It is well known that the location of Automatic Vehicle Identification (AVI) sensors on a traffic network for route flow estimation is a complex problem. Heuristics and metaheuristic tools have been proved to provide good results mainly for large networks where exact approaches require high (exponential) computational times. The numerous contributions in the literature mainly focus on the assumption of a steady state condition of the network flows and limited studies have addressed the problem accounting for the dynamic nature of the mobility and the uncertain knowledge of traffic conditions (i.e., the actual routes, the actual O-D matrix, etc.). In this paper, we propose a genetic algorithm to find high quality solutions to the problem of locating AVI sensors on a traffic network assuming uncertainty in the actually used routes of the network. We use the orthogonal array technique to determine the parameter settings of the algorithm to ensure robustness of the subsequent route flows estimation. Furthermore, we have tested our approach on different networks to show its effectiveness.

An iterative multiparametric approach for determining the location of AVI sensors for robust route flow estimation

Cerulli R.;Gentili M.;
2022-01-01

Abstract

It is well known that the location of Automatic Vehicle Identification (AVI) sensors on a traffic network for route flow estimation is a complex problem. Heuristics and metaheuristic tools have been proved to provide good results mainly for large networks where exact approaches require high (exponential) computational times. The numerous contributions in the literature mainly focus on the assumption of a steady state condition of the network flows and limited studies have addressed the problem accounting for the dynamic nature of the mobility and the uncertain knowledge of traffic conditions (i.e., the actual routes, the actual O-D matrix, etc.). In this paper, we propose a genetic algorithm to find high quality solutions to the problem of locating AVI sensors on a traffic network assuming uncertainty in the actually used routes of the network. We use the orthogonal array technique to determine the parameter settings of the algorithm to ensure robustness of the subsequent route flows estimation. Furthermore, we have tested our approach on different networks to show its effectiveness.
2022
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4779263
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact