These are the three basic steps to solve a real world vehicle routing problem with GraphHopper’s Optimization API: 1) Assign geo locations to (road) network 2) Calculate travel times and distances 3) Solve vehicle routing problem Each step impacts the solution of the vehicle routing problem. This article illustrates how 1) – the assignment of […]

In this post, we are going to look a bit into the future and analyse how the arrival of autonomous vehicles can reshape the existing transport systems and how fleet management systems can help us make the most of the new opportunities. The idea of autonomous car have been inspiring people for many years, however, […]

Today, we would like to show you a demo of a feature we’ve been quietly working on for over a year: Real-time public transit routing. Grassroots transit apps depend a lot on open data published by operators. Trimet of Portland, Oregon, publishes both their schedule and realtime updates. This is what it looks like when […]

This article describes how we calculate and prepare travel time matrices to solve time dependent vehicle routing problems. Let me first explain what a typical vehicle routing problem is: Given a vehicle fleet and a number of customers, find the optimal set of vehicle routes to serve all customers and to minimize total transportation costs, […]

Load balancing in the context of vehicle routing problems is a way to achieve a more evenly distribution of jobs or activities over your drivers’ routes. There are a number of reasons to balance load. The most obvious ones are resource utilization and fairness. Why do we need a blog post for this? Why aren’t […]

We had the opportunity to improve two critical components of our Directions API. The Matrix API is now up to 20 times faster for large matrices, which makes our Route Optimization API also significantly faster as it uses the Matrix API under the hood. You can find more details in our recent blog post. The […]