Among the most important variants of the traveling salesman problem (TSP) are those relaxing the constraint that every locus should necessarily get visited, rather taking into account a revenue (prize) for visiting customers. In the Profitable Tour Problem (PTP), we seek for a tour visiting a subset of customers while maximizing net gain (profit) as difference between total revenue collected from visited customers and incurred traveling costs. The metric TSP can be modeled as a PTP with large revenues. As such, PTP is well-known to be NP-hard and also APX-hardness follows. Nevertheless, PTP is solvable in polynomial time on particular graph structures like lines, trees and circles. Following recent emphasis on robust optimization, and motivated by current flourishing of retail delivery services, we study the Probabilistic Profitable Tour Problem (PPTP), the generalization of PTP where customers will show up with a known probability, in their respective loci,only after the tour has been planned. Here, the selection of customers has to be made a priori, before knowing if a customer will actually submit his request or will not. While the tour has to be designed without this knowledge, revenues will only be collected from customers who will require the service. The objective is to maximize the expected net gain obtained by visiting only the customers that show up. We provide a polynomial time algorithm computing and characterizing the space of optimal solutions for the special case of the PPTP where customers are distributed on a line.