3 research outputs found
Impact analysis of accidents on the traffic flow based on massive floating car data
The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion
Identifying important nodes with strong propagation capabilities in road
networks is a significant topic in the field of urban planning. However,
existing methods for evaluating the importance of nodes in traffic network
consider only topological information and traffic volumes, ignoring the
diversity of characteristics in road networks, such as the number of lanes and
average speed of road segments, limiting their performance. To solve this
problem, we propose a graph learning-based framework (MGL2Rank) that integrates
the rich characteristics of road network for ranking the importance of nodes.
In this framework, we first develop an embedding module that contains a
sampling algorithm (MGWalk) and an encoder network to learn latent
representation for each road segment. MGWalk utilizes multi-graph fusion to
capture the topology of the road network and establish associations among road
segments based on their attributes. Then, we use the obtained node
representation to learn the importance ranking of road segments. Finally, we
construct a synthetic dataset for ranking tasks based on the regional road
network of Shenyang city, and our ranking results on this dataset demonstrate
the effectiveness of our proposed method. The data and source code of MGL2Rank
are available at https://github.com/ZJ726