Reference number : GC-2017-Q1-02904, amendment number : , agreement type : G, recipient legal name : Maoh, Hanna (University of Windsor), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Modern supply chain processes rely heavily on goods transferred between different manufacturers, distributors and retailers. Trucks are by far the dominant mode of transportation for moving goods in Canada. Increased freight transportation activities in recent years underline the importance for understanding freight movements by trucks. Previous efforts to study freight movements have relied on data coming from surveys. However, these surveys are usually expensive for the analyst and time consuming for the respondent. An emerging alternative to surveys over the past few years has been the analysis of Global Positioning Systems (GPS) Data. Carriers have been using GPS transponders for over 2 decades to identify the location of their trucks in real-time and to determine when their goods get delivered. While GPS data provide remarkable opportunities to researchers, their usage in transportation modeling is challenging for two reasons: 1) these GPS records were not originally intended as an input for transportation models and analysis, and 2) the volume, velocity and variety of the obtained GPS data translates into billions of records (i.e. big-data) that have to be analyzed. Therefore, a need exists for novel methods and techniques to mine big-data from passive GPS records and fuse these data with other sources in order to understand and model truck movements and supply chain processes over space. 
 
To this end, the specific objectives of this research program are as follows: 1) Advance the current state of knowledge on freight movement and supply chain processes in Canada using a big-data that depict the movement of 60,000 Canadian trucks over a period of 7 months (September 2015 - March 2016); 2) Develop, implement and test new data mining methods that can be used to extract meaningful patterns and knowledge on truck movement and trade flows; 3) Develop a new software platform that embeds the devised methods from objectives #2 to visualize and characterize the nature of truck movements in Canada and between Canada and the US; 4) Model freight transportation movement to understand the travel behavior of shippers, and the underlying factors affecting the movement of cargo through transportation facilities; 5) Develop integrated simulation and microsimulation predictive models for freight transportation, trade flow and supply chain movements, to examine policies that could improve the performance of traffic on major congested corridors and cross-border points. 
 
The novelty of the proposed research program is twofold: 1) this is the first research program in Canada to analyze a big-data that depicts the movement of a large number of Canadian trucks for the years 2015 and 2016; and 2) to our knowledge, this is the only research program in Canada that seeks to develop and apply a suite of new data mining and fusion methods on big-data coming from GPS and use the outcomes to devise freight microsimulation models., program purpose french : Modern supply chain processes rely heavily on goods transferred between different manufacturers, distributors and retailers. Trucks are by far the dominant mode of transportation for moving goods in Canada. Increased freight transportation activities in recent years underline the importance for understanding freight movements by trucks. Previous efforts to study freight movements have relied on data coming from surveys. However, these surveys are usually expensive for the analyst and time consuming for the respondent. An emerging alternative to surveys over the past few years has been the analysis of Global Positioning Systems (GPS) Data. Carriers have been using GPS transponders for over 2 decades to identify the location of their trucks in real-time and to determine when their goods get delivered. While GPS data provide remarkable opportunities to researchers, their usage in transportation modeling is challenging for two reasons: 1) these GPS records were not originally intended as an input for transportation models and analysis, and 2) the volume, velocity and variety of the obtained GPS data translates into billions of records (i.e. big-data) that have to be analyzed. Therefore, a need exists for novel methods and techniques to mine big-data from passive GPS records and fuse these data with other sources in order to understand and model truck movements and supply chain processes over space. 
 
To this end, the specific objectives of this research program are as follows: 1) Advance the current state of knowledge on freight movement and supply chain processes in Canada using a big-data that depict the movement of 60,000 Canadian trucks over a period of 7 months (September 2015 - March 2016); 2) Develop, implement and test new data mining methods that can be used to extract meaningful patterns and knowledge on truck movement and trade flows; 3) Develop a new software platform that embeds the devised methods from objectives #2 to visualize and characterize the nature of truck movements in Canada and between Canada and the US; 4) Model freight transportation movement to understand the travel behavior of shippers, and the underlying factors affecting the movement of cargo through transportation facilities; 5) Develop integrated simulation and microsimulation predictive models for freight transportation, trade flow and supply chain movements, to examine policies that could improve the performance of traffic on major congested corridors and cross-border points. 
 
The novelty of the proposed research program is twofold: 1) this is the first research program in Canada to analyze a big-data that depicts the movement of a large number of Canadian trucks for the years 2015 and 2016; and 2) to our knowledge, this is the only research program in Canada that seeks to develop and apply a suite of new data mining and fusion methods on big-data coming from GPS and use the outcomes to devise freight microsimulation models., agreement title english : Mining, Fusion and Modeling of Truck Big Data for the development of Agent-Based Microsimulation Models, agreement title french : Mining, Fusion and Modeling of Truck Big Data for the development of Agent-Based Microsimulation Models, agreement number : RGPIN, agreement value : 105000.0, agreement start date : 2017-05-10, 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 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), 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