A Crowd-Assisted Real-time Public Transport Information Service: No More Endless Wait

Many passengers have expressed frustration in waiting for public bus endlessly without knowing the estimated ar- rival time. In many developing countries, requiring bus operators to invest in the installation of a GPS unit on every bus in order to track the bus location and subsequently predicting the bus arrival time can be costly. This paper proposes passenger-assisted sharing of bus location to provide an estimation of bus arrival time. Our scheme aims to exploit the availability and capability of passenger mobile phones to share location information of the travelling buses in order to collect transportation data, at the same time provide an estimation of bus arrival time to the general public. A mobile app is developed to periodically report bus location to the cloud service, and it can detect location spoofing by malicious users. The preliminary results of the field tests suggest that the proposed system is viable and the predicated ETA falls within three minutes of the bus actual arrival time

Predicting the Number of Passengers of MRT Jakarta Based on the Use of the QR-Code Payment Method during the Covid-19 Pandemic Using Long Short-Term Memory

The trend of using public transportation has been rising over the last several decades. Because of increased mobility, public transportation has now become more crucial. In modern environments, public transportation is not only used to carry people and products from one location to another but has also evolved into a service company. In Jakarta, Mass Rapid Transit Jakarta (MRTJ) started to operate in late 2019. Recently, they updated their payment gateway system with QR codes. In this study, we predicted the hourly influx of passengers who used QR codes as their preferred payment method. This research applied machine learning to perform a prediction methodology, which is proposed to predict the number of passengers using time-series analysis. The dataset contained 7760 instances across different hours and days in June 2020 and was reshaped to display the total number of passengers each hour. Next, we incorporated time-series regression alongside LSTM frameworks with variations in architecture. One architecture, the 1D CNN-LSTM, yielded a promising prediction error of only one to two passengers for every hour