Public transportation is expensive to operate and maintain and is often unsatisfactory. The attractiveness of public transportation can be enhanced by making it more seamless, which, in turn, would reduce financial constraints and inefficiencies. The adoption of mobile devices for ticketing solutions is promising. However, current solutions are often inflexible and require manual interactions that produce evanescent data. Therefore, using leading-edge technologies and infrastructure, it is desirable to develop a solution to fully automate fare collection. In this paper, we provide a comprehensive literature review to understand the state of public transportation and to facilitate the development and implementation of automated fare collection solutions. First, we discuss existing mobile technologies and their common ticketing implementations. Second, we provide a predictive behavior model with sensor analytics to better understand customer needs. Finally, we highlight how machine learning can harness transactional ticketing data to create valuable business intelligence. Overall, developing and implementing automated fare collection solutions in urban transportation is expected to have a significant positive impact on customer experiences, the emergence of new business models and the reduction of pollutant emissions.
Survey of Automated Fare Collection Solutions in Public Transportation
IEEE Transactions on Intelligent Transportation Systems ; 23 , 9 ; 14248-14266
2022-09-01
1928196 byte
Article (Journal)
Electronic Resource
English
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