Fine (Penalty) Process Modelling of Real-Time Road Traffic Data with Process Mining

2020 ◽  
Author(s):  
Parham Porouhan
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Process Mining ◽  
Process Modelling ◽  
Traffic Data ◽  
Fine Penalty

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.

Process Mining With Semantics

2020 ◽  
pp. 181-216
Keyword(s):  
Real Time ◽  
Business Process ◽  
Process Mining ◽  
Task Force ◽  
Process Modelling ◽  
Business Process Modelling ◽  
Process Discovery ◽  
The Individual ◽  
Semantic Process

This chapter contains the application of the semantic process mining approach in real-time. This includes the series of analysis that were performed not only to show how the method is applied in different scenarios or settings for process mining purposes, but also how to technically apply the method for semantic process mining tasks. In the first section, the work shows how the authors practically apply the current tools that supports the process mining through its participation in the Process Discovery Contest organised by the IEEE CIS Task Force on Process Mining. In the second section, the chapter shows how it expounds the results and amalgamation of the two process mining techniques, namely fuzzy miner and business process modelling notation (BPMN) approach, in order to demonstrate the capability of the proposed semantic-based fuzzy miner being able to perform a more conceptual and accurate classification of the individual traces within the process or input models.

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.

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