<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.</p>
</abstract>

Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.

Dynamic location models of mobile sensors for travel time estimation on a freeway

China University of Mining and Technology 1

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors,...

Dynamic location models of mobile sensors for travel time estimation on a freeway

Published Online: Jul 08, 2021

Page range: 271 - 287

Received: Dec 01, 2020

Accepted: Apr 10, 2021

DOI: https://doi.org/10.34768/amcs-2021-0019

Keywords
traffic mobile sensor, dynamic location model, travel time estimation, simulated annealing algorithm, data fusion

© 2021 Weiwei Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dynamic location models of mobile sensors for travel time estimation on a freeway

Published Online: Jul 08, 2021

Page range: 271 - 287

Received: Dec 01, 2020

Accepted: Apr 10, 2021

DOI: https://doi.org/10.34768/amcs-2021-0019

Keywordstraffic mobile sensor, dynamic location model, travel time estimation, simulated annealing algorithm, data fusion

© 2021 Weiwei Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
traffic mobile sensor, dynamic location model, travel time estimation, simulated annealing algorithm, data fusion