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A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method
| 题名： | A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method |
| 正文语种： | eng |
| 作者： | Swastik Khadka;Peirong 'Slade' Wang;Pengfei 'Taylor' Li;Francisco J. Torres |
| 作者单位： | Dept. of Civil Engineering Univ. of Texas at Arlington Arlington TX 76019;Dept. of Civil Engineering Univ. of Texas at Arlington Arlington TX 76019;Dept. of Civil Engineering Univ. of Texas at Arlington Arlington TX 76019;Transportation System Modeler Dept. of Model and Data Development North Central Texas Council of Governments Arlington TX 76011 |
| 关键词： | Connected vehicle (CV) data; Travel demand estimation; Deep learning; Transportation planning; Deep neural network (DNN) |
| 摘要： | Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model's performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas Fort Worth, Texas, area. The results reveal good accuracy and robustness. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 4 |
| 页码： | 04023015.1-04023015.14 |
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