  In this paper, we study the problem of \textsc{Utility Driven Job Selection}
on Road Networks for which the inputs are: a road network with the vertices as
the set of Point-Of-Interests (Henceforth mentioned as POI) and the edges are
road segments joining the POIs, a set of jobs with their originating POI,
starting time, duration, and the utility. A worker can earn the utility
associated with the job if (s)he performs this. As the jobs are originating at
different POIs, the worker has to move from one POI to the other one to take up
the job. Some budget is available for this purpose. Any two jobs can be taken
up by the worker only if the finishing time of the first job plus traveling
time from the POI of the first job to the second one should be less than or
equal to the starting time of the second job. We call this constraint as the
temporal constraint. The goal of this problem is to choose a subset of the jobs
to maximize the earned utility such that the budget and temporal constraints
should not be violated. We present two solution approaches with detailed
analysis. First one of them works based on finding the locally optimal job at
the end of every job and we call this approach as the \emph{Best First Search
Approach}. The other approach is based on the Nearest Neighbor Search on road
networks. We perform a set of experiments with real\mbox{-}world trajectory
datasets to demonstrate the efficiency and effectiveness of the proposed
solution approaches. We observe that the proposed approaches lead to more
utility compared to baseline methods.
