Assessing the effects of road type and position on the road on small mammal carcass persistence time

All vehicles are affected by the type of the road they are moving on it. Therefore the stability depends mainly on the amount of vibrations and steering system, which in turn depend on two main factors: the first is on the road type, which specifies the amount of vibrations arising from the movement of the wheels above it, and the second on is the type of the used suspension system, and how the parts connect with each other. As well as the damping factors, the tires type, and the used sprungs. In the current study, we will examine the effect of the road roughness on the performance coefficients (speed, displacement, and acceleration) of the joint points by using a BOGE device.

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How Long Do the Dead Survive on the Road? Carcass Persistence Probability and Implications for Road-Kill Monitoring Surveys

PLoS ONE ◽

10.1371/journal.pone.0025383 ◽

2011 ◽

Vol 6 (9) ◽

pp. e25383 ◽

Cited By ~ 141

Author(s):

Sara M. Santos ◽

Filipe Carvalho ◽

António Mira

Keyword(s):

Persistence Probability ◽

Road Kill ◽

The Road ◽

Carcass Persistence ◽

The Dead ◽

On The Road

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RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6730 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10965-10972

Author(s):

Songtao He ◽

Favyen Bastani ◽

Satvat Jagwani ◽

Edward Park ◽

Sofiane Abbar ◽

...

Keyword(s):

Neural Networks ◽

Receptive Field ◽

Spatial Correlation ◽

Satellite Imagery ◽

Road Network ◽

Network Graph ◽

The Road ◽

Road Type ◽

On The Road ◽

Graph Neural Networks

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation – the limited effective receptive field of image classifiers.To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. Using a GNN allows information to propagate on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km2 area in 20 U.S. cities and a synthesized dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. In addition, RoadTagger is robust to disruptions in the satellite imagery and is able to learn complicated inductive rules for aggregating scattered information along the road network.

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Investigation of the suspension of vehicles moving on stone roads

MATEC Web of Conferences ◽

10.1051/matecconf/202032901009 ◽

2020 ◽

Vol 329 ◽

pp. 01009

Author(s):

Umar Vakhidov ◽

Vladimir Makarov ◽

Vladislav Klubnichkin ◽

Evgeny Klubnichkin ◽

Vladimir Belyakov

Keyword(s):

Ride Comfort ◽

Travel Speed ◽

Mass Model ◽

Work Time ◽

Vibration Load ◽

The Road ◽

Calculation Results ◽

Road Type ◽

On The Road ◽

Bed Slope

The paper studies the problem of traffic on mountain roads of natural origin. A mathematical model of a natural stone road is presented, the paper demonstrates also diagrams of “floodplain surface vs. support bed slope” correlation function. The calculation of the ride comfort of vehicles utilizes a four-mass model. Calculation results are demonstrated for a GAZ-3308 truck, as well as for a vehicle with half the suspension rate. The result obtained shows 30-50% reduction of the vibration load at a suspension rate reduction by 50%, dependent on the road type. The travel speed can be increased by 45% dependent on the road type and driver’s work time.

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