An optimization model of on-demand mobility services with spatial heterogeneity in travel demand

Park, Junsu; Lee, JinWoo; Kim, Jinhee; Chung, Jin-Hyuk

doi:10.1016/j.trc.2023.104229

Cited by 2 publications

(2 citation statements)

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“…(4) According to Formula 1, we compute the dataset of the imbalance index for taxi demand-supply across all the grids and all the time periods. (5) In the end, the data from the first 21 days of January 2019 are utilized as the training set, while the remaining 10 days' data serve as the testing set.…”

Section: Data Descriptionmentioning

confidence: 99%

“…These algorithms require precise forecasting of the demand-supply imbalance in specific areas. So, grid-level taxi demand-supply prediction [5][6][7], especially the prediction of said imbalance [8,9], has become a focal point of research. The accurate prediction of the demand-supply imbalance in specific areas is a critical consideration when it comes to making taxi dispatching decisions.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

Yang,

2024

IJGI

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The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. The application of Graph Convolutional Networks (GCNs) in traffic forecasting has inspired the development of a spatial–temporal model for grid-level prediction of the taxi demand–supply imbalance. However, spatial–temporal GCN prediction models conventionally capture only static inter-grid correlation features. This research aims to address the dynamic influences caused by taxi mobility and the variations of other transportation modes on the demand–supply dynamics between grids. To achieve this, we employ taxi trajectory data and develop a model that incorporates dynamic GCN and Gated Recurrent Units (GRUs) to predict grid-level imbalances. This model captures the dynamic inter-grid influences between neighboring grids in the spatial dimension. It also identifies trends and periodic changes in the temporal dimension. The validation of this model, using taxi trajectory data from Shenzhen city, indicates superior performance compared to classical time-series models and spatial–temporal GCN models. An ablation study is conducted to analyze the impact of various factors on the predictive accuracy. This study demonstrates the precision and applicability of the proposed model.

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Section: Data Descriptionmentioning

confidence: 99%