Analysis of bus ride comfort using smartphone sensor data

• Main Authors: CHIN, Hoong-Chor, PANG, Xingting, WANG, Zhaoxia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Ride comfort; smartphone sensor; classification & regression tree; kinematic motion; driver behavior analysis; Numerical Analysis and Scientific Computing; Operations Research, Systems Engineering and Industrial Engineering; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/5485; https://ink.library.smu.edu.sg/context/sis_research/article/6488/viewcontent/Analysis_of_Bus_Ride_Comfort_Using_Smartphone_Sensor_Data_pvoa.pdf
• Institution: Singapore Management University

Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second intervals. The kinematic motion characteristics obtained includes tri-axial linear accelerations, tri-axial rotational velocities, tri-axial inclinations and the latitude and longitude position of the vehicle and the updated speed. The data acquired were statistically analyzed using the Classification & Regression Tree method to correlate ride comfort with the best set of kinematic data. The results indicated that these kinematic changes captured in the smartphone can reflect the passenger ride comfort with an accuracy of about 90%. The work demonstrates that it is possible to make use of larger and readily available kinematic data to assess passenger comfort. This understanding also suggests the possibility of measuring driver behavior and performance.