Comparative Analysis of Bat Activity at Paved and Unpaved Roads in Forested Habitat

The degradation of paved and unpaved roads stands as a critical concern in contemporary infrastructure management. When faced with limited budgets, it is important to identify the optimal combination of road preservation strategies to minimize the lifecycle cost of the road network. Specific studies are necessary to improve the maintenance management systems and analyze the behavior of road surface deformation. To narrow these knowledge gaps, this study investigates a management system that focuses on the application of optimization techniques for managing both paved and unpaved tropical roads. Probabilistic deterioration models were constructed using the Markovian process, resulting in precise degradation curves in the context of 18 unpaved road segments in the Zona da Mata County of Minas Gerais (Brazil), along with 88 paved roads located in Minas Gerais. An optimization algorithm was proposed for the prediction of maintenance resources for unpaved and paved roads, emphasizing the cost-effectiveness of preventive and minor rehabilitation treatments over reconstruction. Comparisons between the maintenance costs of unpaved and paved roads indicated that the full rehabilitation costs of paved roads were approximately 10 times higher per kilometer compared to those of unpaved roads. The models effectively captured the trend wherein a major treatment leads to minor additional treatments being necessary for the subsequent several years in both scenarios. The findings of this study provide future directions for the optimized allocation of resources in the management of transportation infrastructures.

Roads are exposed to road surface disruptions (RSD) because of erosion, poor water drainage, rain, and soil quality. A delay in maintenance results in severe road damage that blocks the traffic for several days. Up to now, RSD detection on unpaved roads is done manually or reported by drivers. Conversely, different techniques have already been proposed for paved roads, such as vibration, image, and laser scanning. Unfortunately, the methods proposed for paved roads are not directly applicable for unpaved roads due to constraints and properties of RSD and pothole on unpaved roads. Therefore, this paper proposes a novel and low-cost method of detecting RSD based on ultrasonic sensors. The suggested model uses, as input data, relative distances collected by ultrasonic sensor beams, compute the approximate potholes and bumps on surfaces and outputs 2-D surface state map. This innovative and applicable approach has been tested on unpaved and paved roads and showed an accuracy of 94% regarding pothole characteristics (size, surface, and depth) on both paved and unpaved roads. A pothole detection rate of 62% was achieved on the paved road. Furthermore, the implemented algorithm is adaptable through a number of thresholds along with the desirable surface-sensor distance, the required RSD, the sensor’s velocity, and the distance between measurements. The results showed that the system could detect RSD and give valuable information which can help the maintenance team to plan for repair.

ABSTRACTObjective: All-terrain vehicles (ATVs) are designed for off-highway use only, and many of their features create increased risk with roadway travel. Over half of all ATV-related fatalities occur on roadways, and nonfatal roadway crashes result in more serious injuries than those off the road. A number of jurisdictions have passed or have considered legislation allowing ATVs on public roadways, sometimes limiting them to those unpaved, arguing that they are safe for ATVs. However, no studies have determined the epidemiology of ATV-related fatalities on different road surface types. The objective of the study was to compare ATV-related deaths on paved versus unpaved roads and to contrast them with off-road fatalities. Methods: Retrospective descriptive and multivariable analyses were performed using U.S. Consumer Product Safety Commission fatality data from 1982 through 2012. Results: After 1998, ATV-related deaths increased at twice the rate on paved versus unpaved roads. Still, 42% of all roadway deaths during the study period occurred on unpaved surfaces. States varied considerably, ranging from 18% to 79% of their ATV-related roadway deaths occurring on unpaved roads. Paved road crashes were more likely than those on unpaved surfaces to involve males, adolescents and younger adults, passengers, and collisions with other vehicles. Both the pattern of other vehicles involved in collisions and which vehicle hit the other were different for the 2 road types. Alcohol use was higher, helmet use was lower, and head injuries were more likely in paved versus unpaved roadway crashes. However, head injuries still occurred in 76% of fatalities on unpaved roads. Helmets were associated with lower proportions of head injuries among riders, regardless of road surface type. Relative to off-road crashes, both paved and unpaved roads were more likely to involve collisions with another vehicle. The vast majority of roadway crashes, however, did not involve a traffic collision on either paved or unpaved roads. Conclusions: Although differences were observed between paved and unpaved roads, our results show that riding on either represented significantly greater dangers than riding off the road. Many vehicle warnings specifically mention the risks of paved but not unpaved roads, yet we found 23 states with half or more of their roadway deaths on unpaved surfaces. Safety warnings should explicitly state the dangers of roadway riding regardless of surface type. These data further support laws/ordinances greatly restricting ATV riding on all types of public roadways.

For developing countries like Timor-Leste, regular road surface monitoring is a major challenge not only for maintaining road quality but also for national plan of road network construction. In Timor-Leste nearly 50% of roads are still unpaved. For this reason, an automated system is required to do a survey of paved and unpaved roads. In this study, we present a new approach for the use of smartphones sensor to classify paved and unpaved roads, and anomaly detection. Although, the most remarkable factor to differentiate paved and unpaved road is based on amplitude of the vertical acceleration, each vehicle has a different type of suspension system. Therefore, we used high-dimensional features and state-of-the-art machine learning techniques to make the system robust for differences of vehicle and also smartphone type. This study divided into two stages such as paved and unpaved road classification and road anomaly detection such as pothole and bump. For paved and unpaved road classification, we tried to use the SVM, HMM and ResNet and compared the performance of these models. Of all comparison, the ResNet was the best choice in this study, because it outperformed the SVM and HMM on the all performance evaluation criteria. Furthermore, the KNN and DTW are applied for anomaly detection on the paved road. The KNN-DTW are also compared to the other machine learning techniques like SVM and classical KNN using same criteria. As a result of the comparison, the KNN-DTW and SVM performed better than classical KNN.

ObjectivesThe study was to examine the nature and risk factors associated with road traffic crashes on unpaved roads and identify potential measures to control them. ProblemBackgroundRoad traffic crashes has become...

AimTo determine seed retention rates on vehicles as a function of distance driven, road surface, weather condition and seed location on the vehicle undercarriage.LocationMontana,UnitedStates.MethodsMetal plates were covered with a seed‐soil slurry, dried and attached to different locations underneath a vehicle. The vehicle was then driven on paved and unpaved roads under both wet and dry conditions. Plates were removed from the vehicle at seven distances between 4 and 256 km. The number of seeds remaining was determined. Four general models were assessed to explain observed seed retention.ResultsUnder dry conditions, seed retention rates were high on both unpaved and paved roads, with 86–99% of the seeds remaining at 256 km. Under wet conditions, lower rates of seed retention were observed for both road surfaces: 0.3–80% of seeds were retained at 256 km on paved wet roads and 50–96% of seeds were retained at 256 km on unpaved wet roads. Plate location had a significant effect on seed retention under certain road surfaces and conditions, with loss generally being highest from the wheel wells. Of the statistical models compared, a double exponential model explained the most variation in seed retention.Main conclusionsVehicles act as vectors of long‐distance dispersal. Seed adhered to vehicles can be retained for hundreds of kilometres under dry conditions. When wet conditions occur, a greater proportion of seeds will be dispersed shorter distances. Consequently, vehicle seed dispersal has implications for plant invasions and species migration rates, and those concerned with prevention and control of non‐native plant invasions should consider vehicle seed transport when developing management strategies and plans.

Vehicle-based road dust emission measurement—Part II: Effect of precipitation, wintertime road sanding, and street sweepers on inferred PM10 emission potentials from paved and unpaved roads

Impact of different road types on small mammals in Mt. Kalamaili Nature Reserve

It has been recognized that a good quality road infrastructure attract socio-economic development than the bad road condition. From this study, it was revealed that the study area has a total length of 6288km of roads. A total of 1272.6km (20%) constituted paved or tarred roads while about 5015.4km (80%) constituted unpaved or untarred roads. Information on road transport infrastructure variables was collected using field survey, questionnaire and data from relevant agencies. Factor analysis technique was applied to the fourteen variables to achieve a parsimonious description and identify the major factors which act as a pivot of road infrastructure quality development. From the analysis, two major factors were identified. Factor 1 identified and named as paved road and Factor 2, identified and named as unpaved road. The total variance for the factors is 55%. The study revealed that a greater percentage of the roads in the area are unpaved and it is recommended that efforts should be made to increase the length of paved roads in the area to attract socio-economic development.KEYWORDS: Road, Paved, Unpaved, Socio-economic, Development.

14 - Geotextiles used in reinforcing paved and unpaved roads and railroads

Small farms (less than 0.5 hectare) are very common in Egypt and many developing countries, which typically have vast growing populations with limited job opportunities. Using human resources in such conditions is mandatory. Therefore, this investigation was carried out to develop a human powered unit (prone cart- driven by two men) suitable for small farms. Four men were considered to drive the unit alternatively. This power unit was tested on two surfaces (paved and unpaved roads). The experiments included the following treatments: (1) pedal to drive wheel reduction ratio (1:2.5, 1:3.5 and 1:4.5), (2) traction mass [(25, 50 and 75 kg) on unpaved roads and (75, 100 and 125 kg) on paved road], (3) drawbar height (30, 35 and 40 cm), (4) tire inflation pressure (1.0, 1.5 and 2.0 bars). To evaluate the performance of the designed unit, the forward speed, slippage, rolling resistance were estimated. Results indicated that the best forward speed and drawbar pull of the unit were 2.56 km/hr and 55 kg, respectively, and the resulting slippage was 1.14 % on the paved road. However on the unpaved road, the best forward speed, drawbar pull, and slippage were 1.84 km/hr, 60 kg, and 3.26 %, respectively. Results show promises of using the human powered unit to do light farm operations such as applying fertilizers, weed and pesticide spraying and flame weed control. Future research is needed to test the ability of the developed unit to do different farm operations.

Evaluation of PM 10 emission rates from paved and unpaved roads using tracer techniques

Road dust is a major source for airborne particulate matter (PM). It is an agglomerate of deposited particles from vehicle exhaust, tire wear, break-lining wear, road surface and litter abrasions, local soil dust, vegetative detritus, and atmospheric fallout from many sources. Consequently, road dust is a mixture of coarse PM (road surface abrasions, soil dust, tire wear, and brake-lining wear) and fine PM (vehicular exhaust and portions of most sources of coarse PM). Although most studies concerned with road dust report the inorganic composition, only a few focus on organic constituents. Here, as part of the San Joaquin Valley Fugitive Dust Characterization Study, road dust samples from paved and unpaved urban and rural roads have been analyzed for close to 200 individual organic compounds, including n-alkanes, n-alkanoic and alkenoic acids, n-alkanols, n-alkanals, n-alkan-2-ones, alkylcyclohexanes, steroids, steranes, hopanes, triterpenoids, isoprenoids, benzothiazoles, polycyclic aromatic hydrocarbons, saccharides, pesticides, plasticizers, and diphenylamines. Organic compounds indicative of both higher plant detritus and traffic-related emissions (exhaust plus tire wear) are highest in paved road dust, suggesting that the grinding of plant detritus by vehicle tires on the hard paved surface liberate natural lipids and other natural occurring compounds that then admix with the road dust and can become airborne. Fossil fuel combustion markers such as hopanes and polycyclic aromatic hydrocarbons are highest for most paved roads and farming staging areas and lowest for unpaved roads. The highest level of vehicular exhaust organics associated with road dust was found in rural paved road dust. Rural roads compared with urban roads are typically not swept and have lower traffic density and as a result lower traffic induced resuspension. Consequently, vehicular exhaust deposited onto paved roads is more likely to accumulate on rural roads than on urban roads.

A review of monitoring systems of pavement condition in paved and unpaved roads

Roads can affect animals as well as their habits at different levels. Avoidance behavior is a common response of animals to this type of perturbation, preventing access to areas rich in resources. The effects of roads on ungulates have not been studied in South America extensively, especially in arid environments. We have studied the space use by ungulates in relation to roads, using a dung heap count and camera traps. The aim was to evaluate whether paved road and unpaved road may have an effect on the spatial use of a low density population of guanacos (Lama guanicoe) in Ischigualasto Provincial Park, Argentina. We observed an increase in dung heap abundance in unpaved road as respect to paved road, this difference was larger for both. Besides, we recorded less individuals in paved road zones than in unpaved road zones. This showed that roads, especially paved roads, negatively affect the space used by guanacos. Our results are relevant to the management and conservation of animal populations in protected areas since spatial segregation due to the presence of roads may lead to the isolation of individuals. It is important to pay attention and further assess the effects that roads can have in the native fauna.