Road Traffic Prediction Using Context-Aware Random Forest Based on Volatility Nature of Traffic Flows

2013 ◽  
pp. 196-205 ◽  
Author(s):  
Narjes Zarei ◽  
Mohammad Ali Ghayour ◽  
Sattar Hashemi
Keyword(s):  
Random Forest ◽  
Road Traffic ◽  
Traffic Prediction ◽  
Context Aware ◽  
Traffic Flows

LIGHT REGULATION SYSTEMS IMPROVEMENT ANALYSIS TO FORECAST THE TRANSPORT FLOWS IN ALMATY

2021 ◽  
Vol 116 (1) ◽  
pp. 299-304
Author(s):  
Assel Aliyadynovna Sailau
Keyword(s):  
Environmental Pollution ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Light Regulation ◽  
Transport Network ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Transport Telematics

The number of vehicles on the roads of Almaty, Kazakhstan is growing from year to year. This brings about an increasing intensity and density of traffic flows in the streets which leads to congestion, decreasing speed of the traffic flow, increasing environmental pollution caused by car emissions, and which can potentially lead to the road traffic accidents (RTA), including fatalities. While the number of injuries grows up mainly due to drivers’ non-compliance with the speed limit, the environmental pollution is caused by longer traffic jams. Therefore, to reduce the level of road traffic injuries and emissions into the environment it is necessary to ensure the uniform movement of traffic flows in cities. Currently, one of the effective ways to do it is the use of transport telematics systems, in particular, control systems for road signs, road boards and traffic lights. The paper presents an analysis of existing systems and methods of traffic light regulation. The  analyses of the systems and methods are based on the use of homogeneous data, that is the data on standard parameters of traffic flows. The need in collecting and analyzing additional semi-structured data on the factors that have a significant impact on the traffic flows parameters in cities is shown as well. The work is dedicated to solving the problem of analysis and forecast of traffic flows in the city of Almaty, Kazakhstan. GPS data on the location of individual vehicles is used as the initial data for solving this problem. By projecting the obtained information onto the graph of the city's transport network, as well as using additional filtering, it is possible to obtain an estimate of individual parameters of traffic flows. These parameters are used for short-term forecast of the changes in the city's transport network.

Street-level Air Quality Inference Based on Geographically Context-aware Random Forest Using Opportunistic Mobile Sensor Network

2021 ◽  
Author(s):  
Xuening Qin ◽  
Tien Huu Do ◽  
Jelle Hofman ◽  
Esther Rodrigo ◽  
Valerio La Manna Panzica ◽  
...  
Keyword(s):  
Air Quality ◽  
Random Forest ◽  
Sensor Network ◽  
Context Aware ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Street Level

scholarly journals The impact of traffic flow structure on traffic safety: the case of Slovenian motorways

Transport ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 216-222 ◽  
Author(s):  
Marina Zanne ◽  
Aleš Groznik
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Transport Infrastructure ◽  
Traffic Flows ◽  
Moving Vehicle ◽  
Public Road ◽  
Past Data ◽  
The Impact

Road traffic accident is an accident on a public road in which at least one moving vehicle has been involved and material damage or injury or death has occurred. Traffic accidents occur for various reasons, with one of them being the transport infrastructure and next the condition of traffic environment. Motorways are considered to be the safest roads, which have initially been planned as dedicated roads intended to be travelled only by personal cars, but the evolution of modal split of freight transport in Europe is causing the heterogeneity of traffic flows on these roads, which consequently affects the traffic safety. The aim of this paper is to explore the effects of changing volume and structure of traffic flows on road safety on Slovenian motorways. After the exhaustive analysis of past data, the paper provides different models for forecasting traffic safety on Slovenian motorways.

scholarly journals Adarules: Learning rules for real-time road-traffic prediction

2017 ◽  
Vol 27 ◽  
pp. 11-18 ◽  
Author(s):  
Rafael Mena-Yedra ◽  
Ricard Gavaldà ◽  
Jordi Casas
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Prediction ◽  
Learning Rules

In-vehicle Traffic Information Systems in Europe: Never Mind the (Band)Width, Feel the Quality

2001 ◽  
Vol 54 (3) ◽  
pp. 329-335 ◽  
Author(s):  
Jordi Calafell ◽  
Martin Pyne
Keyword(s):  
Information Systems ◽  
Service Providers ◽  
Road Traffic ◽  
Traffic Information ◽  
Navigation Systems ◽  
Traffic Flows ◽  
Planning Stage ◽  
Free Service ◽  
Major Player ◽  
On The Road

The ever-increasing number of vehicles on the road has created a serious demand for traffic information not only on the move but also at the planning stage of a journey. While on the move, the driver will be able to re-route his/her journey avoiding traffic congestion, but this information could be even more valuable before starting the journey. Today there are differences between the three main ITS markets (USA, Japan and Europe). Japan is leading the way, with the introduction in 1995 of the Vehicle Information Communication System (VICS), which is a free service. Vehicles are equipped with VICS receivers taking information from a network of road beacons installed on main roads, transmitting traffic flows by infrared rays, wave beacons, and FM multiplex broadcast. Europe has been involved in driver information systems from the early '90s when RDS was developed, which is another free service, and most of the car radios sold in Europe are able to process RDS signals. Since then, new free services have been developed – for example, RDS-TMC. In the UK, the private sector has been heavily involved recently, its major player being Trafficmaster. The Trafficmaster system is based on a network of traffic detection sensors, which covers all major UK motorways and most of the major A roads, with plans to expand into the continent. Trafficmaster collects road traffic flows and disseminates this information to its subscribers via a range of technologies, including GSM. The quality of the information supplied by the free and subscription services can be improved by being fully integrated with on-board navigation systems and by providing more detailed and wider types of information. All current methods are described/analysed and compared in this paper, with future enhancement highlighted. One of the main limitations lies in current data transmission routes, which are not fast enough to support the data required for an optimal use of the system. Some technologies available will potentially allow the many service providers to transmit information.

scholarly journals Accident Severity Prediction Using Data Mining Methods

2019 ◽  
pp. 528-536 ◽  
Author(s):  
S. Ramya ◽  
SK. Reshma ◽  
V. Dhatri Manogna ◽  
Y. Satya Saroja ◽  
G. Sanjay Gandhi
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Analytical Data ◽  
Developed Countries ◽  
Influential Factors ◽  
Living Environment ◽  
Severity Prediction ◽  
Accident Severity

The smart city concept provides opportunities to handle urban problems, and also to improve the citizens’ living environment. In recent years, road traffic accidents (RTAs) have become one of the largest national health issues in the world and it is leading cause for deaths. The burden of road accident casualties and damage is much higher in developing countries than in developed countries. Many factors (driver, environment, vehicle, etc.) are related to traffic accidents, some of those factors are more important in determining the accident severity than others. The analytical data mining solutions can significantly be employed to determine and predict such influential factors among human, vehicle and environmental factors. In this research, the classification technique i.e., Random forest algorithm is used to identify relevant patterns and for classifying the type of accident severity of various traffic accidents with the help of influential environmental features of RTAs that can be used to build the prediction model. This technique was tested using a real dataset. A decision system has been built using the model generated by the Random Forest technique that will help decision makers to enhance the decision making process by predicting the severity of the accident.

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