Interactive route search in the presence of order constraints

Abstract

A route search is an enhancement of an ordinary geographic search. Instead of merely returning a set of entities, the result is a route that goes via entities that are relevant to the search. The input to the problem consists of several search queries, and each query defines a type of geographical entities. When visited, some of the entities succeed in satisfying the user while others fail to do so; however, only the probability of success is known prior to arrival. The main task is to find a route that visits at least one satisfying entity of each type. In an interactive search, the route is computed in steps. In each step, only the next entity of the route is given to the user, and after visiting that entity, the user provides a feedback specifying whether the entity satisfies her. This paper investigates interactive route search in the presence of order constraints that specify that some types of entities should be visited before others. We present heuristic algorithms for interactive route search for two cases, depending on whether the constraints define a complete order or a partial one. The main challenge is to utilize the feedback in order to compute a route that is shorter and has a higher degree of success, compared to routes that are computed non-interactively. We also discuss how to compare the results of the algorithms and introduce suitable measures for doing so. Experiments on real-world data illustrate the efficiency and effectiveness of our algorithms.

Interactive traffic-aware route search on smartphones

“…Kanza et al [10] showed that even finding the expected distance from any POI tō t is NP-Hard. When travel time varies, the computation is even more intricate.…”

“…As a heuristic, we place the POIs with the highest expected distance last in the order. An algorithm for approximating expected distance has been proposed in [10]. Finally, since the value of the penalty πi represents a failure to satisfy the subquery Qi, we define πi to be the time it takes to travel via all the POIs of Oi according to their j-order, spend a duration of d f at each POI except the last one, and a duration of ds in the last POI of Oi (this is similar to a solution that was proposed in [10]).…”

“…They allow utilizing user interaction to provide a route that is short and effective in satisfying the user [9,10]. In an interactive search, the route is computed in steps.…”

“…(Interactivity can also be achieved by providing a complete route to the user and recomputing the route in any case of a negative feedback.) The goal in previous papers [9,10] was to minimize the expected travel distance. For instance, the tourist in Example 1.1 provides a feedback after each visit at a shoe store to inform the system whether there is a need to visit additional shoe stores.…”

“…Previous papers (see [9,10]) on interactive route search dealt with the problem of how to compute the shortest route and did not take into account traffic conditions and temporal constraints. Other papers dealt with planning a route noninteractively while taking into account the effect of traffic and temporal constraints [1,7,8,16].…”