Abstract
Dockless bike-sharing systems provide parking anywhere feature and environment-friendly approach for commuter. It is booming all over the world. Different from dockless bike-sharing systems, for example, previous studies focus on rental mode and docking stations planning. Yet, due to the fact that human mobility patterns of temporal and geographic lead to bike imbalance problem, we modeled human mobility patterns, predicted bike usage, and optimized management of the bike-sharing service. First, we proposed adaptive Geohash-grid clustering to classify bike flow patterns. For simplicity and rapid modeling, we defined three queuing models: over-demand, self-balance, and over-supply. Second, we improved adaptive Geohash-grid clustering-support vector machine algorithm to recognize self-balance pattern. Third, based on the result of adaptive Geohash-grid clustering-support vector machine, we proposed Markov state prediction model and Poisson mixture model expectation-maximization algorithm. Based on data set from Mobike and OFO, we conduct experiments to evaluate models. Results show that our models offer better prediction and optimization performance.
Highlights
With the development of technology, dockless bikesharing systems (BSSs) have been solved the last mile problem in intelligent city life.1 BSSs are booming all over the world, especially in large cities
Support vector machine is good at bi-classification problem, as the method can significantly reduce the need for labeled training instances
We proposed adaptive Geohash-grid clustering (AGC), improved SVM (ISVM), AGCMSP, and Poisson mixture model (PMM)-EM approaches compared with six baselines method
Summary
With the development of technology, dockless bikesharing systems (BSSs) have been solved the last mile problem in intelligent city life. BSSs are booming all over the world, especially in large cities. With the development of technology, dockless bikesharing systems (BSSs) have been solved the last mile problem in intelligent city life.. In the traditional self-service mode, users have to rent or return bike sharing at fixed stations. Based on mobile Internet, global positioning system (GPS), and location-based service (LBS), BSSs allow users to start or end service in community curbside, subway stations, and central business district (CBD) parking zone. Since about 2015, the central problems for municipal administration to solve include acquiring space to park the bikes and achieve efficient use of the bikes. According to bike sharing park-anywhere feature, the core of the issue is focused on two factors: This is a supply and demand planning problem that changes with temporal and geographic.. The truckbased and the user-based approaches are two baseline approaches to solve the bike imbalance issue According to bike sharing park-anywhere feature, the core of the issue is focused on two factors: This is a supply and demand planning problem that changes with temporal and geographic. The truckbased and the user-based approaches are two baseline approaches to solve the bike imbalance issue
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