Give your Demand Responsive Transport service the best chance of success

Test different market scenarios with our
simulator to find parameters that work for you

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Features

SkedGo has developed a powerful simulation tool to help you test market scenarios of Demand Responsive Transport (DRT). Our simulator allows you to adjust a large variety of parameters, creating market scenarios which predict the most likely successful outcome for you.

SkedGo DRT System
Plublic routing algorithm logo
Multi-modal routing

This algorithm considers the existing transport networks, allowing you to determine which transport mode choices users are likely to make and at what price and time levels Demand Responsive Transport is competitive with public transport.

Behaviour capturing algorithm logo
Behaviour capturing

The simulator can mimic the behaviour of small or large groups of users as they operate a hypothetical trip planner, make decisions about transport modes, make DRT bookings in real-time at short notice, and cancel and amend bookings.

Realtime drt dispatching logo
Real-time DRT dispatching

Simulation of users making realistic choices on how to get from A to B, using all available transport mode options. Users make bookings, amendments and cancellations. Tailor data inputs, until you get a suitable market scenario.

How it works

We make simulation of realistic DRT services easy! Simply adjust various parameters such as passenger pricing, fleet size, vehicle sizes and assumptions about user behaviour. The simulator then displays summary and detail view of the DRT trips taken, as well as cost, revenue and profit margin.

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Things you can adjust - inputs

Market design logo
Market scenarios
  • Wholesale pricing models – what commercial terms do vehicle operators (van operators, taxis & ride-sourcers) offer the dispatcher and where the vehicles are located
  • Retail pricing models – adjust time-of-day, day-of-week, number of riders, and define areas of operation
  • Cancellation policies
  • Incentives for giving more advance notice of bookings
Human behavior logo
User behaviour assumptions
  • Determine the number of users aware of the service based on your marketing budget. Alternatively, determine the market budget based on what figure you need to achieve critical mass
  • Specify how many vehicles are in the system, what size, where they are located and if they are they wheelchair enabled
  • Create a dataset defining a representative set of trips (“from A to B at time T”) that the users might put into their trip planner
  • Incentives for giving more advance notice of bookings

Things you get - Outputs

  • Overall profit
  • Costs and revenue, broken down by time-of-day and geographical area
  • What percentage of vans are idling, travelling empty to the next pickup, with just a single passenger, or several passengers, broken down by time-of-day
  • How many users chose to travel by public transport, DRT, or a combination of the two, broken down
  • Detailed data: The set of vehicle “post-manifests” (itineraries evolve throughout the day)
  • Detailed data: Log file of events

Want to learn more about how our simulator can help you? Would you like to schedule a demo?

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