We present a method to classify street networks using only geo-tagged street-level imagery. By combining pre-trained image embeddings with unsupervised clustering, it produces visually coherent street typologies without supervised training or labeled data and requires only minimal data curation. The approach is lightweight, scalable, and, in principle, transferable across urban contexts. In a Delft (Netherlands) case study, we classify approximately 2,000 road sections using over 70,000 images. Our method recovers distinct street types such as residential, arterial, and historic ones. These results show that pre-trained visual embeddings alone can support effective street classification from visual inputs, offering a practical tool for urban planning, transport analysis, and mobility research.

By installing wireless sensors such as Bluetooth or Wi-Fi at a specific set of intersections and/or roadways it is possible to detect the passage of vehicles equipped with these technologies. However, Wi-Fi or Bluetooth-equipped vehicles are not necessarily detected by every sensor they pass by, as the detection probability depends on several factors, such as weather, nearby infrastructure, or the vehicle’s speed. To address this lack of perfect information, we propose a methodology to infer the most likely route used by a vehicle between two successive detections. The methodology consists of three stages. The first stage entails constructing a graph of the road network and the location of the sensors. The second stage consists of using the wireless data to calibrate the distribution of dwell time at each node and travel time for each link of the graph defined in the first stage. The third and final stage consists of convoluting node and link time distributions between successive detections to obtain an aggregate time distribution for each potential route. A Bayesian inference is then applied based on the travel time observed for each vehicle and the number of missed detections, to determine the probability of each alternative route. The methodology is tested through microsimulations, showing a prediction performance of over 90% in most favorable scenarios tested. When compared to a benchmark approach to infer routes, the proposed methodology provides better results when the network’s sensory density is low and data available are reduced.