Department of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Korea.
Department of Computer Engineering, Changwon National University, Changwon 51140, Korea.
Sensors (Basel). 2022 Mar 29;22(7):2606. doi: 10.3390/s22072606.
Road speed is an important indicator of traffic congestion. Therefore, the occurrence of traffic congestion can be reduced by predicting road speed because predicted road speed can be provided to users to distribute traffic. Traffic congestion prediction techniques can provide alternative routes to users in advance to help them avoid traffic jams. In this paper, we propose a machine-learning-based road speed prediction scheme using road environment data analysis. The proposed scheme uses not only the speed data of the target road, but also the speed data of neighboring roads that can affect the speed of the target road. Furthermore, the proposed scheme can accurately predict both the average road speed and rapidly changing road speeds. The proposed scheme uses historical average speed data from the target road organized by the day of the week and hour to reflect the average traffic flow on the road. Additionally, the proposed scheme analyzes speed changes in sections where the road speed changes rapidly to reflect traffic flows. Road speeds may change rapidly as a result of unexpected events such as accidents, disasters, and construction work. The proposed scheme predicts final road speeds by applying historical road speeds and events as weights for road speed prediction. It also considers weather conditions. The proposed scheme uses long short-term memory (LSTM), which is suitable for sequential data learning, as a machine learning algorithm for speed prediction. The proposed scheme can predict road speeds in 30 min by using weather data and speed data from the target and neighboring roads as input data. We demonstrate the capabilities of the proposed scheme through various performance evaluations.
道路速度是交通拥堵的一个重要指标。因此，通过预测道路速度可以减少交通拥堵的发生，因为预测的道路速度可以提供给用户来分配交通流量。交通拥堵预测技术可以提前为用户提供替代路线，帮助他们避开交通拥堵。在本文中，我们提出了一种基于机器学习的道路速度预测方案，该方案使用道路环境数据分析。所提出的方案不仅使用了目标道路的速度数据，还使用了可能影响目标道路速度的相邻道路的速度数据。此外，所提出的方案可以准确地预测平均道路速度和快速变化的道路速度。所提出的方案使用了目标道路按星期几和小时组织的历史平均速度数据，以反映道路上的平均交通流量。此外，该方案分析了道路速度变化较快的路段的速度变化，以反映交通流量。由于事故、灾害和施工等意外事件，道路速度可能会迅速变化。所提出的方案通过将历史道路速度和事件作为道路速度预测的权重来预测最终的道路速度。它还考虑了天气条件。所提出的方案使用了适合顺序数据学习的长短时记忆（LSTM）作为速度预测的机器学习算法。该方案可以使用天气数据和目标及相邻道路的速度数据作为输入数据，在 30 分钟内预测道路速度。我们通过各种性能评估展示了所提出方案的能力。
