![](bga.png)
the four directions in the first hidden layer of
the network. This modular architecture performs
equally well and has fewer learnable parameters
in comparison with a feed-forward network.
ACKNOWLEDGEMENTS
The work was supported in part by NSF CNS
1922782 and by the Florida Department of transporta-
tion (FDOT) District 5. The opinions, findings and
conclusions expressed in this publication are those of
the authors and not necessarily those of FDOT D5.
REFERENCES
Chen, A., Chootinan, P., Ryu, S., Lee, M., and Recker, W.
(2012). An intersection turning movement estimation
procedure based on path flow estimator. Journal of
Advanced Transportation, 46:161 – 176.
Collette, A. (2013). Python and HDF5. O’Reilly.
Day, C. M., Bullock, D. M., Li, H., Remias, S. M., Hainen,
A. M., Freije, R. S., Stevens, A. L., Sturdevant, J. R.,
and Brennan, T. M. (2014). Performance measures for
traffic signal systems: An outcome-oriented approach.
Technical report.
Ghanim, M. S. and Shaaban, K. (2019). Estimating turn-
ing movements at signalized intersections using artifi-
cial neural networks. IEEE Transactions on Intelligent
Transportation Systems, 20(5):1828–1836.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers,
R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor,
J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer,
S., van Kerkwijk, M. H., Brett, M., Haldane, A., del
R’ıo, J. F., Wiebe, M., Peterson, P., G’erard-Marchant,
P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi,
H., Gohlke, C., and Oliphant, T. E. (2020). Array pro-
gramming with NumPy. Nature, 585(7825):357–362.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J.,
Fl
¨
otter
¨
od, Y.-P., Hilbrich, R., L
¨
ucken, L., Rummel, J.,
Wagner, P., and Wießner, E. (2018). Microscopic traf-
fic simulation using sumo. In The 21st IEEE Interna-
tional Conference on Intelligent Transportation Sys-
tems. IEEE.
Lv, Y., Duan, Y., Kang, W., Li, Z., and Wang, F. (2015).
Traffic flow prediction with big data: A deep learn-
ing approach. IEEE Transactions on Intelligent Trans-
portation Systems, 16(2):865–873.
P. T. Martin, M. C. B. (1992). Network programming to
derive turning movements from link flows. In Transp.
Res. Rec., no. 1365, pages 147–154. Transportation
Research Board.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J.,
Chanan, G., Killeen, T., Lin, Z., Gimelshein, N.,
Antiga, L., Desmaison, A., Kopf, A., Yang, E., De-
Vito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,
Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019).
PyTorch: An Imperative Style, High-Performance
Deep Learning Library.
Pengpeng Jiao, Huapu Lu, L. Y. (2005). Real-time esti-
mation of turning movement proportions based on ge-
netic algorithm. In Proceedings. 2005 IEEE Intelli-
gent Transportation Systems, 2005., pages 96–101.
Rocklin, M. (2015). Dask: Parallel computation with
blocked algorithms and task scheduling. In Huff,
K. and Bergstra, J., editors, Proceedings of the 14th
Python in Science Conference, pages 130 – 136.
US Department of Transportation, F. H. A. (2008). Traf-
fic signal timing manual. https://ops.fhwa.dot.gov/
publications/fhwahop08024/chapter4.htm. (Accessed
on 03/18/2021).
Wang, Y., Zhang, D., Liu, Y., Dai, B., and Lee, L. H. (2019).
Enhancing transportation systems via deep learning:
A survey. Transportation research part C: emerging
technologies, 99:144–163.
Wu, J. and Thnay, C. (2001). An o-d based method for
estimating link and turning volume based on counts.
In Transp. Res. Rec., no. 1365, pages 865–873. ITE
Dist 6.
Xu, K., Yi, P., Shao, C., and Mao, J. (2013). Development
and testing of an automatic turning movement identi-
fication system at signalized intersections. Journal of
Transportation Technologies, 03:241–246.
iMLTrans 2021 - Special Session on Intelligent Mobility, Logistics and Transport
744