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首页> 外文期刊>Arabian Journal for Science and Engineering >Genetic-Inspired Map Matching Algorithm for Real-Time GPS Trajectories
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Genetic-Inspired Map Matching Algorithm for Real-Time GPS Trajectories

机译：GPS轨迹的遗传启发式地图匹配算法

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摘要

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose realchallenges formap matching process. Genetic algorithms have recently been trialed for renderingGPS fix on digitalmaps. Thismanuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM)algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithmformap matching. The fitness function used byGiMMmakes use ofBucket Dijkstra’s and fast dynamic timewrapping (FDTW)algorithms to render GPS information on digital maps. Bucket Dijkstra’s suggests the shortest path available in between twopoints, andFDTWis responsible for comparing two data series. Unlike traditional genetic algorithm for mapmatching, GiMMwas evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real timeusing OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generationcount, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations andpopulation size were concluded to be the most influential parameters for the performance of genetic algorithms for mapmatching.
机译：复杂的道路网络，不正确的GPS接收器输出，低采样率以及许多其他相关问题构成了地图匹配过程的挑战。最近已经尝试使用遗传算法在数字地图上渲染GPS定位。该手稿介绍了一种改进的遗传算法，称为后处理遗传启发式地图匹配（GiMM）算法。提出的GiMM旨在减轻与最初提出的遗传算法形式匹配相关的固有挑战。 GiMM使用的适应度函数利用了Dicket的Bucket Dijkstra算法和快速动态时间包装（FDTW）算法在数字地图上呈现GPS信息。 Bucket Dijkstra提出了两点之间可用的最短路径，而FDTW负责比较两个数据序列。与用于地图匹配的传统遗传算法不同，GiMM在稀疏和密集GPS数据上进行了评估。 GiMM算法的性能使用OpenStreetMap数据和GPS数据集映射到82公里的道路网络上进行了实时评估。 GiMM使用人口规模，世代计数，准确性和执行时间作为输入参数。均方根误差为0.06时，最大准确度为99.4％，而均方根误差为0.47时，最小准确度为60％。结论是迭代次数和种群数量是影响地图匹配遗传算法性能的最重要参数。

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