A predictive model for the passenger demand on a taxi network (original) (raw)

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Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories

ISPRS International Journal of Geo-Information, 2019

Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affectin...

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Taxi Demand Prediction using Machine Learning

IRJET, 2023

Taxi demand prediction is the process of using historical data to forecast future taxi requests in a particular area. Managers may pre-allocate taxi resources in cities with the aid of accurate and real-time demand forecasting, which helps drivers find clients more quickly and cuts down on passenger waiting times. This project is aimed to choose the best model in predicting the taxi demand where we use various Machine learning techniques such as regression analysis and time series forecasting. Various baseline models, including moving averages (simple, weighted, and exponential), linear regression with grid search, random forest regressor with random search, and XGBoost regressor with random search are used. We find out which model is more suitable in predicting the output using the metrics we obtain.

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A predictive model for the passenger demand on a taxi network (original) (raw)

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