Dynamic cluster-based over-demand prediction in bike sharing systems

Abstract

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Complementary base station clustering for cost-effective and energy-efficient cloud-RAN

“…Solution: Based on [13], [14], we propose a DistanceConstrained Complementarity-Aware (DCCA) algorithm to cluster base stations. The basic idea of DCCA is iteratively assigning base stations to the adjacent clusters, where the gain of assigning base station v to cluster C is iteratively evaluated by a value function as follows:…”

Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system

“…The majority of BSS analyses use data from big cities such as London [19,31,46,72,[75][76][77][78][79], Washington DC [12,17,27,47,56,76,[80][81][82][83][84][85], Paris [86][87][88][89][90][91][92][93][94], and New…”

“…York [17,81,89,[95][96][97]. Other studied data sets are from Chicago [73,80,98,99], Lyon [63,65,74,100], Boston [12,76,80,101], Barcelona [11,70,102], Hangzhou [15,16,103], Brisbane [61,83], Minneapolis [76,104], Vienna [105,106], Denver [76,84], Pisa [64,107], Dublin [14,108], Minnesota [84], Seville [102], Montreal [109] Helsinki [110], Vancouver [111], Nanjing [112], and Castellon [113].…”

“…Similar to the work on human mobility prediction in subsection 2.1.7 above, numerous prediction methods have been applied to BSS data. They have been investigated for various prediction purposes such as prediction of traffic flows [15,17], the available of bikes and/or vacant docking slots [47,70], number of trips [16], trips duration [73], level of demand [62,85], over-demand prediction [81], pairwise demand prediction [96], number of usage patterns [27], next place prediction [46], cycle lane usage [109], station occupancy [120], the potential destination station and arrival time [73], waiting time for the next available bike [14], and pair of pickup and return stations that close to the origin and destination places of users [73,96].…”

“…At this station level, the spatial correlation between stations can be investigated. Most of the existing BSS studies undertake prediction at systemlevel [10,11,21,49,70,81,90], only a few predict at a cluster level [65] and at the station level [2,87], and none at all investigate all three levels in one study. It would be expected that the system usage on an hourly basis will become more chaotic or unpredictable as prediction moves from system-wide to cluster to station level.…”