Name
Abstract | ||
|---|---|---|
In this paper we develop a dynamic pricing framework for bicycle rental system to help achieve self-balance and apply this model to a simple two stations case. The first part is short-time demand forecasting for Origin-Destination (OD) pairs, using an Adaptive Difference Method. Such method integrates the information from both historical and real-time data to improve the precision of demand forecasting and estimation. Based on given OD demand, the second part illustrates a dynamic pricing model for self-balanced bicycle rental systems. We develop an optimum programming whose object is to achieve the self-balance of rental system under minimized society cost. We also employ discrete choice models to model the equilibrium between different travel modes. Lastly, this OD demand forecasting method and the dynamic pricing model are simulated in a two stations case. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FSKD.2014.6980830 | FSKD |
Keywords | Field | DocType |
od pairs,self-balanced bicycle rental systems,forecasting theory,travel modes,discrete choice model,mathematical programming,rental,dynamic pricing model,od demand forecasting method,demand estimation,difference method,society cost minimization,dynamic pricing framework,short-time demand forecasting,optimum programming,origin-destination pairs,adaptive difference method,bicycle rental system,discrete choice models,dynamic demand forecasting,minimisation,pricing,dynamic pricing,cost reduction | Demand forecasting,Computer science,Artificial intelligence,Utilization,Simulation,Dynamic pricing,Operations research,Dynamic demand,Discrete choice,Total cost,Stockout,Machine learning,Renting | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenbo Kuang | 1 | 0 | 0.34 |
| Zhengtian Xu | 2 | 0 | 0.34 |
| Huapu Lu | 3 | 34 | 7.46 |