{"title":"一种通用的基于特征的轨迹简化地图匹配框架","authors":"Yifang Yin, R. Shah, Roger Zimmermann","doi":"10.1145/3003421.3003426","DOIUrl":null,"url":null,"abstract":"Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s ∼ 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"A general feature-based map matching framework with trajectory simplification\",\"authors\":\"Yifang Yin, R. Shah, Roger Zimmermann\",\"doi\":\"10.1145/3003421.3003426\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s ∼ 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features.\",\"PeriodicalId\":210363,\"journal\":{\"name\":\"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3003421.3003426\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3003421.3003426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
摘要
准确的地图匹配是近年来备受关注的一个基础问题,也是一个具有挑战性的问题。它旨在通过将GPS点与数字地图上的道路网络相匹配来减少轨迹的不确定性。大多数现有的工作都集中在基于GPS观测估计候选路径的可能性上,而忽略了从驾驶员的角度建模路线选择的概率。本文提出了一种新的基于特征的地图匹配算法,该算法基于GPS观测值和人为因素来估计候选路径的成本。考虑人为因素是非常重要的,特别是在处理低采样率的数据时,大多数运动细节都丢失了。此外,我们还利用一种新的基于分段的概率地图匹配策略同时分析相干GPS点的子序列,该策略不易受定位数据噪声的影响。我们在一个公共的大规模GPS数据集上对所提出的方法进行了评估,该数据集由分布在世界各地的100条轨迹组成。实验结果表明,我们的方法对于大采样间隔(例如60 s ~ 300 s)和具有挑战性的轨迹特征(例如u形转弯和环路)的稀疏数据具有鲁棒性。与两种最先进的地图匹配算法相比,我们的方法将路径失配误差大幅降低了6.4% ~ 32.3%,并在所有不同采样率和挑战性特征的组合中获得了最佳的地图匹配结果。
A general feature-based map matching framework with trajectory simplification
Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s ∼ 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features.