  Similarity search is a fundamental but expensive operator in querying
trajectory data, due to its quadratic complexity of distance computation. To
mitigate the computational burden for long trajectories, neural networks have
been widely employed for similarity learning and each trajectory is encoded as
a high-dimensional vector for similarity search with linear complexity. Given
the sequential nature of trajectory data, previous efforts have been primarily
devoted to the utilization of RNNs or Transformers.
  In this paper, we argue that the common practice of treating trajectory as
sequential data results in excessive attention to capturing long-term global
dependency between two sequences. Instead, our investigation reveals the
pivotal role of local similarity, prompting a revisit of simple CNNs for
trajectory similarity learning. We introduce ConvTraj, incorporating both 1D
and 2D convolutions to capture sequential and geo-distribution features of
trajectories, respectively. In addition, we conduct a series of theoretical
analyses to justify the effectiveness of ConvTraj. Experimental results on
three real-world large-scale datasets demonstrate that ConvTraj achieves
state-of-the-art accuracy in trajectory similarity search. Owing to the simple
network structure of ConvTraj, the training and inference speed on the Porto
dataset with 1.6 million trajectories are increased by at least $240$x and
$2.16$x, respectively. The source code and dataset can be found at
\textit{\url{https://github.com/Proudc/ConvTraj}}.
