2020
DOI: 10.3390/ijgi9030152
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Public Traffic Congestion Estimation Using an Artificial Neural Network

Abstract: Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Ext… Show more

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Cited by 21 publications
(17 citation statements)
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“…A large number of studies focus on the temporal analysis of RC. One line of research addresses the prediction of RC, where machine learning models such as neural networks [17][18][19] or Support Vector Machines [20] currently constitute the state-of-the-art. The closely related task of shortterm traffic forecasting is well studied in the existing literature [21].…”
Section: Congestion Analysismentioning
confidence: 99%
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“…A large number of studies focus on the temporal analysis of RC. One line of research addresses the prediction of RC, where machine learning models such as neural networks [17][18][19] or Support Vector Machines [20] currently constitute the state-of-the-art. The closely related task of shortterm traffic forecasting is well studied in the existing literature [21].…”
Section: Congestion Analysismentioning
confidence: 99%
“…Algorithm 1 presents an incremental greedy approach to merge spatially overlapping affected subgraphs. The algorithm consist of a main loop (line 6-24) where the individual steps include candidate generation (line 9-11), similarity computation (line 12-14) and merging (line [15][16][17][18][19][20][21][22][23][24]. For the candidate generation, we consider all subgraph pairs that share at least one unit as candidates (line 13).…”
Section: Spatial Merging Of Affected Subgraphsmentioning
confidence: 99%
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