How to Combine Reactivity and Anticipation: The Case of Conflicts Resolution in a Simulated Road Traffic

2000 ◽  
pp. 82-96 ◽  
Author(s):  
Sameh El hadouaj ◽  
Alexis Drogoul ◽  
Stéphane Espié
Keyword(s):  
Road Traffic ◽  
Conflicts Resolution

Road Traffic Monitoring

IEE Review ◽  
1989 ◽  
Vol 35 (5) ◽  
pp. 188
Author(s):  
P.L. Belcher
Keyword(s):  
Road Traffic ◽  
Traffic Monitoring

ROAD TRAFFIC FATALITIES AND ACCIDENT RISK IN UTTARAKHAND STATE OF INDIA: A GEOGRAPHICAL ANALYSIS

2020 ◽  
Vol 40 (2) ◽  
pp. 234-247
Author(s):  
Santosh Kumari ◽  
◽  
D.D SHARMA ◽  
VIRENDER SINGH ◽  
◽  
...  
Keyword(s):  
Road Traffic ◽  
Accident Risk ◽  
Traffic Fatalities ◽  
Geographical Analysis

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

Chest Trauma Evaluation and Outcome of Management in a Tertiary Hospital - One Year Experience

2020 ◽  
Vol 22 (2) ◽  
pp. 110-117
Author(s):  
Md Mahmudul Islam ◽  
Khondkar AK Azad ◽  
Md Aminul Islam ◽  
Rivu Raj Chakraborty
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Chest Trauma ◽  
Road Traffic ◽  
Tube Thoracostomy ◽  
Rib Fracture ◽  
Medical College Hospital ◽  
College Hospital ◽  
Medical College ◽  
The Mean

Background: Chest trauma is responsible for 50% of deaths due to trauma. This kind of death usually occurs immediately after the trauma has occurred. Various therapeutic options have been reported for management of chest injuries like clinical observation, thoracocentesis, tube thoracostomy and open thoracotomy. Objective: To observe the pattern and outcome of management in chest trauma Methods: This is an observational study carried out in Casualty department of Chittagong Medical College Hospital (CMCH), Chittagong, between April 2015 to March 2016. Our study was included all patients, both sexes, following chest injury at Casualty units of Chittagong Medical College Hospital. All the data were recorded through the preformed data collection sheet and analyzed. Result: The mean age was found 37.7±18.1 years with range from 12 to 80 years. Male female ratio was 11.8:1. The mean time elapsed after trauma was found 6.1±3.1 hours with range from 1 to 72 hours. Almost one third (35.7%) patients was affecting road traffic accident followed by 42(27.3%) assault, 35(22.7%) stab injury, 15(9.7%) fall and 7(4.5%) gun shot . More than three fourth (80.5%) patients were managed by tube thoracostomy followed by 28(18.2%) observation and 2(1.3%) ventilatory support. No thoracotomy was done in emergency department. 42(27.2%) patients was found open pneumothorax followed by 41(26.6%) rib fracture, 31(20.1%) haemopneumothorax, 14(9%) simple pneumothorax, 12(7.8%) haemothorax, 6(3.9%) chest wall injury, 5(3.2%) tension pneumothorax, and 3(1.9%) flail chest. About the side of tube 60(39.0%) patients were given tube on left side followed by 57(37.0%) patients on right side, 9(5.8%) patients on both (left & right) side and 28(18.2%) patients needed no tube. Regarding the complications, 13(30%) patients had persistent haemothorax followed by 12(29%)tubes were placed outside triangle of safety, 6(13.9%) tubes were kinked, 6(13.9%) patients developed port side infection, 2(4.5%)tube was placed too shallow, 2(4.5%) patients developed empyema thoracis and 2(4.5%) patients developed bronchopleural fistula. The mean ICT removal information was found 8.8±3.6 days with range from 4 to 18 days. Reinsertion of ICT was done in 6(4.7%) patients. More than two third (68.2%) patients were recovered well, 43(27.9%) patients developed complication and 6(3.9%)patients died. More than two third (66.9%) patients had length of hospital stay 11-20 days. Conclusion: Most of the patients were in 3rd decade and male predominant. Road traffic accident and tube thoracostomy were more common. Open pneumothorax, rib fracture and haemopneumothorax were commonest injuries. Nearly one third of the patients had developed complications. Re-insertion of ICT needed almost five percent and death almost four percent. Journal of Surgical Sciences (2018) Vol. 22 (2) : 110-117

TO THE STABILITY OF TANK VEHICLES IN THE BRAKE MODE

2019 ◽  
pp. 27-32
Author(s):  
Denys Popelysh ◽  
Yurii Seluk ◽  
Sergyi Tomchuk
Keyword(s):  
Mathematical Model ◽  
Control Systems ◽  
Distribution Systems ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Center Of Mass ◽  
Dynamic Processes ◽  
Course Stability ◽  
The Stability ◽  
Tank Vehicles

This article discusses the question of the possibility of improving the roll stability of partially filled tank vehicles while braking. We consider the dangers associated with partially filled tank vehicles. We give examples of the severe consequences of road traffic accidents that have occurred with tank vehicles carrying dangerous goods. We conducted an analysis of the dynamic processes of fluid flow in the tank and their influence on the basic parameters of the stability of vehicle. When transporting a partially filled tank due to the comparability of the mass of the empty tank with the mass of the fluid being transported, the dynamic qualities of the vehicle change so that they differ significantly from the dynamic characteristics of other vehicles. Due to large displacements of the center of mass of cargo in the tank there are additional loads that act vehicle and significantly reduce the course stability and the drivability. We consider the dynamics of liquid sloshing in moving containers, and give examples of building a mechanical model of an oscillating fluid in a tank and a mathematical model of a vehicle with a tank. We also considered the method of improving the vehicle’s stability, which is based on the prediction of the moment of action and the nature of the dynamic processes of liquid cargo and the implementation of preventive actions by executive mechanisms. Modern automated control systems (anti-lock brake system, anti-slip control systems, stabilization systems, braking forces distribution systems, floor level systems, etc.) use a certain list of elements for collecting necessary parameters and actuators for their work. This gives the ability to influence the course stability properties without interfering with the design of the vehicle only by making changes to the software of these systems. Keywords: tank vehicle, roll stability, mathematical model, vehicle control systems.

Road Traffic Noise Prediction Model

2012 ◽  
Vol 3 (4) ◽  
pp. 110-112
Author(s):  
Rahul Singh ◽  
◽  
Parveen Bawa ◽  
Ranjan Kumar Thakur
Keyword(s):  
Prediction Model ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction
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