Fleet Management Optimisation with Spatio-Temporal Demand Forecasting in MaaS for Free-floating Micro-mobility
| dc.contributor.author | Almström, Oscar | |
| dc.contributor.author | Carlsson, Erik | |
| dc.contributor.author | Cronqvist, Daniel | |
| dc.contributor.author | Karlsson, Max | |
| dc.contributor.author | Lilliecreutz, Fredrik | |
| dc.contributor.author | Viala Bellander, Alexander | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Dubhashi, Devdatt | |
| dc.contributor.supervisor | Carlsson, Emil | |
| dc.date.accessioned | 2021-09-21T07:29:43Z | |
| dc.date.available | 2021-09-21T07:29:43Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | In recent years, micro-mobility services have grown rapidly. Companies such as Voi, Bolt and Lime are front figures in this development. Utilising their resources efficiently through fleet management has been an essential factor in reaching profitability. Gathering data is becoming more critical for companies, enabling them to use data science and mathematical models to leverage their business. This report aims to examine the opportunity to use different data-driven models to predict future demand for micro-mobility services. Voi Technology provided real market data for this paper, limited to Gothenburg. All geospatial data was aggregated to geospatial units defined by Uber’s H3 spatial index. Furthermore, the research focused on the following methods; Markov properties, Time Series Analysis and Poisson processes. These methods were implemented in the programming language Python. The Markov and Prophet models provided solid demand predictions. However, due to sparse data representation, for some areas of the city, the data was clustered to give more accurate forecasts at the cost of geospatial granularity. Comparing the two, the Prophet models gave better results alone, while the Markov models instead gave insight into how the supply moves around in the market. With further work, the Markov model could prove favourable for a fleet operator during rebalancing due to its ability to track the vehicle flow and predict lack and surplus of supply. The data indicated significant seasonality effects and sporadic behaviour. FBprophet showed decent results in analysing those characteristics, using a sliding window technique as a significant contributor. Supporting FBprophet with several features improved the prediction, where rain stood out as the most impactful feature. The Poisson proccess model interprets demand as non-homogeneous and stochastic with inherent temporal randomness. While the theory behind the Poisson process model seems relevant, the results remain inconclusive. FBprophet performed the best demand prediction in contrast to the other models. However, further research could contradict this, and there is room for deeper exploration. Onwards, Neural Networks and Deep Learning are also exciting subjects for further research in demand forecasting. | sv |
| dc.identifier.coursecode | TKDAT | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304158 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | data analysis | sv |
| dc.subject | micro-mobility | sv |
| dc.subject | MaaS | sv |
| dc.subject | demand forecasting | sv |
| dc.subject | Markov chains | sv |
| dc.subject | time series analysis | sv |
| dc.subject | fleet optimisation | sv |
| dc.subject | FBProphet | sv |
| dc.subject | Poisson processes | sv |
| dc.title | Fleet Management Optimisation with Spatio-Temporal Demand Forecasting in MaaS for Free-floating Micro-mobility | sv |
| dc.type.degree | Examensarbete på kandidatnivå | sv |
| dc.type.uppsok | M2 |