Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China

Jinjun Tang, Shen Zhang, Xinqiang Chen, Fang Liu and Yajie Zou

Physica A: Statistical Mechanics and its Applications, 2018, vol. 493, issue C, 430-443

Abstract: Understanding Origin–Destination distribution of taxi trips is very important for improving effects of transportation planning and enhancing quality of taxi services. This study proposes a new method based on Entropy-Maximizing theory to model OD distribution in Harbin city using large-scale taxi GPS trajectories. Firstly, a K-means clustering method is utilized to partition raw pick-up and drop-off location into different zones, and trips are assumed to start from and end at zone centers. A generalized cost function is further defined by considering travel distance, time and fee between each OD pair. GPS data collected from more than 1000 taxis at an interval of 30 s during one month are divided into two parts: data from first twenty days is treated as training dataset and last ten days is taken as testing dataset. The training dataset is used to calibrate model while testing dataset is used to validate model. Furthermore, three indicators, mean absolute error (MAE), root mean square error (RMSE) and mean percentage absolute error (MPAE), are applied to evaluate training and testing performance of Entropy-Maximizing model versus Gravity model. The results demonstrate Entropy-Maximizing model is superior to Gravity model. Findings of the study are used to validate the feasibility of OD distribution from taxi GPS data in urban system.

Keywords: Traffic distribution; Taxi GPS data; Entropy-Maximizing model; K-means clustering method; Generalized cost function (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:493:y:2018:i:c:p:430-443

DOI: 10.1016/j.physa.2017.11.114

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