Instance generation tool for on-demand transportation problems

We present REQreate, a tool to generate instances for on-demand transportation problems. Such problems consist of optimizing vehicle routes according to passengers' demand for transportation under space and time restrictions (called requests). REQreate is flexible and can be configured to generate instances for a variety of problems types in this problem class. In this paper, we exemplify this with the Dial-a-Ride Problem (DARP) and On-demand Bus Routing Problem (ODBRP). In most of the literature, researchers either test their solution algorithms with instances based on artificial networks or they perform real-life case studies on instances derived from a specific city or region. Furthermore, locations of requests for on-demand transportation problems are mostly randomly chosen according to a uniform distribution, rather than being derived from actual data. The aim of REQreate is to overcome any shortcomings from synthetic or specific instances. Rather than relying on artificial or limited data, we retrieve real -world street networks from OpenStreetMaps (OSM). To the best of our knowledge, this is the first tool to make use of real-life networks to generate instances for an extensive catalog of existing and upcoming on-demand transportation problems. Additionally, we present a simple method that can be embedded in the instance generation process to produce distinct urban mobility patterns. We perform an analysis with real-life data sets reported by rideshare companies and compare them with properties of synthetic instances generated with REQreate. Another contribution of this work is the introduction of the concept of instance similarity that serves as support to create a set of diverse instances, in addition to properties (size, dynamism, urgency, and geographic dispersion) that can be used to comprehend which characteristics of the problem instances have an impact on the performance quality (or efficiency) of a solution algorithm.