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TARS: traffic-aware route search

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Abstract

In a traffic-aware route search (TARS), the user provides start and target locations and sets of search terms. The goal is to find the fastest route from the start location to the target via geographic entities (points of interest) that correspond to the search terms, while taking into account variations in the travel speed due to changes in traffic conditions, and the possibility that some visited entities will not satisfy the search requirements. A TARS query may include temporal constraints and order constraints that restrict the order by which entities are visited. Since TARS generalizes the Traveling-Salesperson Problem, it is an NP-hard problem. Thus, it is unlikely to find a polynomial-time algorithm for evaluating TARS queries. Hence, we present in this paper three heuristics to answer TARS queries—a local greedy approach, a global greedy approach and an algorithm that computes a linear approximation to the travel speeds, formulates the problem as a Mixed Integer Linear Programming (MILP) problem and uses a solver to find a solution. We provide an experimental evaluation based on actual traffic data and show that using a MILP solver to find a solution is effective and can be done within a limited running time in many real-life scenarios. The local-greedy approach is the least effective in finding a fast route, however, it has the best running time and it is the most scalable.

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Notes

  1. In practice, the travel-time function depends on the date, e.g., the travel-time function for workdays may not be the same as the one for weekends, however, this is a technical issue, which we ignore to simplify the model. It can be handled by using different time functions according to the day of the travel.

  2. See http://msdn.microsoft.com/en-us/library/cc966826.aspx.

  3. http://developer.yahoo.com/search/local/V3/localSearch.html

  4. http://msdn.microsoft.com/en-us/library/cc966826.aspx

  5. http://www.gurobi.com/

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  1. Taub Building, Technion Israel Institute of Technology, Haifa, 32000, Israel

    Roy Levin & Yaron Kanza

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Correspondence to Roy Levin.

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Levin, R., Kanza, Y. TARS: traffic-aware route search. Geoinformatica 18, 461–500 (2014). https://doi.org/10.1007/s10707-013-0185-z

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