Map matching is a key part of many GIS applications, linking observed GPS traces to road networks via a map. But when that map contains errors such as missing or mislabeled roads, map matching can give poor or even misleading results. Here, an approach to tracking vehicles able to move both on and off known road networks is introduced that efficiently unifies existing hidden Markov model (HMM) approaches for map matching and standard free-space tracking methods (e.g. Kalman smoothing) in a principled way. In addition to avoiding generating misleading map-matching output, this approach has applications in learning map information from GPS traces, for example detecting unmapped or incorrectly mapped roads and parking lots. The approach is a form of interacting multiple model (IMM) filter subject to an additional assumption on the type of model interaction permitted. This allows an efficient formulation, here termed a semi-interacting multiple model (sIMM) filter. A forward filter (suitable for realtime tracking) and backward MAP sampling step (suitable for MAP trajectory inference and map matching) are described. The framework set out here is agnostic to the specific tracking models used, and makes clear how to replace these components with others of a similar type.

翻译：地图匹配是许多地理信息系统应用的关键部分,通过地图将观测到的GPS痕迹与道路网连接起来。但当该地图含有缺失或标签错误的道路等错误时,地图匹配可能会产生差错,甚至误导性的结果。在这里,引入了一种跟踪能够在已知的道路网内外移动的车辆的方法,以高效统一现有的隐藏的Markov模型(HMM)方法,用于地图匹配和标准自由空间跟踪方法(例如,Kalman平滑),这是有原则的。除了避免产生误导性的地图匹配输出外,这一方法还可用于从GPS痕迹中学习地图信息,例如探测未绘制或错误绘制的道路和停车场。该方法是一种互动的多模型过滤器形式,但需在允许的模型类型上附加额外假设。这允许一种高效的配方,这里称为半互动的多个模型(sIMM)过滤器。一个前端过滤器(适合实时跟踪)和后端的MAP取样步骤(可用于MAP轨迹的推断和地图匹配)。在这里设置的框架可以取代特定的跟踪模型类型。