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Use Case

Dynamic Bus Frequency Optimization Based on Real-Time Passenger Counting

Turkey (2024)
Scale: Individual site
We developed a passenger counting system that adjusts bus frequency based on real-time platform crowd density. By analyzing passenger flow, the system optimizes scheduling, reduces wait times, and improves overall transit efficiency for a smoother, more responsive public transport experience.

Industry, Innovation And Infrastructure

Sustainable Cities And Communities

Challenge / Goal

This use case was initiated to tackle the persistent challenge of overcrowding and inefficient scheduling in high-demand public transit systems, particularly on Metrobus lines. Traditional fixed schedules often fail to adapt to real-time fluctuations in passenger flow, leading to long wait times, overcrowded platforms, and decreased passenger satisfaction.

Our goal was to develop an intelligent, data-driven passenger counting system capable of dynamically adjusting bus frequency based on real-time platform density. By continuously monitoring the number of passengers at each station, the system can optimize scheduling, dispatch additional buses during peak hours, and reduce unnecessary idle times during off-peak periods.

This solution aims to enhance the overall public transport experience by:

  1. Reducing Wait Times: Ensuring buses arrive when and where they are needed most.
  2. Improving Passenger Comfort: Minimizing overcrowding both on platforms and inside vehicles.
  3. Optimizing Operational Efficiency: Helping transit authorities allocate resources more effectively, reducing costs while maintaining high service quality.

Ultimately, this use case supports the vision of smarter, more responsive urban mobility systems that adapt to real-time conditions, leading to more efficient and passenger-friendly public transportation.

Solution

To address the challenge of overcrowding and inefficient scheduling in public transit, we followed these key steps and implemented targeted solutions:

1. Problem Analysis

  • Identified Key Issues: Analyzed existing transit data to understand peak hours, overcrowded stations, and gaps in service frequency.
  • Defined Objectives: Aimed to reduce wait times, manage crowd density, and optimize resource allocation.

2.Data Collection and Integration

  • Installed Passenger Counting Sensors: Deployed cameras at metrobus platforms to collect real-time passenger flow data.
  • Integrated with Existing Systems: Ensured compatibility with current scheduling and fleet management systems for seamless operation.

3. Real-Time Data Processing

  • Developed AI Algorithms: Implemented AI-driven models to analyze passenger density, predict crowd surges, and detect patterns in real-time.
  • Dynamic Threshold Setting: Configured the system to trigger alerts when platform crowd density reaches predefined thresholds.

4.Dynamic Scheduling Optimization

  • Automated Frequency Adjustment: Designed the system to automatically adjust bus dispatch intervals based on real-time passenger data.
  • Resource Reallocation: Enabled dynamic redistribution of buses from less crowded routes to high-demand areas.

5.Pilot Testing and Iteration 

  • Conducted Pilot Runs: Tested the system in a controlled environment to assess accuracy and responsiveness.
  • Feedback Loop: Collected data from the pilot phase, refined algorithms, and improved system performance based on real-world feedback.

6.Implementation and Monitoring

  • Full-Scale Deployment: Rolled out the solution across selected metrobus lines.
  • Continuous Monitoring: Established dashboards for real-time monitoring and performance tracking, allowing transit operators to make informed decisions quickly.

7.Impact Assessment and Continuous Improvement

  • Measured Outcomes: Evaluated key metrics such as reduced wait times, improved passenger flow, and operational cost savings.
  • Iterative Enhancements: Continuously fine-tuned the system to adapt to changing transit patterns and improve efficiency further.

Through this structured approach, we successfully developed a dynamic, data-driven passenger counting and scheduling optimization system that significantly enhances the efficiency and reliability of public transportation.

 

Citizen participation

Citizens were not directly involved during development, but we plan to collect feedback through surveys to understand their experiences and improve the system based on their needs.

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Time period

Planning time: Less than 6 months

Implementation time: Less than 6 months

Implementers

SmartIR & IETT (Subsidary of IMM)

Service providers

SmartIR & IETT (Subsidary of IMM)

End users

Municipalities, MultiModal Mobility Solution Providers

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