پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 423 |
| تعداد مقالات | 5,542 |
| تعداد مشاهده مقاله | 6,470,727 |
| تعداد دریافت فایل اصل مقاله | 5,541,632 |
Deep learning model for express lane traffic forecasting | ||
| AUT Journal of Mathematics and Computing | ||
| مقاله 1، دوره 3، شماره 2، آذر 2022، صفحه 129-135 اصل مقاله (988.32 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2022.21395.1089 | ||
| نویسندگان | ||
| Farzad Karami1؛ Shahram Bohluli2؛ Chao Huang3؛ Nassim Sohaee* 4 | ||
| 1Amazon Inc., Austin, Texas, USA | ||
| 2Gradient Systematics LLC., Dallas, Texas, USA | ||
| 3Modern Mobility Partners LLC, Atlanta, Georgia, USA | ||
| 4Department of Information Technology and Decision Science, University of North Texas, Denton, Texas, USA | ||
| چکیده | ||
| Traffic forecasting plays a crucial role in the effective operation of managed lanes, as traffic demand and revenue are relatively volatile given parallel competition from adjacent, toll-free general purpose lanes. This paper proposes a deep learning framework to forecast short-term traffic volumes and speeds on managed lanes. A network of convolutional neural networks (CNN) was used to detect spatial features. Volume and speed were converted into heatmaps feeding into the CNN layers and temporal relationships were detected by a recurrent neural network (RNN) layer. A dense layer was used for the final prediction. Six months of historical volume and speed data on the I-580 Express Lanes in California, United States were utilized in this case study. Computational results confirm the effectiveness of the proposed data-driven deep learning framework in forecasting short-term traffic volumes and speeds on managed lanes. | ||
| کلیدواژه‌ها | ||
| Traffic forecast؛ Convolutional neural netwrok؛ Toll management | ||
| مراجع | ||
|
[1] J. Bates, Papers on stated preference methods in transport research, Journal of Transport Economics and Policy, 22 (1988), pp. 7–9.
[2] M. E. Ben-Akiva and S. R. Lerman, Discrete choice analysis: theory and application to travel demand, vol. 9, MIT press, 1985.
[3] E. Ben-Elia and Y. Shiftan, Which road do i take? a learning-based model of route-choice behavior with real-time information, Transportation Research Part A: Policy and Practice, 44 (2010), pp. 249–264.
[4] C. R. Bhat and S. Castelar, A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the san francisco bay area, Transportation Research Part B: Methodological, 36 (2002), pp. 593–616.
[5] M. C. Bliemer, J. M. Rose, and S. Hess, Approximation of bayesian efficiency in experimental choice designs, Journal of Choice Modelling, 1 (2008), pp. 98–126.
[6] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons, 1970.
[7] R. C. Boyer, W. T. Scherer, and M. C. Smith, Trends over two decades of transportation research: a machine learning approach, Transportation research record, 2614 (2017), pp. 1–9.
[8] Cambridge Systematics, Inc., Eastside corridor independent traffic and revenue study, September 2012. Final Report.
[9] C&M Associates Inc., Modeling for managed lane traffic and revenue forecasting, 2015. Final Report.
[10] , I-73 intermediate traffic and revenue study, south carolina, 2016. Final Report.
[11] , Ntta special projects system: Traffic and revenue bring down letter, 2016. Final Report.
[12] A. de Palma and N. Picard, Route choice decision under travel time uncertainty, Transportation Research Part A: Policy and Practice, 39 (2005), pp. 295–324.
[13] A. Dharia and H. Adeli, Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16 (2003), pp. 607–613.
[14] A. Elfar, A. Talebpour, and H. S. Mahmassani, Machine learning approach to short-term traffic congestion prediction in a connected environment, Transportation Research Record, 2672 (2018), pp. 185–195.
[15] D. A. Hensher, J. M. Rose, J. M. Rose, and W. H. Greene, Applied choice analysis: a primer, Cambridge university press, 2005.
[16] HNTB Corporation, Atlanta regional managed lane system plan, technical memorandum 6, 2010. Final Report.
[17] C. Huang, M. Burris, and W. D. Shaw, Differences in probability weighting for individual travelers: a managed lane choice application, Transportation, 44 (2017), pp. 375–393.
[18] , Models of transportation choice with risk: an application to managed lanes, Transportation letters, 9 (2017), pp. 276–295.
[19] C. A. Inc., Traffic & revenue study for i-66 express lanes outside the capital beltway, virginia, September 2015. Final Report.
[20] Larso Transportation Institute, Public perspective on traffic and revenue forecasts for public-private partnerships, 2017.
[21] J. Luan, F. Guo, J. Polak, N. Hoose, and R. Krishnan, Investigating the transferability of machine learning methods in short-term travel time prediction, in Transportation Research Board 97th Annual Meeting, Washington, DC, 2018.
[22] Y. Lv, X. Zhang, W. Kang, and Y. Duan, Managing emergency traffic evacuation with a partially random destination allocation strategy: A computational-experiment-based optimization approach, IEEE Transactions on Intelligent Transportation Systems, 16 (2015), pp. 2182–2191.
[23] MnDOT, MnPASS system study phase 3, 2017. Final Report.
[24] R. Phillips, C. Gocmen, and G. van Ryzin, Revenue maximizing dynamic tolls for managed lanes: A simulation study, 10 2015.
[25] S. Poddar, K. Ozcan, P. Chakraborty, V. Ahsani, A. Sharma, and S. Sarkar, Comparison of machine learning algorithms to determine traffic congestion from camera images, in Transportation Research Board 97th Annual Meeting, Washington, DC, 2018.
[26] B. Sana, J. Castiglione, D. Cooper, and D. Tischler, Using google’s passive data and machine learning for origin-destination demand estimation, Transportation Research Record, 2672 (2018), pp. 73–82.
[27] J. Schmidhuber and S. Hochreiter, Long short-term memory, Neural Comput., 9 (1997), pp. 1735–1780.
[28] WilburSmith Associates, SH 183 managed lanes intermediate level 2. Final Report. | ||
|
آمار تعداد مشاهده مقاله: 643 تعداد دریافت فایل اصل مقاله: 721 |
||