Increasing rail transit ridership by improving passenger ride comfort

Area of Science:

• Civil Engineering
• Transportation Engineering
• Railway Engineering

Background:

• Passenger ride comfort (PRC) is vital for urban rail transit ridership.
• Track geometry, especially tangent length between curves, impacts PRC but is under-researched.
• Current design practices may not adequately address tangent length for optimal comfort.

Purpose of the Study:

• To optimize minimum tangent length requirements between consecutive railway curves.
• To enhance passenger ride comfort (PRC) in urban rail transit systems.
• To develop a predictive model for optimal tangent lengths using data mining.

Main Methods:

• Established and validated a vehicle-track interaction model using field measurements.
• Conducted parametric analysis on curve radius, tangent length, and speed.
• Developed a data mining model to predict minimum tangent lengths.

Main Results:

• Reverse curves necessitate 5% longer tangent lengths than compound curves.
• Reverse curves exhibit a 9% higher ride comfort index.
• The developed model optimizes tangent lengths for speeds exceeding 80 km/h, improving PRC.

Conclusions:

• Optimized tangent lengths significantly improve passenger ride comfort.
• Enhanced comfort is expected to increase rail transit ridership.
• The data-driven model provides a valuable tool for railway design and PRC improvement.