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Floating Car and Camera Data Fusion for Non-parametric Route Travel Time Estimation
Author(s) -
Mahmood Rahmani,
Erik Jenelius,
Harilaos N Koutsopoulos
Publication year - 2014
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2014.08.058
Subject(s) - computer science , sensor fusion , real time computing , floating car data , data collection , ranging , identification (biology) , telecommunications , artificial intelligence , traffic congestion , transport engineering , statistics , botany , mathematics , engineering , biology
Traffic management centers take advantage of various data collection systems ranging from stationary sensors e.g. automated vehicle identification systems to mobile sensors e.g. fleet management systems. Each type of data collection system has its own advantages and disadvantages. Stationary sensors has less measurement noise than mobile sensors but their network coverage is limited. On the other hand, mobile sensors cover expand areas of road networks but they have less penetration rate and frequency of reports. Traffic state estimation can benefit from fusion of data from various sources as they complement each other. This paper introduces a route travel time estimation method that aggregates data from two traffic data sources, automated number plate recognition system and floating car data

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