We provide a framework for simulating Mobility as a Service (MaaS), Demand Responsive Transportation (DRT), and City Logistics applications.
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.
We’ll help you develop a watertight business case for running a service or connect your fleet management algorithms to our simulator, with interaction between your fleet and other road users.
Shared Mobility - Case Studies
Capri – Queen Elizabeth Olympic Park
The team ran a study to investigate the quantity of pods required in order to provide the right level of service.
Capri – Bristol Airport
This study helped prove the business case for an autonomous pod on demand (POD) shuttle service, over and above the existing airport shuttle bus service.
How we’ll help you develop your business case
'What if?' scenario testing
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:
- Fixed routes, semi-fixed routes, or completely free ‘taxi-style’ routing
- Fixed timetables or purely on-demand requests, realtime or pre-booked
- Physical stops, virtual stops, or stop-anywhere
- Different fleet types and configurations
- Rides offered in isolation or combined with public transport as part of a holistic transportation model
- Algorithms for managing routes, scheduling departures, offering and operating user-pooling, and fleet redistribution
- Algorithms for generating different offers, pricing models, and target service levels
- Attractiveness levels of different types of service
- Competition between different providers or services
- Sensitivity to key input parameters, e.g.,running multiple simulations with different assumptions on behavioral parameters
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.
Dynamic mode choice
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.