1. bookVolume 32 (2022): Edizione 1 (March 2022)
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License
Formato
Rivista
eISSN
2083-8492
Prima pubblicazione
05 Apr 2007
Frequenza di pubblicazione
4 volte all'anno
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Inglese
access type Accesso libero

Sensor Location for Travel Time Estimation Based on the User Equilibrium Principle: Application of Linear Equations

Pubblicato online: 31 Mar 2022
Volume & Edizione: Volume 32 (2022) - Edizione 1 (March 2022)
Pagine: 23 - 33
Ricevuto: 01 Jun 2021
Accettato: 11 Nov 2021
Dettagli della rivista
License
Formato
Rivista
eISSN
2083-8492
Prima pubblicazione
05 Apr 2007
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese
Abstract

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

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): 35–43.10.3141/2338-05 Search in Google Scholar

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-2019-0016.10.2478/amcs-2019-0016 Search in Google Scholar

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): 49–58.10.3141/1886-07 Search in Google Scholar

Gentili, M. and Mirchandani, P.B. (2018). Review of optimal sensor location models for travel time estimation, Transportation Research C: Emerging Technologies 90: 74–96.10.1016/j.trc.2018.01.021 Search in Google Scholar

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): 60–68.10.3141/2160-07 Search in Google Scholar

Li, X. and Ouyang, Y. (2012). Reliable traffic sensor deployment under probabilistic disruptions and generalized surveillance effectiveness measures, Operations Research 60(5): 1183–1198.10.1287/opre.1120.1082 Search in Google Scholar

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. Search in Google Scholar

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-2018-0003.10.2478/amcs-2018-0003 Search in Google Scholar

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): 390–408.10.1080/15472450.2017.1298037 Search in Google Scholar

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): 857–871.10.1016/j.trb.2005.11.003 Search in Google Scholar

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): 99–107.10.3141/2027-13 Search in Google Scholar

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. Search in Google Scholar

Wardorp, J. (1952). Some theoretical aspects of road traffic research, ICE Proceedings Engineering Divisions 1(5): 767–768.10.1680/ipeds.1952.11362 Search in Google Scholar

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): 2273–2285.10.1109/TITS.2014.2314732 Search in Google Scholar

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): 26–40.10.1080/15472450.2016.1194206 Search in Google Scholar

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