An instant matching algorithm in the context of ride-hailing applications, using isochrones and social scoring

Abstract

Ride-sharing nowadays is a term most people have heard about. It is an efficient way to reduce both climate detrimental emissions and the overall traffic within large cities. A key aspect of ride-sharing is to match drivers and passengers according to a set of attributes. By matching people based on static and dynamic settings such as social preferences, this paper shows a fast and efficient approach for solving this problem. As a key difference to other solutions the core of the developed instant matching algorithm is built up on isochrones. These are used to match trips on specific geographical constraints, like the maximum detour and time until a passenger’s pickup. Furthermore, social metrics allow optimization based on individual preferences in the users trip selection. The