VMC® GeoStatistics

Virtual statistics by a simple mouse click.

VMC® GeoStatistics

Developing vehicles for global markets requires processes which can handle the usage variability given by different user profiles (commuter, taxi driver etc.), different driver characteristics and - last not least – the geographic variability (topography, climate/weather, road-network, population density).

Durability may primarily depend on road roughness and curviness and to some extend also on temperatures and humidity. Energy efficiency/CO2/NOX will depend on topography (hilliness) and traffic conditions, to some extend also on climate. Other target quantities like ageing of rubber bushings or reliability of connectors and electronic devices may each depend on other combinations of the above mentioned influence parameters.

 

VMC® GeoStatistics as a lean and transparent software solution

VMC® GeoStatistics provides the engineer with fast and efficient qualification and quantification of geographic influences. If you need to know how different countries compare with respect to e.g. hilliness, curviness, rainfall days in November and daily temperature gradients, you can easily analyse this with VMC® GeoStatistics with a few clicks on your desktop computer. The analysis is done based on the VMC® geo-referenced data base.

With VMC® GeoStatistics we propose a lean and transparent software solution to address this problem close to the drawing board in an efficient way. By offering an extensive worldwide data base for vehicle engineering, most required statistics can be sampled virtually. Once you selected regions or routes of interest, you get processed statistics by a mouse click.

Within minutes you find reproducible and transparent answers for questions like:

  • Drive train development: Is the Vietnam road network more curvy (or hilly) than that of Thailand?
  • Energy efficiency: Does stop-and-go traffic in Rio de Janeiro differ from Tokyo?
  • Electronics/harness: How to cluster China for air humidity in different altitudes?

 

Especially, you are able to:

  • Easily compare two regions for relevant factors
  • Compare sets of routes to a region: “Is the measurement representative for the region?”
  • Compare two sets of routes, e.g. from salesman and commuters
  • Cluster sub regions for similar factors values: “How many climate zones (rainy days, daytime temperature, …) are relevant in India?”