The prediction of bus travel time with accuracy is a significant step toward improving the qualit... more The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.
Modeling and prediction of traffic systems is a challenging task due to the complex interactions ... more Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus...
2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2021
Improving the accuracy of travel time predictions depends on providing the correct inputs as well... more Improving the accuracy of travel time predictions depends on providing the correct inputs as well as the prediction algorithm used. Clustering algorithms can be used to identify the patterns in the data, which can improve the inputs to the prediction algorithm. The feature vectors used for clustering greatly affect the clusters formed and, ultimately, the prediction performance. Clustering being an unsupervised learning technique, the accuracy or correctness of the cluster formed can not be evaluated directly. A possible solution for this would be to link the problem with prediction accuracy and choose the feature vector combination with maximum prediction accuracy. The present study analyses the use of different feature vectors for clustering and the effect on travel time predictions. Here, three cases, namely, travel time alone, travel time along with features such as time of the day, section index, and day of the week as numerical features and as a mix of categorical and numerica...
Transportation Research Record: Journal of the Transportation Research Board
Providing accurate and reliable travel time information to travellers is essential to improve the... more Providing accurate and reliable travel time information to travellers is essential to improve the quality of public transit systems. With the availability of the latest technologies, it has become possible to collect a large amount of traffic data to analyze and understand these systems better. Traffic in India is characterized by lack of lane discipline and the presence of vehicles of varying static and dynamic characteristics, which makes prediction of bus travel time especially challenging. The aim of this study is to identify both a prediction algorithm that can handle high variability and suitable inputs or regressors to be used. Earlier studies performed offline manual grouping considering the patterns observed, which leads to limitations for automated field implementations. The present study explores the use of data-driven approaches, primarily clustering, to address the challenges for the prediction of bus travel time trends. Discrete wavelet transform (DWT) was used to extr...
The prediction of bus travel time with accuracy is a significant step toward improving the qualit... more The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.
Modeling and prediction of traffic systems is a challenging task due to the complex interactions ... more Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus...
2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2021
Improving the accuracy of travel time predictions depends on providing the correct inputs as well... more Improving the accuracy of travel time predictions depends on providing the correct inputs as well as the prediction algorithm used. Clustering algorithms can be used to identify the patterns in the data, which can improve the inputs to the prediction algorithm. The feature vectors used for clustering greatly affect the clusters formed and, ultimately, the prediction performance. Clustering being an unsupervised learning technique, the accuracy or correctness of the cluster formed can not be evaluated directly. A possible solution for this would be to link the problem with prediction accuracy and choose the feature vector combination with maximum prediction accuracy. The present study analyses the use of different feature vectors for clustering and the effect on travel time predictions. Here, three cases, namely, travel time alone, travel time along with features such as time of the day, section index, and day of the week as numerical features and as a mix of categorical and numerica...
Transportation Research Record: Journal of the Transportation Research Board
Providing accurate and reliable travel time information to travellers is essential to improve the... more Providing accurate and reliable travel time information to travellers is essential to improve the quality of public transit systems. With the availability of the latest technologies, it has become possible to collect a large amount of traffic data to analyze and understand these systems better. Traffic in India is characterized by lack of lane discipline and the presence of vehicles of varying static and dynamic characteristics, which makes prediction of bus travel time especially challenging. The aim of this study is to identify both a prediction algorithm that can handle high variability and suitable inputs or regressors to be used. Earlier studies performed offline manual grouping considering the patterns observed, which leads to limitations for automated field implementations. The present study explores the use of data-driven approaches, primarily clustering, to address the challenges for the prediction of bus travel time trends. Discrete wavelet transform (DWT) was used to extr...
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