• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Annual Computers, Software, and Applications Conference >Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest
【24h】

Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest

机译：使用基于LSTM的深度序列模型和兴趣点进行出租车需求预测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).
机译：如今，城市交通在现代城市的城市规划，导航和其他交通服务中起着重要作用。出租车是大城市中至关重要的公共服务，每天有成千上万的乘客乘坐出租车。减少街头空置车辆的数量将有助于服务提供商增加驾驶员的收入，减少能源消耗，优化交通效率并控制大城市的空气污染问题。由于驾驶员对乘客和其他出租车的位置没有足够的信息，因此大多数驾驶员可能会开车到同一地区。由于缺乏乘客信息，当他们附近有需求量很大的区域时，他们往往最终没有接载任何乘客。为了解决这些问题，可以将机器学习技术应用于分析从IoT传感器获取的移动性数据，并帮助公司组织出租车队或最小化城市中乘客和驾驶员的等待时间。本文将基于LSTM的深度序列学习模型应用于智能城市中特定市区的出租车需求预测。为此，从Google Maps中提取城市中的兴趣点（POI），并将其与移动性数据源集成在一起。给定一个真实的数据集和两个评估指标，我们观察到每个市区的出租车需求可能会受到外部因素的影响，例如附近的位置和该地区的POI。结果表明，在均方误差（MSE）和对称平均绝对百分比误差（SMAPE）方面，该方法优于普通LSTM模型，并且平均误差低于基线方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号