Vehicle scheduling approach and its practice to optimise public bicycle redistribution in Hangzhou

Liu, Dong-Xu; Dong, Hongzhao; Li, Tiebei; Corcoran, Jonathan; Ji, Shiming

doi:10.1049/iet-its.2017.0274

Cited by 10 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An analogous problem that has received much more attention in the past is the prediction of the availability of bicycles at public bicycle sharing stations [26][27][28][29][30][31][32][33][34][35]. The problems are comparable since there are a number of spots that can either be occupied or not and it is relevant to determine when stations are out of bicycles to rent.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the context of this paper, the aggregation unit is the individual CS, with the corresponding high noise making such tools unsuitable. A selection of employed machine learning models includes random forest models [26], deep learning models [27], k-means clustering [31,32], or, less frequently, models such as the averaged one dependence estimators with subsumption resolution model [33]. Most of these models are in fact quite useful for our purpose as well and have been investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

2021

View full text Add to dashboard Cite

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

2021

View full text Add to dashboard Cite

show abstract

“…Imbalanced bicycle supply is one of the major problems facing by PBS systems [9,13,32]. In [32], Zhao et al studied the dispatching and management of DL-PBS systems and established a semi-open dispatching model based on fuzzy time windows.…”

Section: Related Workmentioning

confidence: 99%

“…Different from SD-PBS systems, the bicycle dispatching of DL-PBS systems is more complex due to the uncertainty of the location of bicycle stations and the requirements of bicycles. In [13], Liu et al focused on the DL-PBS bicycle dispatching and divided the DL-PBS networks by the K-means clustering algorithm. They built a bicycle dispatching model based on a rolling horizon dispatching algorithm, which can effectively guide bicycle redistribution between bicycle stations.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

Chen

et al. 2021

ACM Trans. Cyber-Phys. Syst.

View full text Add to dashboard Cite

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

show abstract

A data-driven clustering method for redistribution timing of a public bicycle sharing program

Liu,

Dong

2023

Public Transp

View full text Add to dashboard Cite

Vehicle scheduling approach and its practice to optimise public bicycle redistribution in Hangzhou

Cited by 10 publications

References 23 publications

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

A data-driven clustering method for redistribution timing of a public bicycle sharing program

Contact Info

Product

Resources

About