  Nowadays, intelligent highway traffic network is playing an important role in
modern transportation infrastructures. A variable speed limit (VSL) system can
be facilitated in the highway traffic network to provide useful and dynamic
speed limit information for drivers to travel with enhanced safety. Such system
is usually designed with a steady advisory speed in mind so that traffic can
move smoothly when drivers follow the speed, rather than speeding up whenever
there is a gap and slowing down at congestion. However, little attention has
been given to the research of vehicles' behaviours when drivers left the road
network governed by a VSL system, which may largely involve unexpected
acceleration, deceleration and frequent lane changes, resulting in chaos for
the subsequent highway road users. In this paper, we focus on the detection of
traffic flow anomaly due to drivers' lane change intention on the highway
traffic networks after a VSL system. More specifically, we apply graph
modelling on the traffic flow data generated by a popular mobility simulator,
SUMO, at road segment levels. We then evaluate the performance of lane changing
detection using the proposed Lane-GNN scheme, an attention temporal graph
convolutional neural network, and compare its performance with a temporal
convolutional neural network (TCNN) as our baseline. Our experimental results
show that the proposed Lane-GNN can detect drivers' lane change intention
within 90 seconds with an accuracy of 99.42% under certain assumptions.
Finally, some interpretation methods are applied to the trained models with a
view to further illustrate our findings.
