Reference number : GC-2017-Q2-00466, amendment number : , agreement type : G, recipient legal name : Labbe, Aurélie (HEC Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Since the end of 2013, Intact Insurance offers usage-based car insurance programs designed to leverage_x000D_
data-driven technologies and improve insurance products. Under these programs, Intact collects driving data_x000D_
from customers using either a GPS dongle connected to the car on-board computer or a smartphone_x000D_
application. This data is used to characterize driving behavior and to detect risky events. In order to pre-process_x000D_
this massive amount of data collected, a map-matching algorithm needs to be implemented. Map matching is_x000D_
the process of estimating a user's position on a road segment. Concretely, GPS positions are provided only in_x000D_
terms of latitude and longitude and are not linked spatially to the road network. This is particularly of concern_x000D_
in urban centers, where tall buildings can completely block GPS signals or create spurious signals, leading to_x000D_
positional noise of several meters in magnitude. If the goal is to determine travelling patterns, then it is_x000D_
necessary to explicitly match each trip to the travelled network links. Although several map-matching_x000D_
procedures have been developed in the literature, no formal comparison exists in terms of accuracy and_x000D_
computational speed. These issues are extremely important for Intact Insurance, who needs to find the optimal_x000D_
trade-off between computational cost and precision. Furthermore, the performance of existing methods may_x000D_
vary greatly depending on the type of environment (urban, suburban, highways, etc.) and the use of a single_x000D_
method might not be the optimal solution. Moreover, the combination of GPS and sensor data has been rarely_x000D_
investigated in the context of big data; this could improve the map-matching accuracy but increases the_x000D_
computing complexity. Therefore, the objective of this research project is to: 1) evaluate the performance of_x000D_
existing map-matching methods both in terms of efficiency and accuracy, 2) investigate the benefits of_x000D_
combining GPS and sensor data in the map-matching accuracy, and 3) calibrate the optimal method(s) into_x000D_
Intact Insurance's analytical pipeline., program purpose french : Since the end of 2013, Intact Insurance offers usage-based car insurance programs designed to leverage_x000D_
data-driven technologies and improve insurance products. Under these programs, Intact collects driving data_x000D_
from customers using either a GPS dongle connected to the car on-board computer or a smartphone_x000D_
application. This data is used to characterize driving behavior and to detect risky events. In order to pre-process_x000D_
this massive amount of data collected, a map-matching algorithm needs to be implemented. Map matching is_x000D_
the process of estimating a user's position on a road segment. Concretely, GPS positions are provided only in_x000D_
terms of latitude and longitude and are not linked spatially to the road network. This is particularly of concern_x000D_
in urban centers, where tall buildings can completely block GPS signals or create spurious signals, leading to_x000D_
positional noise of several meters in magnitude. If the goal is to determine travelling patterns, then it is_x000D_
necessary to explicitly match each trip to the travelled network links. Although several map-matching_x000D_
procedures have been developed in the literature, no formal comparison exists in terms of accuracy and_x000D_
computational speed. These issues are extremely important for Intact Insurance, who needs to find the optimal_x000D_
trade-off between computational cost and precision. Furthermore, the performance of existing methods may_x000D_
vary greatly depending on the type of environment (urban, suburban, highways, etc.) and the use of a single_x000D_
method might not be the optimal solution. Moreover, the combination of GPS and sensor data has been rarely_x000D_
investigated in the context of big data; this could improve the map-matching accuracy but increases the_x000D_
computing complexity. Therefore, the objective of this research project is to: 1) evaluate the performance of_x000D_
existing map-matching methods both in terms of efficiency and accuracy, 2) investigate the benefits of_x000D_
combining GPS and sensor data in the map-matching accuracy, and 3) calibrate the optimal method(s) into_x000D_
Intact Insurance's analytical pipeline., agreement title english : Comparison, validation and calibration of map-matching algorithms for GPS data, agreement title french : Comparison, validation and calibration of map-matching algorithms for GPS data, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada