PDF(Pubmed)
Abstract：
The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time.
摘要：
灵活性的增强，能源效率,和环境友好是城市基础设施发展中公认的趋势。各种类型的运输车辆的激增加剧了交通管制的复杂性。智能交通系统,利用实时交通状态预测技术，比如速度估计，成为有效管理和控制城市道路网络的可行解决方案。该项目的目的是解决使用深度学习技术提高大规模预测交通状况准确性的复杂任务。为了完成研究的目的，使用了一定时间范围内巴黎和伊斯坦布尔的历史交通数据，考虑到速度等变量的影响，交通量,和方向。具体来说,交通电影片段基于2年的现实世界数据为两个城市被利用。这些电影是使用从大量车队收集的超过1000亿个GPS（全球定位系统）探测点获得的HERE数据生成的。我们提出的模型，与以前的大多数不同，考虑到速度的累积影响，流量,和方向。与众所周知的模型相比，开发的模型显示出更好的结果，特别是,与SR-ResNet模型相比。巴黎和伊斯坦布尔的像素级MAE（平均绝对误差）值分别为4.299和3.884，与SR-ResNET的4.551和3.993相比。因此，所创建的模型展示了进一步提高智能交通系统的准确性和有效性的可能性，特别是在大型城市中心，从而促进提高安全性，能源效率,为道路使用者提供便利。获得的结果将对负责基础设施发展规划的当地决策者有用，以及该领域的专家和研究人员。未来的研究应该调查如何纳入更多的信息来源，特别是来自物理交通流模型的先前信息，有关天气状况的信息，等。进入深度学习框架，以及进一步增加生产能力和减少处理时间。
