Smart IoT for Transportation Studies

The Internet of Things (IoT) is an environment in which physical “things” embedded with electronics, software, sensors, and network connectivity, with the ability to transfer data, and to generate collaborative outcomes. We have focused the applications of smart IoT in intelligent transportation systems.

Here are some case studies that WIOMAX researchers and engineers have done on prototypes and applications of smart IoT in intelligent transportation systems.

Smart IoT for Urban Traffic Control

The smart and scalable urban traffic control system can be seen as an Internet of smart intersections (things), where each intersection is an intelligent entity equipped with an artificial intelligence (AI) brain called schedule-driven intersection control (SchIC) to optimize traffic flow, using predictive traffic flow information from neighbors.

Smart and Scalable Urban Signal Networks: Methods and Systems for Adaptive Traffic Signal Control, 2015.

@misc{Xie2015c2tr,
author = {Xiao-Feng Xie and {et al}},
title = {{Smart and Scalable Urban Signal Networks: Methods and Systems for Adaptive Traffic Signal Control}},
year = {2015},
note = {U.S. Patent, No. 9,159,229},
dayfiled = {Oct 13, 2015},
nationality = {U.S.},
PDF={http://www.wiomax.com/team/xie/paper/US9159229.pdf},
LNK={http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=9159229},
yearfiled = {2014}
}

“Schedule-driven intersection control,” Transportation Research Part C, vol. 24, 2012.

@Article{Xie2012,
Title = {Schedule-driven intersection control},
Author = {Xiao-Feng Xie and S. Smith and Liang Lu and G. Barlow},
Journal = {Transportation Research Part C},
Year = {2012},
Pages = {168-189},
Volume = {24},
PDF={http://www.wiomax.com/team/xie/paper/TRC12.pdf},
Doi = {10.1016/j.trc.2012.03.004}
}

“Schedule-driven coordination for real-time traffic network control,” in International Conference on Automated Planning and Scheduling (ICAPS), Sao Paulo, Brazil, 2012.

@InProceedings{Xie2012c,
Title = {Schedule-driven coordination for real-time traffic network control},
Author = {Xiao-Feng Xie and S. Smith and G. Barlow},
Booktitle = {{International Conference on Automated Planning and Scheduling (ICAPS)}},
Year = {2012},
PDF={http://www.wiomax.com/team/xie/paper/ICAPS12.pdf},
VID={https://www.youtube.com/watch?v=d_BCzByquGg},
Address = {Sao Paulo, Brazil},
Pages = {323--331}
}

Smart IoT for Intersection Safety

In this project, we have proposed an integrated in-vehicle decision support system to help driver make better and safer stop/go decisions as a vehicle is approaching a signalized intersection.

“SIV-DSS: Smart in-vehicle decision support system for driving at signalized intersections with V2I communication,” Transportation Research Part C, vol. 90, 2018.

@Article{Xie2018SIV,
Title = {{SIV-DSS}: Smart in-vehicle decision support system for driving at signalized intersections with {V2I} communication},
Author = {Xiao-Feng Xie and Zun-Jing Wang},
Journal = {Transportation Research Part C},
Volume = {90},
Pages = {181--197},
PDF={http://www.wiomax.com/team/xie/paper/TRC18Pre.pdf},
Doi = {10.1016/j.trc.2018.03.008},
Year = {2018}
}

Supported by the IoT communications, the system has integrated and harnessed real-time information from both vehicle and infrastructure. Novel decision rules are designed as smart modules utilizing the key physical and behavioral inputs from vehicle motion, vehicle-driver characteristics, intersection geometry and topology, signal phase and timings, and the definition of red-light running (RLR) law. The performance has been evaluated with systematic simulation experiments. The results indicate that the smart system can not only ensure traffic safety by greatly reducing the probability of RLR violations down to zero, but also improve mobility by significantly reducing unnecessary stops at an intersection.

Other Research Work on Smart IoT

We also conduct other research work on traffic safety, which are reported in our blogs.