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Travel time estimation is an important component in modern transportationapplications. The state of the art techniques for travel time estimation useGPS traces to learn the weights of a road network, often modeled as a directedgraph, then apply Dijkstra-like algorithms to find shortest paths. Travel timeis then computed as the sum of edge weights on the returned path. In order toenable time-dependency, existing systems compute multiple weighted graphscorresponding to different time windows. These graphs are often optimizedoffline before they are deployed into production routing engines, causing aserious engineering overhead. In this paper, we present STAD, a system thatadjusts - on the fly - travel time estimates for any trip request expressed inthe form of origin, destination, and departure time. STAD uses machine learningand sparse trips data to learn the imperfections of any basic routing engine,before it turns it into a full-fledged time-dependent system capable ofadjusting travel times to real traffic conditions in a city. STAD leverages thespatio-temporal properties of traffic by combining spatial features such asdeparting and destination geographic zones with temporal features such asdeparting time and day to significantly improve the travel time estimates ofthe basic routing engine. Experiments on real trip datasets from Doha, New YorkCity, and Porto show a reduction in median absolute errors of 14% in the firsttwo cities and 29% in the latter. We also show that STAD performs better thandifferent commercial and research baselines in all three cities.

STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation