Sensor location for travel time estimation based on the user equilibrium principle: Application of linear equations
International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 1, pp. 23-33.

Voir la notice de l'article provenant de la source Library of Science

Travel time is a fundamental measure in any transportation system. With the development of technology, travel time can be automatically collected by a variety of advanced sensors. However, limited by objective conditions, it is difficult for any sensor system to cover the whole transportation network in real time. In order to estimate the travel time of the whole transportation network, this paper gives a system of linear equations which is constructed by the user equilibrium (UE) principle and observed data. The travel time of a link which is not covered by a sensor can be calculated by using the observed data collected by sensors. In a typical transportation network, the minimum number and location of sensors to estimate the travel time of the whole network are given based on the properties of the solution of a systems of linear equations. The results show that, in a typical network, the number and location of sensors follow a certain law. The results of this study can provide reference for the development of transportation and provide a scientific basis for transportation planning.
Keywords: travel time estimation, sensor location, user equilibrium principle, linear equations
Mots-clés : szacowanie czasu podrózy, lokalizacja czujnika, równanie liniowe
@article{IJAMCS_2022_32_1_a2,
     author = {Cao, Shuhan and Shao, Hu and Shao, Feng},
     title = {Sensor location for travel time estimation based on the user equilibrium principle: {Application} of linear equations},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {23--33},
     publisher = {mathdoc},
     volume = {32},
     number = {1},
     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_1_a2/}
}
TY  - JOUR
AU  - Cao, Shuhan
AU  - Shao, Hu
AU  - Shao, Feng
TI  - Sensor location for travel time estimation based on the user equilibrium principle: Application of linear equations
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2022
SP  - 23
EP  - 33
VL  - 32
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_1_a2/
LA  - en
ID  - IJAMCS_2022_32_1_a2
ER  - 
%0 Journal Article
%A Cao, Shuhan
%A Shao, Hu
%A Shao, Feng
%T Sensor location for travel time estimation based on the user equilibrium principle: Application of linear equations
%J International Journal of Applied Mathematics and Computer Science
%D 2022
%P 23-33
%V 32
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_1_a2/
%G en
%F IJAMCS_2022_32_1_a2
Cao, Shuhan; Shao, Hu; Shao, Feng. Sensor location for travel time estimation based on the user equilibrium principle: Application of linear equations. International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 1, pp. 23-33. http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_1_a2/

[1] [1] Asudegi, M. and Haghani, A. (2013). Optimal number and location of node-based sensors for collection of travel time data in networks, Transportation Research Record: Journal of the Transportation Research Board 2338(1): 35–43.

[2] [2] Chang, B.-J., Hwang, R.-H., Tsai, Y.-L., Yu, B.-H. and Liang, Y.-H. (2019). Cooperative adaptive driving for platooning autonomous self driving based on edge computing, International Journal of Applied Mathematics and Computer Science 29(2): 213–225, DOI: 10.2478/amcs-2019-0016.

[3] [3] Chen, A., Chootinan, P. and Pravinvongvuth, S. (2004). Multiobjective model for locating automatic vehicle identification readers, Transportation Research Record: Journal of the Transportation Research Board 1886(1): 49–58.

[4] [4] Gentili, M. and Mirchandani, P.B. (2018). Review of optimal sensor location models for travel time estimation, Transportation Research C: Emerging Technologies 90: 74–96.

[5] [5] Haghani, A., Hamedi, M., Sadabadi, K., Young, S. and Tarnoff, P. (2010). Data collection of freeway travel time ground truth with Bluetooth sensors, Transportation Research Record: Journal of the Transportation Research Board 2160(1): 60–68.

[6] [6] Li, X. and Ouyang, Y. (2012). Reliable traffic sensor deployment under probabilistic disruptions and generalized surveillance effectiveness measures, Operations Research 60(5): 1183–1198.

[7] [7] Mazaré, P., Tossavainen, O. and Bayen, A. (2012). Trade-offs between inductive loops and GPS probe vehicles for travel time estimation: Mobile century case study, Transportation Research Board 91st Annual Meeting, Washington DC, USA, Paper no. 2746.

[8] [8] Patan, M. and Kowalów, D. (2018). Distributed scheduling of measurements in a sensor network for parameter estimation of spatio-temporal systems, International Journal of Applied Mathematics and Computer Science 28(1): 39–54, DOI: 10.2478/amcs-2018-0003.

[9] [9] Sánchez-Cambronero, S., Jiménez, P., Rivas, A. and Gallego, I. (2017). Plate scanning tools to obtain travel times in traffic networks, Journal of Intelligent Transportation Systems 21(5): 390–408.

[10] [10] Sherali, H.D., Desai, J. and Rakha, H. (2006). A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times, Transportation Research B: Methodological 40(10): 857–871.

[11] [11] Soriguera, F., Thorson, L. and Robusté, F. (2007). Travel time measurement using toll infrastructure, Transportation Research Record: Journal of the Transportation Research Board 2027(1): 99–107.

[12] [12] Sun, W., Shen, L., Shao, H. and Liu, P. (2021). Dynamic location models of mobile sensors for travel time estimation on a freeway, International Journal of Applied Mathematics and Computer Science 31(2): 271–287, DOI: 10.34768/amcs-2021-0019.

[13] [13] Wardorp, J. (1952). Some theoretical aspects of road traffic research, ICE Proceedings Engineering Divisions 1(5): 767–768.

[14] [14] Zhu, N., Liu, Y., Ma, S. and He, Z. (2014). Mobile traffic sensor routing in dynamic transportation systems, IEEE Transactions on Intelligent Transportation Systems 15(5): 2273–2285.

[15] [15] Zhu, N., Ma, S. and Zheng, L. (2017). Travel time estimation oriented freeway sensor placement problem considering sensor failure, Journal of Intelligent Transportation Systems 21(1): 26–40.