Perhaps your municipality has a budget for 3 vehicles and you’d like to find the most convenient area in which to engage a given number of ‘early users’. Maybe you have a fleet of 5 vehicles to provide on-demand transportation in a certain district and it would be helpful to know if this will be proficient enough to offer a ‘good service’?
When planning for the deployment of on-demand bus services, Shotl runs a series of simulations using a predictive modelling approach and data analysis in order to help you determine how on-demand bus services could be most successful in a particular area. There are four main factors to consider in any simulation:
The average time that elapses; from when a passenger requests a ride until he or she reaches their destination is a very sensitive variable. Users expect to spend a minimal amount of time waiting prior to the driver showing up, it is also reasonable for users to not be excessively delayed due to various stops that may be added along the route.
That’s the total number of passengers that are using the service during a certain period of time (active users), as well as all the origins and destinations and their coincidental patterns. When 2 or more passengers share common directions, there is greater opportunities to pair their trips with another vehicle; this increases the total overall occupancy. Furthermore, demand needs to be calculated during the different peak and off-peak time periods of a given day as volumes and travel patterns tend to fluctuate throughout the day.
The supply of an on-demand shuttle system relates to the number of vehicles and their capacity as per the total number of seats that are available. The bigger the fleet and size of each vehicle, the better the service offered to users. Vehicle capacity is important as it contributes to a smooth service during high demand peaks which can differ greatly from off-peak times.
The total territory of the confined area of operation has a direct effect on the typology of trips to be serviced, as larger areas would likely result in longer trips, subsequently, passengers would spend more time on board and as a consequence, the number of trips delivered during a certain period of time would be less. Moreover, in large areas, there is a higher chance of longer waiting times, as any suitable vehicle going in a direction that matches a new request can require users to wait much longer than usual.
In conclusion, these four different factors influence each other in multiple varied ways and simulations provide extremely valuable data in the planning stage when starting any new deployment.
One example of what simulations have taught us is to look at a case where demand doubles in a given area. Such an increase in demand only requires 50% additional vehicles as the chances of being paired with other users increases exponentially and the overall system becomes more efficient. By using simulations we’re also able to answer questions such as ‘How many vehicles would we need to keep an optimal quality of service if we increase the size of an area by 50%?’
Thanks to Shotl’s Pre-pilot Simulations we’re starting to understand the more challenging complexities that occur as a result of combining all four factors together. Our team at Shotl is continuously testing hypothetical scenarios in a virtual environment before taking the vehicles out into the streets.
As we dig further into the field of pre-scheduled rides, we continue to find new challenges and goals that we’ll always make available and be happy to share with our community. Continue reading our posts to find out more about what we’ve been up to! Further updates will follow soon.
At Shotl, we’re often asked what the greatest challenges are in getting people to accept our model of flexible, demand-responsive transit. The answer may surprise you.
Groundbreaking technologies are transforming the way we get around town, only this could be causing a new disparity among us.