Traffic bottlenecks
are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as
trajectory-driven traffic bottleneck identification
: Given a road network
R
, a trajectory database
T
, find a representative set of seed edges of size
K
of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic
diffusion probability
between two connected segments and the
residual ratio
of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm
BF
, with an approximation ratio of 1-1/
e
. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm
SG
. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.
Identification of new road segments using a modified version of the k-means algorithm