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Paper Information

Journal:   JOURNAL OF TRANSPORTATION ENGINEERING   SUMMER 2017 , Volume 8 , Number 3 ; Page(s) 529 To 544.
 
Paper: 

USING MAP MATCHING ALGORITHMS TO EXTRACT TRAFFIC INFORMATION FROM LOW SAMPLING RATE GPS TRAJECTORIES

 
 
Author(s):  SHOKRI VAHID, ABBASPOUR RAHIM ALI*
 
* 
 
Abstract: 

GPS, among the various methods of traffic data collection has special popularity due to the low cost and wide access. The primary factor for reliability of determined parameters extracted GPS trajectories is the correct calculation of a vehicle location on a road segment, which is realized by a map-matching algorithm. High percent of GPS trajectories is taken from a variety of sources including mobile GPS, GPS equipped vehicles the, fleet public transport, and social networks are in low sampling rate between 2 to 6 minutes. So determining a suitable map matching algorithm to reduce data errors seems necessary. This paper aims to implement ST-matching and IVVM algorithms to match the GPS tracking data and extract traffic parameters and traffic map by matched trajectories. A special characteristics of ST-matching algorithm is to employ simultaneous spatial, temporal and topology analysis. IVMM algorithm not only considers the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. To evaluate these algorithms GPS trajectories with 2 minutes sampling rate of public fleet transport related to bus lines are used. ST-matching algorithm use only one point before each sampling point to match it there for the first point has not any before point, there is no the previous point, so the algorithm is highly dependent on the starting point. IVMM algorithm by modeling mutual influences between GPS points provides more effective and strong result. Distance Weight Function plays an important role in IVMM algorithm. By increasing the value of the parameter beta in Distance Weight function increases map matching accuracy. The obtained accuracy of the method of IVMM and the ST-matching is 88% 73% respectively. The results of this study show the IVMM algorithm outperforms the ST-matching method. Also In the face of U-turn IVMM gives better results.

 
Keyword(s): TRAFFIC INFORMATION, GPS TRAJECTORY, MAP MATCHING, IVMM, ST-MATCHING
 
 
References: 
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Click to Cite.
APA: Copy

SHOKRI, V., & ABBASPOUR, R. (2017). USING MAP MATCHING ALGORITHMS TO EXTRACT TRAFFIC INFORMATION FROM LOW SAMPLING RATE GPS TRAJECTORIES. JOURNAL OF TRANSPORTATION ENGINEERING, 8(3 ), 529-544. https://www.sid.ir/en/journal/ViewPaper.aspx?id=570432



Vancouver: Copy

SHOKRI VAHID, ABBASPOUR RAHIM ALI. USING MAP MATCHING ALGORITHMS TO EXTRACT TRAFFIC INFORMATION FROM LOW SAMPLING RATE GPS TRAJECTORIES. JOURNAL OF TRANSPORTATION ENGINEERING. 2017 [cited 2021May08];8(3 ):529-544. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570432



IEEE: Copy

SHOKRI, V., ABBASPOUR, R., 2017. USING MAP MATCHING ALGORITHMS TO EXTRACT TRAFFIC INFORMATION FROM LOW SAMPLING RATE GPS TRAJECTORIES. JOURNAL OF TRANSPORTATION ENGINEERING, [online] 8(3 ), pp.529-544. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570432.



 
 
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