35625       33679       28092
Antakalnio str.                 17808       20050       19996
Minsk Road                                  15746       14956

Note: Table made from bar graph.
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Date:Sep 1, 2010
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