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Table 7 Map-matching task ranked by difficulty and score

From: Transport behavior-mining from smartphones: a review

References Mode Category Score Metric Validation
Chen and Bierlaire [26] Walk, Bike, Car, Metro Multimodal, global, shortest-path [80%, 99%] Path similarity indicator n.p.
Torre et al. [106] Bicycle Match when possible, build when needed n.p. n.p. n.p.
Quddus et al. [89] Car Unimodal, incremental, point-based 99.2% \(A = \frac{\#(correctly \,matched \,GPS\, points)}{\#(Total \,GPS\, points)}\) n.p.
Li et al. [68] Car Unimodal, incremental, point-based 99.8% (sub-urban), 97.8% (urban) \(A = \frac{\#(correctly \,matched\,GPS\, points)}{\#(Total\, GPS\, points)}\) n.p.
Wei et al. [115] Car Unimodal, incremental, shortest-path 98% Accuracy n.p.
Bierlaire et al. [19] n.p. Unimodal, global, shortest-path [80%, 99%] Path similarity indicator n.p.
Wu et al. [116] Taxi Unimodal, incremental, point-based 93.58% Prediction accuracy of next road by the road having the maximum probability Hold-out
Hunter et al. [52] Taxi Unimodal, incremental, shortest-path, supervised, unsupervised 100% (1 s resolution), \(>90\%\) (30 s resolution) Accuracy Manifold-cross-validation
Li and Wu [67] Taxi Unimodal, incremental, point-based 87.18% \(A = \frac{\#(correctly \,matched \,GPS\, points)}{\#(Total \,GPS \,points)}\) Hold-out
Jagadeesh and Srikanthan [54] Dataset 1: Taxi. Dataset 2: n.p. Unimodal, global, shortest-path 91.3% Average F-Score with: \(Precision = \frac{Length_{correct}}{Length_{matched}}\), \(Recall = \frac{Length_{correct}}{Length_{truth}}\), Input-to-output latency (Timelines) Hold-out
Newson and Krumm [78] Car Unimodal, incremental, point-based 100% (1 s resolution), \(>90\%\) (30 s resolution) \(Accuracy = 1 - E_L\), where \(E_L = \frac{(d_-+d_+)}{(d_0)}\), \(d_- =\) erroneous subtracted length, \(d_+ =\) erroneous added length, \(d_0 =\) length of correct route Hold-out
Lou et al. [71] n.p. Unimodal, global, shortest-path \(A_N >81\%\) , \(A_L >87\%\) \(A_N = \frac{\#(correctly\, matched \,road \,segments)}{\#(all \,road \,segments\,of\, the \,trajectory)}\), \(A_L = \frac{(\Sigma \,length \,of \,matched\, road \,segments)}{(length \,of \,the \,trajectory)}\) Hold-out