TY - JOUR
T1 - GPS data in urban online ride-hailing
T2 - The technical potential analysis of demand prediction model
AU - Jiang, Wenxiao
AU - Zhang, Haoran
AU - Long, Yin
AU - Chen, Jinyu
AU - Sui, Yi
AU - Song, Xuan
AU - Shibasaki, Ryosuke
AU - Yu, Qing
PY - 2021/1/10
Y1 - 2021/1/10
N2 - Online ride-hailing as an innovative travel mode becomes increasingly popular in cities around the world. To improve the efficiency of dispatching system of ride-hailing, many ride-hailing demand prediction models based on deep learning architecture have been proposed to reduce the gross travel distance between drivers and passengers. However, in most of these prediction models, only the error metrics are used for performance evaluation. There is scarce evidence on how much the demand prediction model reduces the unnecessary travel distance in reality. In this study, a multi-scenario-based method is proposed to evaluate technical potentials of the ride-hailing demand prediction model in ride-hailing dispatching system simulation. The ride-hailing dispatching is simulated in three scenarios: traditional dispatching system, prediction model-based dispatching system, and perfect prediction model-based dispatching system. One-month data of Didi Express service provided by Didi Chuxing GAIA Initiative in Chengdu is employed to support the simulation. Two terms, empty distance and relative performance, are introduced as the criteria of prediction model performance measurement. Simulation results reveal that the total empty distance reduced 1,164 km per day by using the prediction model compared with the traditional dispatching system. The relative performance is only 58.9% compared with the perfect prediction model-based dispatching system.
AB - Online ride-hailing as an innovative travel mode becomes increasingly popular in cities around the world. To improve the efficiency of dispatching system of ride-hailing, many ride-hailing demand prediction models based on deep learning architecture have been proposed to reduce the gross travel distance between drivers and passengers. However, in most of these prediction models, only the error metrics are used for performance evaluation. There is scarce evidence on how much the demand prediction model reduces the unnecessary travel distance in reality. In this study, a multi-scenario-based method is proposed to evaluate technical potentials of the ride-hailing demand prediction model in ride-hailing dispatching system simulation. The ride-hailing dispatching is simulated in three scenarios: traditional dispatching system, prediction model-based dispatching system, and perfect prediction model-based dispatching system. One-month data of Didi Express service provided by Didi Chuxing GAIA Initiative in Chengdu is employed to support the simulation. Two terms, empty distance and relative performance, are introduced as the criteria of prediction model performance measurement. Simulation results reveal that the total empty distance reduced 1,164 km per day by using the prediction model compared with the traditional dispatching system. The relative performance is only 58.9% compared with the perfect prediction model-based dispatching system.
KW - Convolutional long short-term memory
KW - Data mining
KW - Dispatching system simulation
KW - Online ride-hailing
KW - Technical potential analysis
UR - http://www.scopus.com/inward/record.url?scp=85089748479&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.123706
DO - 10.1016/j.jclepro.2020.123706
M3 - Article
AN - SCOPUS:85089748479
VL - 279
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
SN - 0959-6526
M1 - 123706
ER -