Table 7 Map-matching task ranked by difficulty and score

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