Prediction of traffic volume by mining traffic sequences using travel time based PrefixSpan

Ganapathy, Jayanthi; Jothilakshmi, P.

doi:10.1049/iet-its.2018.5165

Cited by 11 publications

(2 citation statements)

References 53 publications

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“…In [23], Yamada et al propose a method that prioritizes traffic log data in the basis of the contribution to prediction accuracy; each base station sends more important traffic log data to the server with higher priority. In [24], Ganapathy et al propose a travel time based PrefixSpan (TT-PrefixSpan) algorithm which analyses traffic flow on highways by mining traffic sequence pattern and prediction of traffic volume based on traffic sequence rules. In [25], Zhang et al propose a hybrid model to simultaneously predict the traffic flow in multiple positions by combining the layerwise structure and the Markov transition matrix (MTM), then they apply the methodology on the real-world traffic data from Xiamen city, China.…”

Section: Related Workmentioning

confidence: 99%

Mining Urban Traffic Condition from Crowd-Sourced Data

et al. 2020

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Traffic congestion is an inherent and hard issue to be tackled in huge urban areas, particularly in developing countries where transportation infrastructures have not been grown well to fulfill speedy developing request demands. This paper proposes novel solutions to these issues by devising mobile crowd-sourcing based approaches to traffic estimation. A framework for effective collecting, integrating and analyzing traffic-related data shared by mobile crowds has been devised. Besides, essential issues on predicting traffic conditions at streets where real-time data is missed are also resolved by applying data mining techniques to historical data. A prototype system has been developed to validate the proposed solutions. The experimental results show the feasibility and the effectiveness of the proposed methods revealing that they are ready to be applied in the practice.

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Section: Related Workmentioning

confidence: 99%

Mining Urban Traffic Condition from Crowd-Sourced Data

et al. 2020

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“…There are many researchers focusing on all kinds of problems related to urban traffic, such as traffic flow prediction [5–10], traffic modelling [11–13], traffic pattern analysis [14], and so on. However, most of those works extract the spatio‐temporal relation, which is unexplainable as a hidden feature for final purpose.…”

Section: Introductionmentioning

confidence: 99%