Improving bus terminal capacity and passenger experience through simulation

The challenge

Growing demand for road-based public transport was putting increasing pressure on a strategically important bus terminal serving a metropolitan area.

In 2025 alone, the terminal recorded more than 200.000 bus entries. Demand was highly concentrated, with the busiest periods occurring between 9 AM and 9 PM, particularly on Fridays and Sundays, and with seasonal peaks during the summer.

Yet understanding the terminal's true capacity required more than looking at traffic volumes. Planning frequently diverged from actual operations due to delays, no-shows, and unplanned services. Bay pre-allocation also reserved capacity beyond the time buses occupied their assigned spaces.

The analysis showed the scale of this distortion. Planned plus actual occupancy exceeded 60% during 40% of annual operating hours, while actual allocation alone reached this level during only 1% of hours. Nearly a quarter of the buses also changed bay relative to the original plan, creating additional uncertainty for passengers.

The company needed to understand how much capacity was genuinely available, how alternative bay allocation policies would affect operations and passenger experience, and whether the terminal could accommodate future demand.

The solution

LTPlabs combined operational data analysis with simulation to create a quantitative representation of the terminal and test operational decisions before implementing them.

The work started by mapping the processes that influence capacity, planning and daily operations. Historical data was then used to characterise demand patterns and the differences between planned and actual activity.

A simulation of current operations established a reference point for scenario comparison. It captured key indicators across capacity, flow and passenger experience, including terminal and bay occupancy, congestion, entrance waiting times, bay dwell time, bay changes and passenger crossings within the terminal.

Alternative allocation models were then simulated. Three approaches grouped bays into destination zones with different configurations, allowing the team to assess the trade-off between operational flexibility, bay predictability and passenger movement.

The analysis was extended through 2030 using an assumed annual demand growth rate of 3%. Configurations with more and fewer bays were also evaluated to understand how infrastructure changes could affect future occupancy and queue times.

This created a decision environment where operational policies and infrastructure configurations could be compared through consistent metrics rather than assumptions about terminal capacity.

Results

The analysis revealed substantial capacity hidden by the existing planning model. Pre-allocating bays could nearly double occupancy relative to effective utilisation in certain situations, showing that planning rules were contributing materially to perceived capacity constraints.

Allocating destination zones across bays while keeping the remaining bays available as alternatives increased bay predictability by 2 percentage points and reduced passenger crossings by 13 percentage points compared with current operations.

The longer term analysis also provided evidence for infrastructure decisions. Under the assumed 3% annual demand growth, the study found no evidence that any of the analysed layouts would prevent the terminal from operating through 2030.

Capacity reductions nevertheless introduce operational consequences. In the restructuring scenario, which considered fewer bays, average queue time during peak hours was estimated to increase by 30% by 2030, reaching approximately four minutes as occupancy approaches 80%.

The study therefore gave the client a quantified basis for balancing capacity, operational efficiency and passenger experience when deciding how the terminal should evolve.