An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services

Abstract

Wearable and smartphone technology innovations have propelled the growth of Pedestrian Navigation Services (PNS). PNS need a map-matching process to project a user’s locations onto maps. Many map-matching techniques have been developed for vehicle navigation services. These techniques are inappropriate for PNS because pedestrians move, stop, and turn in different ways compared to vehicles. In addition, the base map data for pedestrians are more complicated than for vehicles. This article proposes a new map-matching method for locating Global Positioning System (GPS) trajectories of pedestrians onto road network datasets. The theory underlying this approach is based on the Fréchet distance, one of the measures of geometric similarity between two curves. The Fréchet distance approach can provide reasonable matching results because two linear trajectories are parameterized with the time variable. Then we improved the method to be adaptive to the positional error of the GPS signal. We used an adaptation coefficient to adjust the search range for every input signal, based on the assumption of auto-correlation between consecutive GPS points. To reduce errors in matching, the reliability index was evaluated in real time for each match. To test the proposed map-matching method, we applied it to GPS trajectories of pedestrians and the road network data. We then assessed the performance by comparing the results with reference datasets. Our proposed method performed better with test data when compared to a conventional map-matching technique for vehicles.

Variance optimization of UWB observation based on map matching for PPP/INS/UWB tightly coupled positioning

“…The latter can be applied to urban planning, road surveying systems and traffic guidance based on the data in the past. There are many useful algorithms for map matching, such as Frechet distance discrimination [28], spatial temporal (ST)-matching [29], interactive voting-based map matching (IVVM) algorithm [30] and so on [31]. In this contribution, without loss of generality, a ST-matching algorithm was adopted to realize online map matching.…”

Hidden Markov model and driver path preference for floating car trajectory map matching

“…These include methods based on Fréchet distance. For example, Bang et al [12] used the Fréchet distance to measure the similarity of the trajectory and the candidate paths. The matched road segments were retrieved by selecting the minimum value of the Fréchet distance.…”

“…The matched road segments in all candidate road segments of sampling points were used as the evaluation indicator for our experiments, this indicator A s is defined in the below equation A s = correctly matched road segments of the trajectory / all road segments of the trajectory (15) Our experiment consists of two parts, the first part is to evaluate the performance of our HMM-based method (HMM) by comparing with the well-cited algorithms proposed by Newson and Krumm [5] (HMM 1 ), Lou et al [6] (ST-matching), and Bang et al [12] (Frechet-based method). We run the comparison test on the route shown in Fig.…”

Cycling Segments Multimodal Analysis and Classification Using Neural Networks

“…Recently developed computational technologies enable the use of general methods of digital signal processing and computational intelligence for the analysis of multimodal signals for motion monitoring and for the evaluation of physical activities [1][2][3][4][5][6], fitness level [7], or rehabilitation progress. Data fusion of signals recorded with specific biosensors, positioning systems [8,9], and video cameras has a wide range of applications in detecting moving objects, in diagnosing neurological or motion disorders, and in the development of assisted living technologies [10][11][12].…”