Grant: Engage Grants Program
HEC Montréal
$25,000.00
Comparison, validation and calibration of map-matching algorithms for GPS data
FY 2017-18
Summary
Since the end of 2013, Intact Insurance offers usage-based car insurance programs designed to leveragedata-driven technologies and improve insurance products. Under these programs, Intact collects driving datafrom customers using either a GPS dongle connected to the car on-board computer or a smartphoneapplication. This data is used to characterize driving behavior and to detect risky events. In order to pre-processthis massive amount of data collected, a map-matching algorithm needs to be implemented. Map matching isthe process of estimating a user's position on a road segment. Concretely, GPS positions are provided only interms of latitude and longitude and are not linked spatially to the road network. This is particularly of concernin urban centers, where tall buildings can completely block GPS signals or create spurious signals, leading topositional noise of several meters in magnitude. If the goal is to determine travelling patterns, then it isnecessary to explicitly match each trip to the travelled network links. Although several map-matchingprocedures have been developed in the literature, no formal comparison exists in terms of accuracy andcomputational speed. These issues are extremely important for Intact Insurance, who needs to find the optimaltrade-off between computational cost and precision. Furthermore, the performance of existing methods mayvary greatly depending on the type of environment (urban, suburban, highways, etc.) and the use of a singlemethod might not be the optimal solution. Moreover, the combination of GPS and sensor data has been rarelyinvestigated in the context of big data; this could improve the map-matching accuracy but increases thecomputing complexity. Therefore, the objective of this research project is to: 1) evaluate the performance ofexisting map-matching methods both in terms of efficiency and accuracy, 2) investigate the benefits ofcombining GPS and sensor data in the map-matching accuracy, and 3) calibrate the optimal method(s) intoIntact Insurance's analytical pipeline.
Program
Engage Grants Program
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
514125-2017
Name nom
Labbe, Aurélie
Department département
Sciences de la décision
Organization id
65
Institution établissement
HEC Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
25000
Program id
EGP
Program name en
Engage Grants Program
Program name fr
Programme de subventions d'engagement partenariat
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1552
Committee name en
Quebec Internal Decision Committee
Committee name fr
Comité de décision interne pour le Québec
Area of application code
700
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Transportation systems and services
Area of application fr
Services et systèmes de transport
Research subject code
3011
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Multivariate analysis
Research subject fr
Analyse multivariée
Partie
1
Nb partie
1
Application title
Comparison, validation and calibration of map-matching algorithms for GPS data
Keyword
NSERC
Application summary
Since the end of 2013, Intact Insurance offers usage-based car insurance programs designed to leveragedata-driven technologies and improve insurance products. Under these programs, Intact collects driving datafrom customers using either a GPS dongle connected to the car on-board computer or a smartphoneapplication. This data is used to characterize driving behavior and to detect risky events. In order to pre-processthis massive amount of data collected, a map-matching algorithm needs to be implemented. Map matching isthe process of estimating a user's position on a road segment. Concretely, GPS positions are provided only interms of latitude and longitude and are not linked spatially to the road network. This is particularly of concernin urban centers, where tall buildings can completely block GPS signals or create spurious signals, leading topositional noise of several meters in magnitude. If the goal is to determine travelling patterns, then it isnecessary to explicitly match each trip to the travelled network links. Although several map-matchingprocedures have been developed in the literature, no formal comparison exists in terms of accuracy andcomputational speed. These issues are extremely important for Intact Insurance, who needs to find the optimaltrade-off between computational cost and precision. Furthermore, the performance of existing methods mayvary greatly depending on the type of environment (urban, suburban, highways, etc.) and the use of a singlemethod might not be the optimal solution. Moreover, the combination of GPS and sensor data has been rarelyinvestigated in the context of big data; this could improve the map-matching accuracy but increases thecomputing complexity. Therefore, the objective of this research project is to: 1) evaluate the performance ofexisting map-matching methods both in terms of efficiency and accuracy, 2) investigate the benefits ofcombining GPS and sensor data in the map-matching accuracy, and 3) calibrate the optimal method(s) intoIntact Insurance's analytical pipeline.
