Transit agencies and operators have unique customs when it comes to internal rules for operations and scheduling. They are also bound by different regulations and labor requirements, which can lead to widely different rules and preferences when they optimize their transportation network.
Jurisdictions impose all kinds of different regulations when it comes to labor, passenger transportation, and road safety. As a result, the way public transportation is operated varies greatly around the world but is relatively similar between agencies in the same region.
Yet, despite some hard restrictions, there is often a lot of room for flexibility.
As a mobility platform provider to public transportation agencies and operators, offering a cloud-based planning and scheduling platform, Optibus works with customers around the world. And we kept wondering what is the real cost (in dollar value) of different restrictions for vehicle and crew scheduling. It is clear that these rules have an enormous impact on the ability to create operational efficiencies, but the question is by how much.
Examining a set of “habits:” five scenarios
That’s why we decided to analyze public transportation operational “habits.” We tried to understand the variation in costs between different operational practices and sets of rules and preferences.
In doing this analysis, we considered five different operational scenarios, each with a slightly different set of rules. Changes were made in three different areas:
- Driver changeover: allowing drivers to change vehicles during duty
- Duty circularity: requiring operators’ shifts to start and end at the same point
- Driver return mode: the mode through which drivers return to their starting point (when duty circularity is required)
Below is a table that summarizes the conditions of each scenario.
- Scenario #1 is a control scenario; this scenario used the most difficult “habits,” so changeovers are forbidden and duty circularity is required.
- In the second scenario, we check what happens when duty circularity is not required.
- In the third scenario, we allow driver changeover but again require duty circularity, with drivers returning by operating the transit vehicle without accepting passengers (deadheading) or taxi.
- In the fourth scenario, the driver return mode can only be taxi (the relief vehicle, as it’s called in the industry).
- The fifth and last scenario relaxes all three preferences to their fullest extents.
At last, a dollar value
We took the data for a given metropolitan area, which was operating without an Optibus optimization, under the scenario 1 conditions. We then optimized it and achieved 5.4 percent savings. We then changed the rules and preferences, on the same data, for each of the four remaining scenarios.
Here are the results:
You can see that each scenario comes with a cost:
- In the second scenario, when duty circularity is not required, an additional 1.4 percent of all operational costs can be saved.
- In the third scenario, when driver changeover is allowed and duty circularity is required, the added savings are another 0.3 percent.
- In the fourth scenario, more savings are added by relaxing all constraints, creating an amazing (but perhaps not feasible) savings of 10 percent.
The takeaway here is that all transportation companies operate in slightly different ways, for a variety of reasons, and yet it might be worth re-considering some of those rules when the operational cost savings are significant.
In this case, we only modified three preferences out of hundreds that Optibus prides itself at considering. Even within external constraints, such as those imposed by local governments, it can become easier to rethink what might be possible if we just think outside of our status quo.
Photo by Sam Kittner for Mobility Lab