This paper proposes an analytical framework based on stochastic geometry theory for investigating the joint coverage probability of two locations ( \(\ell _1\) and \(\ell _2\) ) in a vehicular networks. Particularly, we utilize the Transdimensional Poisson Point Process (TPPP) to model the actual distribution of base stations (BSs), where TPPP is a combination of 1D PPP and 2D PPP. Then, assume that the typical vehicle moves between the two locations \(\ell _1\) and \(\ell _2\) at a distance of v. With typical vehicle follows the closest BS association policy, two scenarios can occur: (i) the typical vehicle is associated with a new closer BS while moving to \(\ell _2\) , (ii) when the previous BS is still the nearest after moving, the vehicle is served by the same BS in both \(\ell _1\) and \(\ell _2\) . Our research shows that under the closest BS association policy, the joint coverage probability decreases with the increase of v, and the joint coverage probability is only the product of the individual coverage probabilities when v approaches infinity. In addition, we also investigate the effects of a series of network and channel parameters (e.g., BS density and \({\text {SIR}}\) target) on the joint coverage probability. These results provide important theoretical basis and practical reference for understanding coverage performance in vehicular networks.