Real-time on-board passenger comfort estimation in complex public transport networks with intersecting lines

• Advanced estimation framework for on-board comfort from incomplete APC data. • Real-time performance of complex multi-line public transport networks. • Case study on Helsinki commuter train network with high estimation accuracy. • Reliable comfort estimates even at low APC coverage levels. • Guidance for optimal APC deployment and service quality monitoring. Comfort on-board public transport vehicles is a critical metric of user experience and service performance. The quantification of this metric requires knowledge of the number of passengers on-board every time a vehicle arrives at or departs from a stop or station. Automatic Passenger Counting (APC) systems allow obtaining such knowledge in real-time, but the information is often incomplete due to system malfunctions, or, more commonly, a lack of the relevant equipment in some vehicles. This study develops an advanced method for passenger estimation that fills gaps in incomplete APC datasets, with computational performance allowing real-time application, and calculates comfort levels on-board public transport vehicles in complex networks where stations are served by multiple lines. The proposed method is tested on a case study considering the Helsinki commuter train network, comprising 6 service lines and 20 stations. The results indicate that the proposed framework can achieve comfort level estimations with high precision across the different cases evaluated. Furthermore, the study provides insight into the key practical question of the number of vehicles that need to be equipped with APC devices in order to obtain sufficiently accurate on-board passenger comfort estimates, and it is shown that it is possible to obtain these estimates even when only a small subset of the runs of any single day are performed by equipped vehicles. Finally, the proposed estimation approach is a valuable tool for operators to obtain a better understanding of daily mobility patterns, evaluate their services through quantifying user experience, and enhance their operations.