Open AccessRealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks
Author(s) -
Qibin Zhou,
Qinggang Su,
Dingyu Yang
Publication year - 2022
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.21210
Subject(s) - computer science , real time computing , trajectory , overhead (engineering) , floating car data , partition (number theory) , software deployment , data mining , traffic generation model , histogram , traffic congestion , artificial intelligence , transport engineering , physics , mathematics , combinatorics , astronomy , image (mathematics) , engineering , operating system
Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTERealTE, implemented on a popular and open source streaming system StormStorm to quickly deal with high speed trajectory data. In RealTERealTE, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.