Powered by BICO: Responsiveness
In the previous three-parts of the Powered by BICO: Series we have assessed the impacts and proposed requirements for bikesharing operations as governments ease COVID-19 lockdown measures.
From reduced workforce, increased operational processes, changes in demand patterns and increased usage bikeshare operations are going through a monumental change.
Combine this with the already well reported dynamic nature of bikeshare with systems being impacted by real-world environmental (weather, traffic conditions, events, transport strikes etc.) to operational conditions (team, bike & station availability, key performance indicators etc.), the almost impossible tasks of system operations is only become harder to solve.
With the cities inhabitants depending on the reliability of bikeshare as a transport mode only increasing, how can operators assess, utilise and manage these data points to ensure a reliable, sustainable and profitable operation?
Working with bikeshare operators across the industry has given us a unique insight into overcoming not only the daily challenges of bikeshare operations but also the new challenge of returning from lock-down. One of these challenges is the responsiveness to dynamic parameters that effect system performance.
Bikeshare operations are notoriously difficult with COVID-19 only multiplying this further. With complex operational requirements from increased processes, ridership, change in usage patterns, combined with change in operational output (headcount, vehicle availability, bike availability) it’s so difficult to assess what is required.
To provide an example of how these fluctuating parameters cause operators numerous headaches we have references a fictitious scheme below, however the instances indicated will likely be all too familiar to bikeshare operators.
Let’s take a 10,000 bike, 800 station docked system at 08:00AM on a Monday morning.
The operator has 10 operational (rebalancing) vehicles working in three eight-hour shifts to cover the full day and they have predicted a ‘normal’ Monday’s traffic pattern.
But…
The local metro network is experiencing significant delays on one of its busiest routes, the weather is surprisingly sunny for this time of the year, two drivers have started their shift 30-minutes late with another experiencing a flat tyre reducing the initial operations fleet for 10 vehicles to just 7. And, to add to this the two busiest parts of the city that are notoriously difficult to enter with a vehicle at this time have already exceeded there KPI’s for the last two hours.
Now this is an extreme example, combining multiple real-world issues that drastically effect system performance in the immediate and longer term (and we haven’t even referenced rain or poor weekend-to-weekday preparation)
It shows, that for all the best planning, that bikeshare will always fluctuate when you least expect it.
This is why operators must optimise what they have at their disposal based upon the prevailing conditions with predictive information used as an indicative reference point.
With COVID-19 this only becomes more evident due to systems evolving at a rate of change that is completely unprecedented.
So with it being practically impossible for operators to calculate the best actions for the benefit of the entire system in their heads, or field teams in the street, it is time that operators look to utilise a dynamic operational strategy to adapt to the fluctuating network which is already proven to outperform other strategies.
So, What Does This Mean?
As the problem becomes increasingly harder to solve, innovative technologies should be assessed, implemented and utilised to assist with the heavy lifting of the day-to-day operations strategy.
With COVID-19 still providing a higher level of ‘unknown’ when it comes to bikeshare systems from workforce availability, usage patterns and ridership, processes should be data driven and standardised to ensure an optimal experience not only for the user but also the operator’s employees to further benefit cities sustainable transport strategies.
Otherwise the risk becomes that the dynamic nature of bikeshare systems will potentially become its undoing, as operators struggle to provide a reliable system for its users.
So, the question is:
What does good look like?
The nirvana for bikeshare operators would be considerable public funding, in-line with other public transportation modes (especially as some systems exceed their four wheeled counterparts usage) to allow for instantly scalable operations teams, increased and improved bike fleets to facilitate the demand placed on them no matter the weather, season or event.
The reality is a lot less simple, primarily due to the lack of funding, the workforce and fleet that is required to deliver the best system possible at the minimum cost. This is further compounded with operators being hamstrung by medieval Key Performance Indicators (KPI’s) that have little to no impact on the performance of the system in the view of the customer.
Operators must meet supply with demand, not with over supply (as seen in ride hailing and other transport modes) but with optimised supply to facilitate the demand.
Hence, assessed intelligence software tools to further augment the knowledge of operational staff, gain further efficiencies, maximise their resource and adapt to prevailing conditions to ensure provision of the best possible service to the users — no matter the circumstances.
Read Part-1 of the Powered by BICO Series: Workforce
Read Part-2 of the Powered by BICO Series: Changing Patterns
Read Part-3 of the Powered by BICO Series: Increased Demand
BICO AI provides an AI Operations Platform that assesses various environmental, resource, performance and usage data to optimise available resources (teams, bikes, batteries etc.) to ensure asset availability (location, usable, charged) across the entire network when and where it is required.
