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Table 9 Methodology and main features, for Map-matching

From: Transport behavior-mining from smartphones: a review

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 Dead-reckoning 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 Dead-reckoning 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(t + 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 Fréchet distance, shortest-path 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(t + 1), distance GPS(t) \(\rightarrow\) network (only in range < 200 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 560,000 links map
Jagadeesh and Srikanthan [54] DS 1: hidden Markov model, Viterbi, conditional random fields (CRF). DS 2: multinomial logit model, k-shortest path with link-penalty approach Path choice: free-flow travel time (s), number of traffic signals, average road class, number of class changes AGPS, with WiFi and GPS off No Yes