In Ride, the demand-responsive vehicles are an overlay on regular traffic and interact fully with other road users.
Ride is underpinned by dynamic simulation of all flavors of on-demand or flexible transportation services so you can see how congestion impacts your service. Model individual vehicle-to-vehicle interaction or use aggregate representations of a transportation network and demand. Test and refine your fleet management algorithms by connecting them with Ride’s API.
Work with travel requests based on individual agents with customizable parameters based on anything from age, gender, eco-friendliness or economic status.
Understand how your offering fits into the mobility space and how it is likely to combine with and compete against alternative modes:
Our team can help AI start-ups and MaaS operators to evaluate different business models and design and test different types of service.
We can also advise transportation authorities or research groups on the potential impact of the move towards MaaS, or work out how MaaS and DRT could fit together with the wider public transportation system.
The combination of our robust, off-the-shelf simulation engines, an extensible software framework and our team of software and engineer experts, means we can set up models of any size for testing any kind of scenario, including but not limited to:
Let’s assume that you want to test your fleet management algorithms or model the impact of congestion on service delivery or model different types of priority measure. You will need to observe the development of the demand and service delivery over the simulated period, interrogate the routing, and the spatial and temporal distribution of demand. Ride will give you vehicle utilization levels, passenger kilometers and system service levels.
Simulate a population of individuals, each with their own characteristics, preferences and travel patterns. These individuals create requests for travel which pop up during the simulation, between specific locations, and at specific times; they may also include constraints such as earliest/latest pickup together with the individual’s characteristics.
Receive the request, query vehicle positions, shortest paths and ‘current’ network conditions, and use the data to make the agent an offer. Each offer may have distinct elements of walk, wait, and in-vehicle time, together with distance, cost, and number of interchanges.
Specify either a minimum cost or discrete choice logit function to choose between the offers received. You can even include ‘Internal operators’ to represent ‘conventional’ modes such as rail and private car.
The selected offer is accepted and passed to the simulator for execution. The operator is notified and updated on the status of the travel request and assigned vehicle.