Abstract:
Traffic speed prediction using deep learning neural networks has been the topic of many
studies, mostly using data sets that were collected from static sensors. Floating vehicle
data offers a more flexible alternative, as it can be obtained for any roads travelled by GPS
tracked vehicles. In this paper we compare the performance of leading traffic speed
prediction techniques when applied to both static sensor and floating vehicle data sets.
Data sets were collected for the road networks serving Johannesburg, representing South
Africa’s most congested roads. Based on prediction accuracy, training time and robustness
the Graph WaveNet method produced the best results. We found that the static sensor
and floating vehicle data sets, representing traffic movements on the same sets of roads,
produced comparable results, providing evidence that static sensor data can be
complemented and, in some cases, replaced by floating vehicle data. This will enable
traffic speed prediction for roads where no static sensors are installed, resulting in
significant cost savings. Extending traffic speed prediction to all major roads will result in
improved traffic management strategies for the overall road network, leading to less
congestion and an improved road user experience.
