scholarly journals Using Real-Time Road Traffic Data to Evaluate Congestion

2011 ◽  
pp. 93-117 ◽  
Author(s):  
Jean Bacon ◽  
Andrei Iu. Bejan ◽  
Alastair R. Beresford ◽  
David Evans ◽  
Richard J. Gibbens ◽  
...  
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Data

A PARALLEL PROCESSING MODEL FOR REAL-TIME COMPUTER VISION-AIDED ROAD TRAFFIC MONITORING

1992 ◽  
Vol 02 (02n03) ◽  
pp. 257-264 ◽  
Author(s):  
A. T. ALI ◽  
E. L. DAGLESS
Keyword(s):  
Computer Vision ◽  
Parallel Processing ◽  
Real Time ◽  
Road Traffic ◽  
Data Extraction ◽  
Image Data ◽  
Traffic Monitoring ◽  
Traffic Data ◽  
Video Images ◽  
Parallel Processing Model

A transputer-based parallel processing paradjgm for real-time extraction of road traffic data from video images of roadway scenes is proposed. The model can monitor three lanes of motorway traffic in real-time by processing images from two windows associated with each lane. Parallel algorithms are distributed among a network of transputers to perform similar and/or different tasks concerning image data analysis and traffic data extraction. The model can be expanded to cover more lanes or duplicated to monitor a further multi-lane carriageway.

A real-time push system on road traffic information

2014 ◽  
Author(s):  
Yang Xu ◽  
Zhang Zhenjiang ◽  
Liu Yun
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Information

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

Crash risk analysis during fog conditions using real-time traffic data

2018 ◽  
Vol 114 ◽  
pp. 4-11 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Crash Risk ◽  
Traffic Data ◽  
Real Time Traffic
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