Journey Data Based Arrival Forecasting for Bicycle Hire Schemes
Journey Data Based Arrival Forecasting for Bicycle Hire Schemes
复制标题
基于行程数据的自行车租赁计划到达预测
DOI：
10.1007/978-3-642-39408-9_16
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
J. Bradley
中科院分区：
文献类型：
--
作者：
Marcel C. Guenther;J. Bradley
全球城市自行车租赁计划的兴起最近引起了性能和建模研究界的广泛关注。一个特别重要的挑战是准确预测未来自行车迁移趋势，因为这有助于服务提供商确保停靠站的自行车和停车位的可用性，这对于满足客户期望至关重要。这项研究着眼于如何使用有关个人旅程的历史信息来改进小群体停靠站的间隔到达预测。具体来说，我们比较了两种类型模型的小区域到达预测的性能：平均场可分析时间非均匀群体 CTMC 模型 (IPCTMC) 和具有 ARIMA 误差的多元线性回归模型 (LRA)。这些模型使用伦敦巴克莱自行车租赁计划的历史高峰时段旅程数据进行验证，该计划用于训练模型并测试其预测准确性。
The global emergence of city bicycle hire schemes has recently received a lot of attention in the performance and modelling research community. A particularly important challenge is the accurate forecast of future bicycle migration trends, as these assist service providers to ensure availability of bicycles and parking spaces at docking stations, which is vital to match customer expectations. This study looks at how historic information about individual journeys could be used to improve interval arrival forecasts for small groups of docking stations. Specifically, we compare the performance of small area arrival predictions for two types of models, a mean-field analysable time-inhomogeneous population CTMC model (IPCTMC) and a multiple linear regression model with ARIMA error (LRA). The models are validated using historical rush hour journey data from the London Barclays Cycle Hire scheme, which is used to train the models and to test their prediction accuracy.
影响因子：
6.1
作者：
O'Brien, Oliver;Cheshire, James;Batty, Michael
通讯作者：
Batty, Michael
DOI：
10.1016/j.tcs.2010.02.001
发表时间：
2010-05
期刊：
Theor. Comput. Sci.
影响因子：
--
作者：
R. A. Hayden;J. Bradley
通讯作者：
R. A. Hayden;J. Bradley