Why Cleaning and Merging Your Data Enhances Your Bus Planning and Scheduling Performance
By Jared Coffee May 11, 2021 Reading time:
Expanding your bus operations is a great sign that your business is in a healthy position. However, expansion, such as acquiring new routes or increasing capacity, often result in hidden costs and challenges that need to be carefully managed.
One example of a challenge driven by expanding your network is that bus operators may need to maintain multiple datasets potentially on different systems for planning and scheduling purposes, which are to the result of expansions over a period of time. These disparate, disconnected datasets can cause challenges and complexity with network management, that result in reduced efficiencies – and higher costs.
This blog addresses why your bus operation should consider merging your separate planning and scheduling datasets into one, centralised, clean database as a single source of truth that helps increase operational efficiency for your growing bus business.
Like almost any entity that experiences rapid growth, datasets can have growing pains too. When a bus operation undertakes expansion phases via acquisition, the planning and scheduling datasets that are used to create bus schedules are incorporated into an operator’s team and software.
However, these multiple datasets all use different files. Also, the newly acquired datasets have been managed in a different way to existing network datasets. This scenario makes it extremely complex for a new user to plan entire bus schedules due to the number of files, and the fragmented state of the data.
These separate, and fragmented datasets create inefficiencies, and therefore lead to higher costs for multiple reasons. For example:
- Network Changes – edits and optimisations need to occur on several datasets, which can create significant labour costs, even for a minor change.
- Response Times – it can take schedulers longer to respond to mandated network changes.
- Increased running costs – if planning and scheduling outputs are inefficient, this could lead to increased dead-running, driver work and paid time.
- Data Maintenance – can be compromised if it is difficult to retrieve information.
- Errors – if data is inconsistent and complex to manage, the chance of human error increases.
- Staffing – if planning and scheduling becomes a difficult task, it can be challenging to entice new staff to undertake this essential function.
- Siloed teams – because of dataset complexity and lack of documentation, staff may find it challenging to work across different datasets they are not familiar with. This may cause workload imbalances and insufficient leave coverage.
- Incomplete Data and Non-compliance – if your bus operation needs to provide data to transport authorities (e.g., Global Stop ID’s), it may require manual manipulation.
However, cleaning and merging your datasets is a way to minimise the above impacts.
Why Your Bus Operation Should Consider a Dataset Spring Clean
A spring clean for disparate and multiple datasets can be the solution to overcome these growing pains. Cleaning them and merging them all together means that you can recalibrate your entire operation and quickly redefine optimal routes as a whole, with every single data component included. This includes precise key data such as depots, timing points, vehicle stops, and termini.
You will be able to then recalculate common route segments, and automatically generate running times that may uncover new and more efficient routes. Your planning and scheduling software will now provide staff with more accurate information as it is now accessing a common data layer, instead of them trying to produce correct outputs from separate datasets. Merging the datasets will then eliminate double (or more) handling of the data. Operations can now be streamlined as vehicle timetabling, driver scheduling, and crew rostering tools will only need to source one set of data.
Once a single master dataset for the entire bus network has been created, it allows the planning and scheduling team to run what-if scenarios using the whole network. This will give them the ability to adapt to a changing network and look for efficiencies which have not been identified previously.
A clean-up also eliminates many other time-consuming tasks that are created by multiple datasets. This is a good data practice that will help your bus operation save on resources and time when future expansions occur.
Some of the other benefits of consolidating and updating your datasets include:
- Removing unused historical data.
- Applying standardised naming conventions to all files for ease of access.
- The ability to import the latest General Transit Feed Specification (GTFS) data.
- Every stop can be plotted accurately in the network plan, with buses assigned to the required paths.
- Global Stop IDs or unique government identification numbers can be assigned to every stop.
- All meal depots can be created, and
- Award and constraint files can be streamlined for each depot.
Accurately planned bus networks, timetables, schedules, and rosters are critical for a successful bus operation. Since bus planning and scheduling tasks need to consider multiple factors to meet regulatory compliance, operate within set budgets and meet service level requirements to deliver better passenger experiences, dataset integrity must be considered and managed over the long term.
Whilst growth is great, it can also create issues for bus operators who have multiple planning and scheduling datasets to manage in isolation. Merging your datasets into a ‘single source of truth’, means that the planning and scheduling team will have the ability to look for efficiencies in the existing network, and adapt to future changes.
This creates efficiencies for your bus business and provides opportunities for you to grow your network further. Here at Trapeze, we have a wealth of experience in dataset migration, maintenance, and management to increase your bus planning and scheduling efficiencies. Contact us for more information today.