In urban logistics, analyzing urban traffic data plays an important role in achieving higher schedule reliability
and delivery time efficiency. To increase the diversity of urban traffic data, we developed a solution for the
automated collection and analysis of different types of traffic data. These are needed to optimize and control
the flow of traffic.
The use of traditional on-road sensors (e.g., inductive loops) for collecting data is necessary but not currently
sufficient because it cannot draw any conclusions about the type of goods being transported. In this paper,
we propose a framework in which different classes of delivery vehicles and types of goods being shipped
are identified in road videos by deep-learning-based image recognition method. Video sequences are
automatically evaluated according to the following criteria: (i) distinguish between individual and
commercial vehicles, (ii) identify the category of commercial vehicles, for example, van, box trucks, small
trucks, etc. (iii) identify the special features of the vehicle body (such as the name of the carrier) to classify
commercial transportation of food, general goods or package services, etc. Using this method, logistics
throughput of a designated city or region and the peak time of goods transportation can be obtained. This
provides the carrier with better pre-advice and potential actions to improve transportation efficiency. For the
evaluation of our framework, we collected real street videos at different time points in the main traffic arteries
of Heilbronn, Germany. In particular, the difference between traffic flow of logistics services before and
during the COVID-19 epidemic was compared. The results of implementation and testing demonstrated a
high-precision, low-latency performance of the framework for obtaining urban logistics data.
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