Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Pick-Up Points Recommendation System for Ridesourcing Service
In the ridesourcing industry, drivers are often unable to quickly and accurately locate the waiting position of riders, but patrol or wait on the road, which will seriously affect the management of the road traffic order. It may be a good idea to provide an online virtual site for the taxi to facilitate convergence of the rider and driver. The concept of recommended pick-up point is presented in this paper. At present, ridesourcing service platforms on the market have similar functions, but they do not take into account whether the setting of the pick-up point is compatible with the actual traffic environment, resulting in some problems. We have invented a method to select the recommended pick-up point by integrating various traffic influencing factors, so as to ensure that the setting of the pick-up point is compatible with the actual traffic situation, which consists of three steps. Firstly, we studied the rider’s maximum tolerable waiting time and defined an attractive walking range for riders based on the huge amount of data. In the second step, we analyzed spatial distribution characteristics of the taxi demand hotspot and determined candidate pick-up locations. Lastly, the fuzzy analytic hierarchy method was used to select the recommended pick-up point that is most conducive to traffic management from multiple candidate points. A case study was conducted to validate the proposed approach and experimental evidence showed that recommended results based on the approach are in line with the actual situation of the road, and conducive to road traffic management. This recommendation method is based on real ridesourcing orders data.
A Pick-Up Points Recommendation System for Ridesourcing Service
In the ridesourcing industry, drivers are often unable to quickly and accurately locate the waiting position of riders, but patrol or wait on the road, which will seriously affect the management of the road traffic order. It may be a good idea to provide an online virtual site for the taxi to facilitate convergence of the rider and driver. The concept of recommended pick-up point is presented in this paper. At present, ridesourcing service platforms on the market have similar functions, but they do not take into account whether the setting of the pick-up point is compatible with the actual traffic environment, resulting in some problems. We have invented a method to select the recommended pick-up point by integrating various traffic influencing factors, so as to ensure that the setting of the pick-up point is compatible with the actual traffic situation, which consists of three steps. Firstly, we studied the rider’s maximum tolerable waiting time and defined an attractive walking range for riders based on the huge amount of data. In the second step, we analyzed spatial distribution characteristics of the taxi demand hotspot and determined candidate pick-up locations. Lastly, the fuzzy analytic hierarchy method was used to select the recommended pick-up point that is most conducive to traffic management from multiple candidate points. A case study was conducted to validate the proposed approach and experimental evidence showed that recommended results based on the approach are in line with the actual situation of the road, and conducive to road traffic management. This recommendation method is based on real ridesourcing orders data.
A Pick-Up Points Recommendation System for Ridesourcing Service
Wanqiu Zhu (Autor:in) / Jian Lu (Autor:in) / Yunxuan Li (Autor:in) / Yi Yang (Autor:in)
2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Microeconomic analysis of ridesourcing market regulation policies
| Elsevier | 2024
Understanding factors that impact ridesourcing service usage frequency: a case study in Shanghai
| Taylor & Francis Verlag | 2023
Machine learning approach for spatial modeling of ridesourcing demand
| Elsevier | 2022