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We assume that the sequences of random times concerning the motions along each velocity follow two independent geometric counting processes of intensity $$\\lambda $$<\/jats:tex-math>\n \u03bb<\/mml:mi>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and that the resetting times are Poissonian with rate $$\\xi >0$$<\/jats:tex-math>\n \n \u03be<\/mml:mi>\n ><\/mml:mo>\n 0<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Under these assumptions we obtain the probability laws of the modified telegraph process describing the position and the velocity of the running particle. Our approach is based on the Markov property of the resetting times and on the knowledge of the distribution of the intertimes between consecutive velocity changes. We obtain also the asymptotic distribution of the particle position when (i) $$\\lambda $$<\/jats:tex-math>\n \u03bb<\/mml:mi>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> tends to infinity, and (ii) the time goes to infinity. In the latter case the asymptotic distribution arises properly as an effect of the resetting mechanism. A quite different behavior is observed in the two cases when $$v_2<0<v_1$$<\/jats:tex-math>\n \n \n v<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <<\/mml:mo>\n 0<\/mml:mn>\n <<\/mml:mo>\n \n v<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $$0<v_2<v_1$$<\/jats:tex-math>\n \n 0<\/mml:mn>\n <<\/mml:mo>\n \n v<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <<\/mml:mo>\n \n v<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Furthermore, we focus on the determination of the moment-generating function and on the main moments of the process describing the particle position under reset. Finally, we analyse the mean-square distance between the process subject to resets and the same process in absence of resets. Quite surprisingly, the lowest mean-square distance can be found for $$\\xi =0$$<\/jats:tex-math>\n \n \u03be<\/mml:mi>\n =<\/mml:mo>\n 0<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, for a positive $$\\xi $$<\/jats:tex-math>\n \u03be<\/mml:mi>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, or for $$\\xi \\rightarrow +\\infty $$<\/jats:tex-math>\n \n \u03be<\/mml:mi>\n \u2192<\/mml:mo>\n +<\/mml:mo>\n \u221e<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> depending on the choice of the other parameters.<\/jats:p>","DOI":"10.1007\/s10955-023-03189-1","type":"journal-article","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T07:02:36Z","timestamp":1700809356000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["On the Telegraph Process Driven by Geometric Counting Process with Poisson-Based Resetting"],"prefix":"10.1007","volume":"190","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4751-7341","authenticated-orcid":false,"given":"Antonio","family":"Di Crescenzo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8541-8120","authenticated-orcid":false,"given":"Antonella","family":"Iuliano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4583-2612","authenticated-orcid":false,"given":"Verdiana","family":"Mustaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4994-1694","authenticated-orcid":false,"given":"Gabriella","family":"Verasani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"issue":"1","key":"3189_CR1","first-page":"3","volume":"6","author":"L Angelani","year":"2018","unstructured":"Angelani, L., Garra, R.: Probability distributions for the run-and-tumble models with variable speed and tumbling rate. 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