Abstract
Background:An Automatic Passenger Counting system represents a powerful resource for an efficient operational planning of public transport companies, but it gives rise to several challenges such as accuracy and precision, which must be addressed in order to operate successfully.Objective:Unlike previous studies in the North American bus market, this paper evaluates the accuracy and precision of an infrared APC system in a European bus market.Methods:The accuracy is evaluated by considering: (i) the presence/absence of the error and its direction; (ii) the magnitude of the error disregarding the direction and (iii) some tests on the nature of the error. The precision is evaluated by direct and inverse regression models and somet-teston biases.Results:As for accuracy, a small average magnitude of the errors is observed. In addition, the APC accurately measures alighting passengers, while it presents a slight tendency to systematically undercount boarding passengers. As for precision, the amount of measurement error due to the APC system exists and, even if it is relatively contained, it is statistically significant for boarding and alighting passengers.Conclusion:Although one type of APC system is evaluated only on one bus, it seems quite accurate for recording alighting passengers, whereas a correction factor should be applied for boarding passengers.
Highlights
Passenger volumes represent the most relevant component of the bus transit service, they are pivotal for efficient planning and operation and provide a key measure of effectiveness for Public Transport Companies (PTCs)
Conclusion: one type of Automatic Passenger Counting (APC) system is evaluated only on one bus, it seems quite accurate for recording alighting passengers, whereas a correction factor should be applied for boarding passengers
Unlike previous studies in the North American bus market, this paper aims to evaluate the accuracy and precision of a commercial infrared APC system in a real European bus market
Summary
The accuracy is evaluated by considering: (i) the presence/absence of the error and its direction; (ii) the magnitude of the error disregarding the direction and (iii) some tests on the nature of the error. The precision is evaluated by direct and inverse regression models and some t-test on biases
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