信号动脉上的旅行时间信息
1Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
准确的旅行时间预测对通勤者至关重要. 新的异常过算法和长期短期记忆 (LSTM) 和卷积神经网络 (CNN) 集成将预测错误减少2.2%,节省显著的年度成本.
科学领域:
- 运输工程 运输工程
- 数据科学数据科学数据科学
- 交通管理 交通管理
背景情况:
- 旅行时间信息对于日常通勤至关重要,防止延误和公用事业损失.
- 使用车辆识别回应器收集旅行时间数据的现有方法需要强大的处理技术.
- 有效的旅行时间信息需要准确的异常选和可靠的旅行时间预测.
研究的目的:
- 开发用于运输网络中异常选和行程时间预测的先进算法.
- 提高旅行时间信息系统的准确性和可靠性.
- 量化增强旅行时间预测带来的经济效益.
主要方法:
- 开发了一个基于中位数的置信区间算法来过异常值,适用于郊区动脉.
- 集成长期短期记忆 (LSTM) 和卷积神经网络 (CNN) 进入LSTM-CNN模型,用于旅行时间预测.
- 使用来自专用短距离通信收发器的车辆识别数据.
主要成果:
- 拟议的异常选算法有效处理频繁的入口/出口点的旅行时间数据.
- 该LSTM-CNN模型准确地捕捉了长期趋势和旅行时间数据的本地模式.
- 与目前的做法相比,在拥挤的条件下,错误率降低了2.2%,在4公里长的研究地点每年可能节省135,200美元.
结论:
- 开发的算法显著提高了旅行时间信息系统的准确性.
- 准确的旅行时间预测通过减少延误和改善旅行者的实用性,带来了巨大的经济效益.
- 拟议的LSTM-CNN方法为实时交通数据处理和预测提供了一个有希望的解决方案.
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