Bus Stop and Facility Capacity & Speed Computational Engine

Modern transit agencies need more than simple schedules. They must continually evaluate how many passengers a stop can comfortably handle, how long boarding and alighting take, and how these factors affect overall network speed. The Bus Stop and Facility Capacity and Speed Computational Engine (hereafter the Engine) provides a systematic, datadriven way to answer those questions.

Why a Dedicated Engine?

• Complex Interactions: Passenger flow, vehicle length, dwell time, and surrounding infrastructure interact in nonlinear ways.
• Dynamic Demand: Peakhour volumes fluctuate daily, weekly, and seasonally.
• Policy Decisions: Service frequency, stop consolidation, and platform design hinge on reliable capacity estimates.

The Engine captures these dynamics and presents results that are easy to interpret, enabling planners to test whatif scenarios quickly.

Core Concepts

1. Facility Capacity

Facility capacity is the maximum number of passengers a stop or station can serve within a specified time interval while maintaining acceptable service quality. It is expressed as passengers per hour per direction (pphpd). The Engine calculates this value using three main inputs:

1. Physical Space: Width of curb, length of boarding area, presence of shelters or benches.
2. Passenger Distribution: Average dwell crowd density (persons/m) and queue length.
3. Operational Constraints: Minimum headway, bus length, and door configuration.

2. Boarding/Alighting Speed

Speed is measured as passengers per minute per door (ppmpd). It depends on:

• Passenger characteristics (age, luggage, mobility assistance).
• Vehicle design (number of doors, door width, level boarding).
• Driver behavior (dwelltime tolerance, door opening/closing speed).

3. Dwell Time Estimation

Dwell time is the period a bus remains at a stop. The Engine uses the classic formula:

Dw = t + (P / S) + (P / S)

where:

• t Base time (door opening/closing, driver reaction).
• P Number of boarding passengers.
• S Boarding speed (ppmpd).
• P Number of alighting passengers.
• S Alighting speed (ppmpd).

Engine Architecture

Key Input Parameters

1. Stop Geometry Length of curb (m), width of boarding zone (m), presence of barriers.
2. Vehicle Layout Number of doors, door width, step height, wheelchair ramp.
3. Passenger Profile Average passengers per boarding, proportion with luggage, proportion needing assistance.
4. Operational Settings Desired headway, target ontime performance, maximum allowable dwell.

Sample Calculation

Assume a downtown stop with the following characteristics:

• Curb length: 30m, boarding width: 4m.
• Twodoor bus, door width 1.2m, level boarding.
• Peakhour boarding = 120pax, alighting = 80pax.
• Observed base dwell time t = 5s.
• Estimated boarding speed S = 1.8ppmpd, alighting speed S = 2.2ppmpd.

Convert speeds to pax per second per door:

S = 1.8 / 60  0.03 pax/s/door  S = 2.2 / 60  0.0367 pax/s/door

With two doors, total speed doubles:

Total boarding speed = 0.03  2 = 0.06 pax/s  Total alighting speed = 0.0367  2 = 0.0734 pax/s

Dwell time:

Dw = 5s + (120 / 0.06) + (80 / 0.0734)      = 5 + 2000 + 1089  3094s  51.6min

This unrealistic result signals that the assumed speeds are too low for the passenger volume. By increasing door width to 1.8m (raising speed by 30%) the dwell falls dramatically to about 7minutesstill high, indicating the need for either longer headways or additional doors.

Scenario Analysis

The Engine lets planners explore alternatives instantly:

Benefits of Using the Engine

• EvidenceBased Planning: Quantifies impact of infrastructure upgrades before costly construction.
• Operational Efficiency: Identifies bottlenecks that cause schedule slippage.
• Scalability: Handles citywide networks with thousands of stops.
• Flexibility: Accepts custom speed models (e.g., for electric buses with smoother doors).
• Transparency: All calculations are reproducible and can be exported for audit.

Getting Started

1. Prepare a CSV file containing stop geometry and typical passenger volumes.
2. Upload the file through the dashboards Data Import tab.
3. Choose a vehicle profile (number of doors, door width, levelboarding flag).
4. Run the Compute Capacity & Speed job. Results appear as interactive maps and tables.
5. Use the Scenario Builder to test design alternatives or service changes.

Future Enhancements

• Integration with realtime passengercount sensors for live capacity monitoring.
• Machinelearning models that adapt speed estimates based on weather and timeofday.
• Incorporation of pedestriancrossing conflicts and traffic signal delays.
• Support for multimodal hubs where bus stops share space with bikeshare and micromobility docks.

By delivering precise, actionable insights into stop capacity and dwell dynamics, the Bus Stop and Facility Capacity and Speed Computational Engine empowers transit agencies to design faster, more reliable, and passengerfriendly services.