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Luis Morriea-Matias
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1
The rapid increase in automated data collection in the public transport industry facilitates the adjustment of operational planning and real-time operations based on the prevailing traffic and demand conditions. In contrast to automated passenger counts systems, automated vehicle location (AVL) data are often available for the entire public transport fleet for monitoring purposes. However, the potential value of AVL data in estimating passenger volumes has been overlooked. This study examined whether AVL data could be used as a stand-alone source for estimating onboard bus loads. The modeling approach infers maximum passenger load stop from the timetable and then constructs the load profile by reverse engineering through a local constrained regression of dwell times as a function of passengers flows. To test and demonstrate the potential value of the proposed method, a proof of concept was performed by conducting unsupervised experiments on 1 month of AVL data collected from two bus lines in Dublin, Ireland. The results suggest that this method can potentially estimate passenger loads in real time in the absence of their direct measurement and can easily be introduced by public transport operators.
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The rapid increase in automated data collection in the public transport industry facilitates the adjustment of operational planning and real-time operations based on the prevailing traffic and demand conditions. In contrast to automated passenger counts systems, automated vehicle location (AVL) data are often available for the entire public transport fleet for monitoring purposes. However, the potential value of AVL data in estimating passenger volumes has been overlooked. This study examined whether AVL data could be used as a stand-alone source for estimating onboard bus loads. The modeling approach infers maximum passenger load stop from the timetable and then constructs the load profile by reverse engineering through a local constrained regression of dwell times as a function of passengers flows. To test and demonstrate the potential value of the proposed method, a proof of concept was performed by conducting unsupervised experiments on 1 month of AVL data collected from two bus lines in Dublin, Ireland. The results suggest that this method can potentially estimate passenger loads in real time in the absence of their direct measurement and can easily be introduced by public transport operators.
Journal article
(2016)
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Luis Morriea-Matias , Oded Cats, Joao Gama, Joao Mendes-Moreira, Jorge Freire de Sousa
Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable information from raw location-based data. State-of-the-art tools and methodologies such as Regression Analysis, Probabilistic Reasoning and Perceptron's learning with Stochastic Gradient Descent constitute building blocks of this predictive methodology. The prediction's output is then used to select and deploy a corrective action to automatically prevent Bus Bunching. The performance of the proposed method is evaluated using data collected from 18 bus routes in Porto, Portugal over a period of one year. Simulation results demonstrate that the proposed method can potentially reduce bunching by 68% and decrease average passenger waiting times by 4.5%, without prolonging in-vehicle times. The proposed system could be embedded in a decision support system to improve control room operations.
...
Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable information from raw location-based data. State-of-the-art tools and methodologies such as Regression Analysis, Probabilistic Reasoning and Perceptron's learning with Stochastic Gradient Descent constitute building blocks of this predictive methodology. The prediction's output is then used to select and deploy a corrective action to automatically prevent Bus Bunching. The performance of the proposed method is evaluated using data collected from 18 bus routes in Porto, Portugal over a period of one year. Simulation results demonstrate that the proposed method can potentially reduce bunching by 68% and decrease average passenger waiting times by 4.5%, without prolonging in-vehicle times. The proposed system could be embedded in a decision support system to improve control room operations.