Performance Evaluation of Long-Distance Road Transportation of Roundwood in Mountainous Conditions

Abstract

In Europe, long-distance transport of wood from landings to consumers is most often carried out by trucks and trucks with trailers. In forests located mainly in mountainous areas with rugged terrain and frequent curves, the construction of forest roads is complicated and often access for trucks with trailers is difficult or there is not enough space on the landing for maneuvers. In these cases, the truck leaves the trailer next to the public road and without it moves to the landing and loads the wood with Palafinger hydraulic crane model Epsilon Kran GmbH mounted on it, which it transfers to the trailer on the way back. Then, the truck moves to the landing to load itself, returns, hooks up the trailer and transports the wood to the customer. This study, conducted in a coniferous stand in Bulgaria, aimed to determine and develop models for the productivity and costs associated with transporting a truck with a trailer and to evaluate the suitability of this method. To study this very common method of long-distance transport, observations were made of 185 turns of a truck with a trailer operating with coniferous wood in Rila-Rhodope Mountain Massif, Southern Bulgaria. It was found that the duration of the working cycle is affected by the total mileage (average 65.41 km), the volume of the load and the number of logs. The productivity with and without delays, 7.80 and 7.30 m³/h, respectively, is affected by the mileage and the volume of the wood, while the corresponding transportation productivity (177.46 and 167.24 m³ km/h) is affected by the transport distance loaded and the volume of the load. To increase efficiency and reduce the cost of transporting wood over long distances, investments are needed to improve forest roads to eliminate the need to uncouple the trailer.

1. Introduction

Timber harvesting is an important part of forest management, which affects climate change, carbon sequestration, renewable energy production and biodiversity conservation [1,2,3,4]. The performance and costs of this operation are predominant elements that determine the economic viability of timber harvesting [5,6]. To achieve this, it is essential to respect silvicultural and technical–economic requirements during harvesting [7]. Timber harvesting is concerned with the transformation and movement of wood, where the conditions of complexity are increased by the conditions of use [7,8]. The principal processes refer to felling, processing, primary transportation to the landing area or roadside, loading, and log transportation to the industries and direct consumers [9].

Transportation deals with the movement of materials/products between sources and destinations. Its costs represent approximately one-third of total logistics costs [10]. Transport plays a vital role in the proper functioning of every economic system. The success of a company specializing in timber transport relies not only on an efficient transportation system but also on the collaboration of all entities in the supply chain [11]. Transportation modes include trucks, ships [12] or railways [13]. The transportation varies across different nations, contingent upon prevailing circumstances, the existing vehicle fleet and the condition and dimensions of transportation networks [12].

Wood transportation is a vital element of its supply chain, accounting for a significant proportion of the timber industry’s raw material costs and affecting its economy and competitiveness [14,15]. To obtain this, it is necessary to build a properly planned forest road for sustainable forest management [16]. For Pandur et al. [17], forest roads play the principal role of timber transport as well as transport of other (secondary) forest products from the forest stand. Forest transport infrastructure is composed of the primary forest traffic network for long-distance logs transport and the secondary forest traffic network for timber extraction. During timber extraction, the traffic should be limited to prevent soil compaction and to reduce environmental disturbance [18,19,20].

Logging trucks and hydraulic forest cranes are the most important instruments used in forest operations for timber haulage [21]. The composition of these cranes typically consists of a slewing device, a grapple, a grapple slewing device and a stabilizing device, and the performance of the crane is determined by the lifting height and the arm-extending distance [22]. The logging trucks are specifically created for timber haulage and, other than to work on public roads, their design must facilitate the movement on the narrow forestry roads, frequently unpaved [23]. Several, though not numerous, studies evaluated the factors that affect the productivity of logging trucks [24,25,26,27,28,29,30]. These factors can be subdivided into vehicle features, trailers features, road geometry, driving speed, gear changing, driving style, weather conditions, structure and conditions of the upper layer of forest roads, transportation distance and organizational characteristics (driving unloaded vehicles while returning). Furthermore, timber haulage is the costliest transport operation (accounting for 40 to 60% of all transport costs) [23]. This is due to the high variability of transported assortments made of different species, with the variable moisture content of wood, which does not allow for the precise weight of raw wood material [31,32]. These differences can significantly influence the overall weight of the truck unit [33,34].

The load capacities of logging trucks are contingent upon the size of the timber, the condition of the road, traffic regulations and the availability of the necessary machinery and capital to purchase equipment [35]. The construction of forest roads in areas characterized by mountainous terrain with a prevalence of winding roads is a complex undertaking. Access for trucks with trailers is often difficult or there is insufficient space to maneuver on the landing site. In such instances, the truck is detached from the trailer, positioned adjacent to the public road, and subsequently drives to the designated landing area without the trailer. The timber is then loaded using a hydraulic crane affixed to the truck and subsequently transferred to the trailer upon the return journey. Driving on forest roads and public roads in the mountainous conditions of the Rila-Rhodope massif affects the efficiency of the truck with trailer, including an increase in travel time and total work cycle time due to significantly reduced driving speeds. This is due to the low speeds on the forest roads from the public road to the landing twice—once for loading the trailer and the second time for loading the truck itself, both loaded and unloaded, which do not exceed 10 km/h, the large longitudinal gradients and the smaller turning radii. Public roads in the mountains also do not allow speeds higher than 50–60 km/h.

The objectives of this study have been to determine (i) the productivity and costs of logging trucks with trailer transport in a mountain area with high density of road bens, in the Bulgarian conditions, (ii) to develop predictive models of cycle time and productivity for transporting and (iii) to assess the suitability of the transport method selected.

2. Materials and Methods

2.1. Study Site and Description of Machine

The observations were made of 185 work cycles of a truck with a trailer operating with coniferous wood in the Rila-Rhodope Mountain Massif, Southern Bulgaria. The Yundola Training and Experimental Forestry Range at the University of Forestry, Sofia, is representative due to its location in the passage between the Rila Mountains and Rhodope Mountains, of which the massif provides over 40% of the harvested wood in Bulgaria (see

Table 1. Characteristics of the Yundola experimental forestry range.

Parameter	Characteristics
Total area	5190.6 ha
Forested area	4853.8 ha
Elevation	Between 1000 and 1800 m above sea level
Tree species composition by forested territory	- Scots pine (Pinus sylvestris L.)—1935.3 ha (40%); - Norway spruce (Picea abies (L.) H. Karst.)—1427.7 ha (30%); - Silver fir (Abies alba Mill.)—1267.8 ha (26%); - European beech (Fagus sylvatica L.)—166 ha (3%); - Others (1%).
Growing stock	1,324,500 m3 (273 m3·ha−1)
Average age of the forests	75 years
Average annual cut	21,500 m3
Density of the forest roads (incl. public roads)	23 m·ha−1
Average longitudinal slope of the forest roads (incl. public roads)	8 deg (14%)

and Figure 1). The harvested timber is in the form of assortments (logs).

The existing forest roads on the territory of the Yundola Training and Experimental Forestry Range have a total length of 106.5 km as follows:

-

Dirt roads (fourth grade forest roads) with a length of 88.4 km;

-

Partially paved roads (third grade) with a length of 12.7 km;

-

Gravel roads (second grade) with a length of 5.4 km.

These forest roads are single-lane of the second, third and fourth grade according to the Bulgarian legislation [36]. They comply with the following standards for the main geometric elements of forest roads in mountainous conditions:

-

Design speed: for the second and third grade—20 km/h, for the fourth grade—10 km/h;

-

Width of 4 m;

-

Maximum longitudinal slope 9%;

-

Maximum lateral slope in a straight line 2.5%; in a curve 6%;

-

Minimum radius of horizontal curves: second and third degree 20 m, fourth degree 15 m;

-

Minimum radius concave vertical curves 500 m;

-

Minimum radius serpentines 15 m.

The density of the forest roads on the territory of the Yundola Training and Experimental Forestry Range is 20.52 m·ha⁻¹ with an average longitudinal slope of the forest roads of 8%.

For comparison, the design speed of forest roads in flat and hilly terrain conditions is, respectively, for the second and third grade, 40 and 30 km/h, and for the fourth grade, 30 and 25 km/h.

The truck under test was a MAN TGS 33.480 type 6X4 (MAN Truck&Bus SE, Munich, Germany) specialized in assortment transport (Figure 2A,B). It was equipped with a Palfinger hydraulic crane, model Epsilon M120Z82, with a reach of 8.80 m (Epsilon Kran GmbH, Elsbethen-Glasenbach, Austria), and a Schwarzmüller 2-axle timber/stall trailer without box (Schwarzmüller, Freinberg, Austria) (Figure 2). In the study area, access for the truck and trailer was not possible due to the nature of the forest roads (rugged terrain and frequent curves). The solution that was implemented was to leave the trailer on the public road and enter the forest only with the truck and use the crane mounted on it to collect the timber. The technical data of the truck and trailer are given in

Table 2. Main technical data of truck and trailer.

Model	MAN 33.480 6 × 4	Schwarzmüller 2-Axle Timber/Stanchion Trailer
Engine	D2676LF03, 6 in-line cylinders
Displacement, liter	12,419
Effective power	353 kW (480 hp) at 1900 m−1
Torque	2300 Nm at 1050–1400 m−1
Transmission	ZF 16S2520D, 16 synchronized gears with manual shifting, gear ratios 13.80 to 0.84
Tires	Front 385/65R22.5”, load capacity 9000 kg. Rear 315/80R22.5”, load capacity 13,000 kg.	385/65R22.5”, load capacity 9000 kg
Kerbweight, kg	15,300	3000
Permissible gross vehicle weight, kg	25,000	18,000

. The load was limited by the maximal permissible mass of the truck with trailer in Bulgaria (40 tons) according to Bulgarian legislation [37], according to the Regulation of the European Council no. 2015/719 from 25 April 2015 [38], which amended the Regulation of the European Council no. 96/53 ES from 25 July 1996. Therefore, the permitted payload of the truck with the trailer is 21.7 tons or expressed in cubic meters of coniferous timber (27.12 m³).

2.2. Productivity Study and Costs

A detailed time motion study was conducted to determine the duration of the elements of the work cycle and the corresponding load volumes and distances travelled, filming the entire cycle.

The work cycle of the logging truck with trailer was divided into the following elements:

-

Travel unloaded (TU)—the time for movement from the garage to the landing and from the consumer to the landing or garage.

-

Preparation for loading (PL)—the time of landing roadside: the driver gets out of the cabin, moves to the crane, controls the stabilizers, climbs and takes the seat and extends the crane.

-

Loading (L)—the time during which the driver loads the assortments with the crane.

-

Preparation for departure (PD)—the time for placing the crane in the transport position, getting out of the crane seat, retracting the stabilizers, getting the driver into the cabin.

-

Travel to the trailer for reloading and back for loading the truck (R).

-

Travel loaded (TL)—the time for movement with load to the consumer.

-

Unloading (U)—the time for unloading the wooden materials with the crane on the truck or other unloading machine.

-

Delays (D)—the time during which no useful work is performed due to technical or organizational reasons.

Load volume was determined by measuring the length and the mid-length diameter under the bark (u.b.) of all logs from each assortment. During the study, the distances travelled by the truck and trailer were measured with a GPS device [39].

When the vehicle is moving with a load, the distance is productive (D_prod), and when it is moving without a load, it is unproductive (D_unprod). Due to the one-way nature of the movement of timber in forestry, a significant part of the distance of vehicles is unproductive. The distance utilization coefficient β of forestry vehicles, representing the ratio between the loaded distance (D_load) and the total distance (D_total), is found as follows:

$$\beta ={\displaystyle \frac{D_{load}}{D_{total}}}$$

(1)

During the productive distance between loading or unloading points, the vehicle can move partially loaded or fully loaded, i.e., its load capacity can be used partially or fully. The dynamic load capacity utilization coefficient γ_dyn characterizes the effective use of the load capacity during the movement of vehicles by comparing the performed transport work and the transport work that can be performed with full use of the nominal load capacity of the vehicles. It is determined as follows:

$${\gamma }_{dyn}={\displaystyle \frac{A_{TW,actual}}{A_{TW,nominal}}}$$

(2)

where A_TW,actual is the actual transport work, tons·km or m³·km; A_TW,nominal the transport work that the truck can perform when fully utilizing its rated load capacity, tons·km or m³·km. Delay-free productivity (P_PMH), expressed as the volume of timber transported per specific unit of time, e.g., hour, is calculated using the formula

$$P_{PMH}={\displaystyle \frac{V}{T_{net}}},m^3·h^{\mathrm{-1}},$$

(3)

where V is the payload volume of vehicle, m³; T_net—the duration of delay-free work cycle (turn), h.

Transport productivity (TP_PMH) is obtained by multiplying actual productivity by transport distance (D_load):

TP_PMH = P_PMH·D_load, m³·km/h.

(4)

The vehicle costs were calculated using the COST model [40]. To calculate the production cost for 1 m³ timber, the cost analysis employed the number of truck drivers, the hourly cost of a truck driver, the hourly cost of machines, the volume of transported timber, and productive machine hours (excluding all delay times). The machine cost per hour was reported both as productive machine hours (PMHs), excluding delays, and scheduled machine hours (SMHs). The purchase prices and truck driver wages required by the cost calculations were obtained from the accounting records [41]. Labor cost was set to EUR 6.50 SMH⁻¹ inclusive of indirect salary costs. The diesel fuel consumption was determined by the truck’s on-board computer. A salvage value of 10% of the purchase price was assumed and the value-added tax (VAT) was excluded.

Cost calculations assumed that companies worked for 150 working days in the year and depreciation period of 10 years. For transport work, this amounts to 130–150 working days per year (20–21 working days per month) at 8 scheduled working hours per day. Thus, yielded annual working hours to 910–1050 SMHs with a 70% use coefficient [42,43].

2.3. Data Analysis

Experimental data from the truck and trailer studies were used to develop predictive models to estimate cycle time and productivity using regression analysis. The variables used in the modelling approach included the transportation distance (D_load), the distance between the trailer and the yard (l), the transportation distance (D_total), the load volume per cycle (V) and the number of assortments in the load (n). Descriptive statistics of the variables were calculated and a stepwise backward regression procedure was used to model the variability of cycle time and productivity as a function of the independent variables. The confidence level used for the regression analysis was 95% (α = 0.05) and the assumed probability p < 0.05. Independent variables were significant at p < 0.05. Statistica software, version 8 (StatSoft Inc., Tulsa, OK, USA) was used to process the experimental data.

3. Results and Discussion

3.1. Work Cycle Time

The average experimental data of the observed truck with a trailer are shown in

Table 3. Mean experimental data.

Variables	Duration (minutes)			Distance (km)
	Mean Value ±SD	Min	Max	Mean Value ±SD	Min	Max
Travel unloaded (TU)	46.70 ± 19.85	11	119	30.32 ± 13.84	5	67
Preparation for loading (PL)	11.51 ± 4.24	2	25
Loading (L)	27.16 ± 5.70	10	41
Preparation for departure (PD)	12.64 ± 4.91	5	31
Travel to the trailer for reloading and back for loading the truck (R)	29.58 ± 9.48	9	55	9.90 ± 4.84	1	26
Travel loaded (TL)	54.58 ± 24.36	14	145	25.19 ± 13.13	5	59
Unloading (U)	26.12 ± 6.04	14	45
Delays (D)	12.52 ± 5.46	4	29
Total cycle time (Ttot)	218.43 ± 48.06	98	369
Delay-free cycle time (Tnet)	205.91 ± 47.97	90	362
Number of logs per cycle	68.35 ± 10.42	48	121
Load volume (m3 u.b.)	25.31 ± 0.83	22.69	27.03
Productivity (m3·PMH−1)	7.80 ± 1.94	4.29	17.15
Productivity (m3·SMH−1)	7.30 ± 1.70	4.12	15.75
Transport productivity (m3 km·PMH−1)	177.46 ± 67.37	55.57	381.09
Transport productivity (m3 km·SMH−1)	167.24 ± 64.53	50.42	365.27
Distance utilization coefficient (β)	0.45 ± 0.04	0.33	0.50
Dynamic load capacity utilization coefficient (γdyn)	0.92 ± 0.03	0.82	0.99
Number of cycles per SMH	0.29 ± 0.07	0.16	0.61
Mean speed (km h−1)	28.85 ± 8.19	13.04	57.41
Mean speed loaded (km h−1)	27.41 ± 7.63	11.42	51.82
Mean speed unloaded (km h−1)	31.10 ± 10.09	11.43	64.80

. In particular, the largest share of the work cycle elements is occupied by the operation “travel loaded” (26 and 25%, respectively, without and including delays), followed by “travel unloaded” (22 and 20%, respectively, without and including delays), “travel to the trailer for reloading and back for loading the truck” (14%), “loading” and “unloading” (both 13 and 12%, respectively, without and including delays), loading (22 and 20%, respectively, without and including delays), and “preparation for loading” and “preparation for departure” (both operations cover 6 and 5%, respectively, without and including delays). Delays are 5% of the total work cycle.

The breakdown by main groups of elements of delay-free cycle time shows the predominance of the operations related to movement (62%), and loading, unloading and preparatory operations (38%).

The data from Table 2 indicate that productive time was 94% of the work cycle scheduled time. The delays (6%) are due to organizational reasons (2%), mechanical delays (3%) and those due to adverse weather conditions (rain, snow, thick fog) (1%). The travel distance covered when the truck was unloaded exceeded that when it was loaded, as the truck with the trailer began its travel from the garage to the landing site.

The percentage of time spent for the travel is comparable to the data found by [44], in a study carried out in Finland about forest road transportation. The loaded travel covered about 33% of the time spent, while the unloaded travel occupied 19% of the time spent.

The mean haulage distance observed in this study is 25.37 (±12.20) km, which is consistent with the standard road timber haulage in the EU. Klvač et al. [26] demonstrate that haulage distances vary between 29 and 76 km, while Holzleitner et al. [45] report haulage distances differing between 27 and 102 km. In a study, Allman et al. [23] obtain a travel haulage of various distances, ranging from 19 to 72 km. The variations in road timber haulage were influenced by various terrain conditions and the positioning of timber processing infrastructure, as well as the technological approaches employed for timber haulage.

Regression analysis of the work cycle without and with delays was performed using the time study data to obtain predictive models for estimating the two truck–trailer times, shown in

Table 4. Summary of the work cycle time models.

Equations		F	R2	R2adj	Std. Error	p-Value
Tnet = 173.40 + 1.70 · Dtotal − 3.25 · V + 0.30 · n, min	(5)	221.00	0.83	0.83	20,252	p < 0.05
Ttot = 182.22 + 1.98 · Dtotal − 3.20 · V + 0.30 · n, min	(6)	221.76	0.83	0.83	20,334	p < 0.05

. The delay-free cycle time (T_net) regression equation obtained with significant variables, given in Equation (5) in Table 4, identifies the total distance, payload volume and number of assortments as significant influencing factors. The decrease in the total distance and the number of loaded assortments and the increase in the payload volume (i.e., fewer in number, but in thicker assortments) lead to a decrease in T_net. The same conclusions can be drawn for the duration of the work cycle (T_tot), including delays, given in Equation (6) from Table 4.

3.2. Travel Speed Analysis

In typical conditions, the speed of a truck on public roads can exceed 60 km·h⁻¹, whereas on forest roads, the maximum velocity is limited to 30 km·h⁻¹. The factors that influence truck speed include the condition of the road surface and the incline of the road [35]. The average speed of the truck with trailer during the timber transport is 28.85 km·h⁻¹. The average speeds with and without load are, respectively, 27.41 and 31.10 km·h⁻¹. The difference is almost 3.69 km·h⁻¹, due to the movement being downhill when loaded and uphill when unloaded, with gradient resistance acting.

In a study on timber road transportation in Southern Austria, Holzleitner et al. [45] obtained an average speed of 13.5 km·h⁻¹ over a travel distance of 51 km, which is even lower than the speed observed in the present analysis.

3.3. Productivity Analysis

The truck with trailer delay-free productivity (P_PMH) and the productivity including delays were defined by the regression Equations (7) and (8) shown in

Table 5. Summary of the productivity models.

Equations		F	R2	R2adj	Std. Error	p-Value
PPMH = 9.01 − 0.065 · Dtotal + 0.093 · V, m3·h−1	(7)	104.87	0.70	0.69	0.96	p < 0.05
PSMH = 8.4883 − 0.065 · Dtotal + 0.08 · V, m3·h−1	(8)	110.14	0.71	0.70	0.84	p < 0.05

and

Table 6. Summary of the transport productivity models.

Equations		F	R2	R2adj	Std. Error	p-Value
TPPMH = 2.71 · Dload + 3.1 · V, m3 km·h−1	(9)	104.87	0.92	0.92	18.25	p < 0.05
TPSMH = 3.68 · Dload + 2.77 · V, m3 km·h−1	(10)	110.14	0.93	0.92	17.12	p < 0.05

. From Equations (7) and (8), to enhance the delay-free productivity and productivity including delays of the studied vehicle, the total distance D_total should be reduced. In contrast, the payload volume should be increased within the permissible limits.

It is interesting that, in Equations (7) and (8), as well as in (5) and (6), the significant factor is the total distance, not the transport distance (D_load), which is probably due to the relatively small transport distances.

In long-distance transport, transport productivity better describes the transport work per unit time. Equations (9) and (10) show that an increase in transport productivity with delays off (TP_PMH) and on (TP_SMH) can be expected with an increase in transport distance and payload volume. Of course, the transport distance cannot increase excessively, as timber becomes unsaleable. The payload can be increased within permissible limits.

The distance utilization coefficient (β) is on average 0.45, which indicates that there is some margin to achieve an increase.

The dynamic load capacity utilization coefficient (γ_dyn) has a value of 0.92, which indicates that the reserve for increase can be achieved by improving forest roads in montane conditions and allowing the truck with the trailer to reach the roadside landing.

The load average volume obtained in this study is equivalent to 25.31 m³ for the timber haulage, which is comparable to the load volume reported by Holzleitner et al. [45], who measured a load of 25 m³. In Slovakia, Allman et al. [23] recorded a mean load volume of 27 m³. In the Czech Republic, Klvač et al. [26] documented an average load volume of 25, 23 and 28 m³ for three distinct logging trucks (IVECO, Tatra, and Mercedes-Benz). In Indonesia, Muhdi et al. [46] demonstrated that the average volume capacity of two trucks (Fuso FN 527 ML and Mitsubishi Colt Diesel Canter FE 74 HD) was 10 m³ and 32 m³, respectively. This result is in accordance with the legislation that imposes limitations on the load volumes for road transport of timber [37]. As previously stated, in Bulgaria the maximum mass permitted for a vehicle of transport is 40 tons according to Bulgarian legislation. In contrast, the findings from other studies indicated higher loads, attributable to the divergent legislation observed in other European countries. For instance, the maximum load capacity for a means of transport in the Czech Republic is 48 tons, whereas in Austria it is 44 tons [23].

In the timber haulage industry, productivity is the most significant operational parameter. The mean productivity, as determined in this study at an average haulage distance of 25.37 km, an average load volume per cycle of 25.31 m³ and a mean of 68.35 logs per cycle, is 7.79 m³·PMH⁻¹ and 7.70 m³·SMH⁻¹, respectively. These productivity rates are close to those reported by Holzleitner et al. [45], who measured 6.5 m³ h⁻¹ for a timber truck traveling 50 km with a load of 25 m³. Allman et al. [23] archived a productivity of 9.12 m³ h⁻¹ with a haulage distance of 19 km and a mean load volume of 20 m³.

3.4. Cost Analysis

The hourly costs of the studied truck with trailer, as well as the labor costs, are summarized in

Table 7. Transporting costs.

Classification of Costs	Costs per PMH, EUR h−1	Costs, EUR m−3
Total fixed costs:	22.01	3.00
Depreciation	6.68	0.91
Insurance	14.73	2.01
Taxes	0.60	0.08
Total variable costs:	35.65	4.86
Fuel and lubricants	26.85	3.66
Tires	0.36	0.05
Maintenance and repair	5.20	0.71
Other	1.98	0.27
Labor Costs	15.17	2.07
Net costs	72.83	9.92
Gross costs (incl. 10% overheads and 10% profit)	86.45	11.78

. As shown, the net costs for timber transport were estimated at EUR 72.83 PMH⁻¹ (productive machine hour). In terms of costs, variable costs (48.95% of total) are leading, followed by fixed (30.22% of total) and labor costs (20.83% of total). Therefore, regarding the productive time of the machine, the gross transport costs were estimated at EUR 11.78 m⁻³ including 10% overheads and management costs, and 10% profit margin.

In comparison, in Iran, Mousavi and Naghdi [35] determined that the cost of transportation for a dump truck and a chassis truck was USD 18 and 15 m⁻³ (according to the rates for 2013: EUR 13.5 to 11.25 m⁻³). Meanwhile, Holzleitner et al. [45] reported an average transport cost of EUR 11 m⁻³ and an hourly rate of EUR 72.00 for a truck and trailer. Recently, Brown [47] calculated the transport costs of semi-trailers in Australia, which equated to EUR 12.78 m⁻³, reported for an average transport distance of 50 km.

4. Conclusions

Long-distance timber transport by trucks and trailers dominates in Europe, so it is important to find ways to improve it. With many forest roads accessible in mountainous conditions only to trucks, but not with trailers attached, the easier alternative is often the one explored in this paper. This method of timber transport has its drawbacks—it increases work cycle time and costs, and productivity decreases due to the need to unhook the trailer, reload the timber from the truck to the roadside trailer and return the truck for loading. In terms of costs, however, it demonstrates costs per cubic meter that are close to those found by other authors for timber transport without leaving the trailer at the roadside, and also with semi-trailers. Investments in expanding and improving the forest road network will lead to better accessibility and increased versatility, and an increase in the effective use of the load capacity and the actual transport work of the timber trucks with trailers, especially in mountainous environments and for long-distance road transportation, thereby increasing efficiency and reducing costs.