Revenue optimization built for transportation networks
Traditional revenue tools focus on single trips or isolated routes. TDS takes a network-wide approach, combining demand forecasting, channel performance, competitive signals and operational data to optimize revenue across your whole system.
Revenue management tools
The result: smarter pricing decisions, better load factors and stronger revenue per mile.
Demand forecasting
Predict future demand accurately, to make informed decisions about pricing and inventory allocation.
Market segmentation
Understand different customer groups and their willingness to pay, and target your pricing to each.
Dynamic pricing
Adjust prices in real time to demand and other market conditions.
Competitive analysis
Monitor competitors' pricing and strategies to spot opportunities and threats.
Yield management
Manage inventory and pricing together to maximize revenue per seat and per departure.
Revenue optimization
Use data and analytics to make decisions that increase revenue.
Every number, next to last year
TDS reports revenue, passengers, runs and load for any period, side by side with the same period last year or the period before, so you can see what changed and why. Days line up by weekday and holidays are marked, so every comparison is fair.
This month vs same month last year
Revenue by day
Fewer runs, fuller buses: revenue is up 3.2% on 4.3% fewer passengers. Days line up by weekday, so every Friday is compared with a Friday.
This monthSame month last yearHoliday
- Week 1$305Klast year $290K+5.2%
- Week 2$338Klast year $319K+6.0%
- Week 3$314Klast year $300K+4.7%
- Week 4$324Klast year $307K+5.5%
TDS insightFridays and Sundays drove most of the growth; Tuesday and Wednesday revenue is flat against last year.
Sample data: fictional routes and figures, this month compared with the same month last year.
Know when riders buy
See how far ahead riders book, by route, channel and fare, and how that has shifted since last year. The booking curve is what dynamic pricing and forecasting work from.
- Share of tickets by days before travel, this year and last
- Same-day sales and average days booked ahead, by route
- Earlier booking gives pricing more room to work
Time between sale and travel
Share of tickets by days before travel. 33% are bought on the day of travel, down from 37% last year; riders now book 6.2 days ahead on average (last year 5.4).
This monthSame month last year
| Bought | This month | Same month last year |
|---|---|---|
| Same day | 33% | 37% |
| 1 day | 17% | 18% |
| 2 days | 8% | 8% |
| 3 days | 6% | 6% |
| 4–7 days | 13% | 12% |
| 8–14 days | 10% | 9% |
| 15–29 days | 8% | 6% |
| 30+ days | 5% | 4% |
TDS insightRiders are booking earlier than last year, so the lowest fares on Friday and Sunday departures can close sooner.
Sample data: fictional routes and figures, this month compared with the same month last year.
Find the routes behind the change
Break any total down by route, schedule, channel, fare type or day of week, with last year beside every figure. Revenue can rise on fewer passengers when each seat earns more, and the breakdown shows where.
- Revenue, passengers and revenue per passenger for every route
- Change against last year or the previous period
- The same view by schedule, channel and fare
Revenue by route
This month against the same month last year. Group the same figures by schedule, channel, fare type, booking window or day of week.
| Route | Revenue last year | Revenue this year | Change | Passengers, change | Revenue per passenger | Change |
|---|---|---|---|---|---|---|
| Union Station → Harbor | $182,400 | $201,300 | +10.4% | −2.1% | $41.20 | +12.8% |
| Downtown → Airport | $156,900 | $171,800 | +9.5% | +6.8% | $28.60 | +2.5% |
| University → Downtown | $98,300 | $94,100 | −4.3% | −8.9% | $19.40 | +5.0% |
| Lakeside → Union Station | $88,700 | $92,600 | +4.4% | +1.2% | $36.10 | +3.2% |
| Civic Center → Fairview | $61,200 | $55,900 | −8.7% | −6.4% | $22.80 | −2.4% |
TDS insightUnion Station → Harbor earns 10% more on 2% fewer passengers: higher fares held up. Civic Center → Fairview is down on both, worth a look.
Sample data: fictional routes and figures, this month compared with the same month last year.
From demand response to yield orchestration
Most systems react to bookings. TDS is designed to predict and influence them. Its applied AI models continuously evaluate:
- Future demand signals
- Channel performance
- Trip value within the broader network
- Operational constraints
- Customer behavior trends
Instead of optimizing one trip at a time, TDS orchestrates yield across your whole system, so every seat is sold at its highest potential value.
Think your biggest competitor is another bus carrier? Think again.
Travelers compare your service with:
- Driving
- Flying
- Rail
- Rideshare
- Not traveling at all
TDS revenue tools weigh these broader signals, including weather, trip purpose, traffic and destination economics, so you price within the real competitive landscape, not just your own fare table.
Part of the full TDS platform
Revenue tools connect directly with:
- Inventory and schedules
- Distribution channels and OTAs
- Interline partners
- Settlement and reporting
- Operational planning
So pricing decisions flow through the system instead of sitting in spreadsheets or external tools.
What operators achieve with TDS revenue tools
- Higher load factors without blind discounting
- Better revenue per mile and per departure
- Faster response to market changes
- Better performance by sales channel
- Clearer visibility of demand trends
Related products
Route Planning
Network & schedules
Data & Analytics
Network intelligence
OTA Distribution
Retail Distribution Hub
See TDS in action.
A demo with our team, focused on the products you need, with time for your questions.