论文标题
基于Odtravel时间矩阵的旅行时间预测的神经网络模型
Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
论文作者
论文摘要
公共交通系统通勤者通常有兴趣获得准确的旅行时间信息来计划其日常活动。但是，由于道路交通不规则，由于天气状况，道路事故和交通拥堵等因素引起的道路交通不规则，通常难以准确预测此信息。在这项研究中，开发了两个神经网络模型，即多层（MLP）感知器和长期短期模型（LSTM），以预测忙碌的路线的链接旅行时间，并使用使用原始GPS数据集中衍生的原点预性旅行时间矩阵生成的输入。实验结果表明，这两个模型都可以做出近乎准确的预测，但是，随着时间步长的增加，LSTM更容易受到噪声的影响。
Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.
