A heuristic path-estimating algorithm for large-scale real-time traffic information calculating

Lü, Weifeng; Zhu, Tongyu; Wu, Dongrui; Hong, Dan; Huang, Jian

doi:10.1007/s11431-008-5013-6

Cited by 15 publications

(9 citation statements)

References 4 publications

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“…Recently, GPS-enabled vehicles have been widely adopted to collect real time traffic data (Dai, Ferman, & Roesser, 2003;Kühne et al, 2003;Lü, Zhu, Wu, Dai, & Huang, 2008;Tong, Coifman, Merr, 2009). In many applications, such data are often obtained from GPS-enabled taxis instead of private automobiles due to privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

Wang

Xiao

et al. 2012

Landscape and Urban Planning

384

201

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Section: Introductionmentioning

confidence: 99%

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

Wang

Xiao

et al. 2012

Landscape and Urban Planning

384

201

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“…and reduces the processing time of calculation and improves the calculation efficiency. The minimum spacing Δd between two successive vehicle positioning points is set according to the Euclidean distance, as shown in (2). For a continuous GPS trajectory, if the time difference larger than Δt, it is determined that the vehicle is turned off, and the vehicle data will be regarded as invalid data and filtered out, as shown in (3).…”

Section: Gps Data Pre-processingmentioning

confidence: 99%

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Liu

Fang

Tong

et al. 2020

IET intell. transp. syst

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Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial-temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.

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“…GPS taxi data can provide an excellent foundation for revealing urban residents' travel behavior and analyzing spatial-temporal patterns [24,25]. Recently, GPS-enabled vehicles have been widely adopted to collect real time traffic data [26][27][28][29]. In addition to monitoring real-time traffic situations, a large volume of GPS-enabled taxi trajectory data has been used for travel time estimation, dynamic accessibility evaluation, and travel behavior analysis [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Huang

Zeng

2019

IJGI

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Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

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A heuristic path-estimating algorithm for large-scale real-time traffic information calculating

Cited by 15 publications

References 4 publications

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

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