Abstract
Large-scale geolocation telematics data acquired from connected vehicles has the potential to significantly enhance mobility infrastructures and operational systems within smart cities. To effectively utilize this data, it is essential to accurately match the geolocation data to the road segments. However, this matching is often not trivial due to the low sampling rate and errors exacerbated by multipath effects in urban environments. Traditionally, statistical modeling techniques such as Hidden-Markov models incorporating domain knowledge into the matching process have been extensively used for map-matching tasks. However, rule-based map-matching tasks are noise-sensitive and inefficient in processing large-scale trajectory data. Deep learning techniques directly learn the relationship between observed data and road networks from the data, often without the need for hand-crafted rules or domain knowledge. This renders them an efficient approach for map-matching large-scale datasets and makes them more robust to the noise. This paper introduces a sequence-to-sequence deep-learning model, specifically the transformer-based encoder-decoder model, to perform as a surrogate for map-matching algorithms. The encoder-decoder architecture initially encodes the series of noisy GPS points into a representation that automatically captures autoregressive behavior and spatial correlations between GPS points. Subsequently, the decoder associates data points with the road network features and thus transforms these representations into a sequence of road segments. The model is trained and evaluated using GPS traces collected in Manhattan, New York. Achieving an accuracy of 76%, transformer-based encoder-decoder models extensively employed in natural language processing presented a promising performance for translating noisy GPS data to the navigated routes in urban road networks.
Abstract (translated)
从连接的车辆中获得的较大规模的地理定位数据具有显著增强智能城市中交通基础设施和操作系统的潜力。要有效利用这些数据，必须准确地将地理定位数据与道路段匹配。然而，由于城市环境中多径效应的影响，这种匹配通常是困难的。传统上，用于地图匹配的任务中，如隐马尔可夫模型（HMM）等统计建模技术，已经广泛使用了。然而，基于规则的地图匹配任务对噪声敏感，处理大规模轨迹数据效率低下。深度学习技术直接从数据中学习观测数据与道路网络之间的关系，通常不需要手动规则或领域知识。这使得它们成为处理大规模数据集的有效的地图匹配方法，并使它们对噪声更加鲁棒。本文介绍了一种序列到序列的深度学习模型，特别是基于Transformer的编码器-解码器模型，作为地图匹配算法的代理。编码器-解码器架构最初将一系列噪声GPS点编码成一个自动捕捉自回归行为和GPS点之间空间关联的表示。随后，解码器将数据点与道路网络特征关联，从而将这些表示转换为一系列道路段。该模型使用纽约市GPS轨迹进行训练和评估。实现76%的准确度，基于Transformer的编码器-解码器模型在自然语言处理领域广泛应用，其对翻译噪声GPS数据到城市道路网络的导航路线表现出有前景的性能。
URL
https://arxiv.org/abs/2404.12460
