Understanding usage of Volvo trucks
| Title | Understanding usage of Volvo trucks |
|---|---|
| Summary | A project in collaboration with Volvo AB on understanding vehicle usage patterns. |
| Keywords | Data Mining. Data representation |
| TimeFrame | Fall 2018 |
| References | |
| Prerequisites | Good knowledge of machine learning and programming skills for implementing machine learning algorithms |
| Author | |
| Supervisor | Sławomir Nowaczyk, Fredrik Moeschlin (at Volvo) |
| Level | Master |
| Status | Open |
There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings. There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings. There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings. There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings. There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings. There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings.
There is challenge in the automotive industries to properly segment offerings and products. By correctly understanding and grouping customers' needs, offerings and sales can be optimized. By utilizing techniques from data mining and machine learning, applied on telematics data, vehicles can be clustered according to their usage. On a high level segments can be described as e.g. “City distribution”, “Regional distribution”. These groups can then be further described for instance out of the distances, usage patterns, operating domain and other factors.
An example of usage pattern, for instance, Hauling Across Regions, could be: “Primarily long distance transport, typically followed by few clustered deliveries, generally in smooth road conditions. The average distance between pickup and delivery is 100 to 300 km. The vehicle usually returns to its home base each day, with overnight stays up to 3 times per week.”
From telematics systems, detailed information can be attained on where users drive, times, distances, locations and different activities. If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how a vehicle is used. This may lead to more precise segmentation (clustering) with quantified attributes (road conditions, driving time, actual hub to hub distances etc.). We may also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we expect to enable an overview of the segments in terms of quantity of customers, regional variation and more. The core value to reach for is better market understanding to allow for better positioning and offerings.
