A mathematical programming technique for matching time-stamped records in logistics and transportation systems

• Published in: Transportation research. Part C, Emerging technologies Vol. 69; pp. 375 - 385
• Main Authors: Smith, L. Douglas, Ehmke, Jan Fabian
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.08.2016
• Subjects: Airports; Automated matching; Data mining; Estimating; Failure; Logistics; Matching; Mathematical programming; Preprocessing; Preserves; Taxiing; Time-stamped records; Transportation; Automated matching; Preprocessing; Data mining; Mathematical programming; Time-stamped records
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2016.06.007

•Mathematical programming is used to match time-stamped records.•This extracts relevant information from systems with recording errors and omissionss.•It enables systematic exploration of a range of possible interpretations of the data.•The technique is robust for automatic pre-processing of data from new technologies. Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.