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# Table 9 Methodology and main features, for Map-matching

References

Method

Main features

AGPS

INS

GIS

Quddus et al. [89]

Fuzzy logic, extended Kalaman filter

Speed, heading error, perpendicular distance, horizontal dilution of precision

12-channel single frequency high sensitivity GPS receiver

Yes

Li et al. [68]

Rule based, extended Kalaman filter, integrity check

Altitude, longitude, latitude, traffic flow directions, road curvature, grade separation, travel distance, heading

GPS

Yes

Bierlaire et al. [19]

Probabilistic

Timestamp, longitude, latitude, speed, heading, horizontal error Std. Dev., network error Std. Dev.

Yes

No

Yes

Li and Wu [67]

Feed forward neural network

Longitude, latitude, timestamp, heading

GPS

No

Yes

Lou et al. [71]

Mixed method: topological, geometric, probabilistic

Distance GPS(t) $$\rightarrow$$ GPS(tÂ +Â 1), distance GPS $$\rightarrow$$ network, shortest path between candidate points on network, average speed

Yes

No

Yes

Torre et al. [106]

Hidden Markov model, Viterbi

Distance GPS $$\rightarrow$$ node, maximum out-degree of the transportation graph

Yes

No

Cyclo-path map

Wei et al. [115]

Global Max-weight, hidden Markov model, Viterbi

GPS

No

Open Street Map

Chen and Bierlaire [26]

Probabilistic

Transport mode, distance, speed, acceleration

Yes

Accelerometer, Bluetooth Low Energy

Yes

Wu et al. [116]

Recurrent neural network, long short term memory

Longitude, latitude, timestamp, destination

GPS

No

Open Street Map

Newson and Krumm [78]

Hidden Markov model, Viterbi

Distance GPS(t) $$\rightarrow$$ GPS(tÂ +Â 1), distance GPS(t) $$\rightarrow$$ network (only in range <Â 200Â m)

Yes

No

Yes

Hunter et al. [52]

Undirected graph Bayesian network, Viterbi

Path length, distance point projection $$\rightarrow$$ GPS, number of signals, number of turns, average speed, Max/Min Num. lanes

GPS

No