Major container ports may receive thousands of visits per day by trucks dropping off and/or picking up containers. Many of these trucks are now equipped with GPS devices so that their locations can be monitored at a coarse level, such as verifying that the truck went to the correct terminal. We can take advantage of this to measure the performance of truck operations within a container terminal at a finer level of detail than previously. In addition to measuring time in each service, this analysis can also identify the likely container type — dry or refrigerated, import or export or transshipment, etc. — so in many cases we can report processing times for every combination of service and container type. Furthermore this measurement is unobtrusive: It requires no interruption of operations and no extra record-keeping by the terminal. The only data required is access to GPS records and identification on a map of the various yards within the terminal.
We use a Hidden Markov Model and Machine Learning to compensate for inaccuracy of GPS and to match the GPS trail to the most plausible pattern of movement within the terminal. This enables us to infer the types of containers handled and how much time was spent at each step, including time queueing to enter the port and time in traffic. This gives a much more detailed view of port activities than the aggregate, typically reported "truck turn-time" (time from entering the gate to departing). Efforts at improvement can then be directed more precisely.