基于谷歌地图应用中的路线图颜色的马迪恩市铁路道口密度盘点"

A. Prasetyo, Dara Aulia Feryando
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引用次数: 0

摘要

通过这项研究,有望了解铁路道口周围交通密度水平的特点,为提高印尼铁路道口的安全和效率做出贡献。马迪恩市铁路道口的车辆交通密度清单如何?根据谷歌地图应用程序中高速公路地图的颜色状况。所采用的方法是在谷歌地图应用程序上收集指定 JPL 点一定时间段内的交通状况历史数据,即在早高峰、午高峰和晚高峰期间,交通密度状况数据以一定的颜色表示。作为对比数据,谷歌地图上的实时交通数据样本将被分析并确定铁路道口的密度水平。通过数据分析,确定了马迪恩市容易发生拥堵的铁路道口点。通过对铁路道口密度数据的分析,将确定具有高、中和低密度水平的道口点。颜色状况数据显示,JPL 138 号线上的地块道口频率百分比为 50.37%,绿色距离占 47.71%,车辆通行密度水平为不密集至相当密集。位于 Jalan Yos Sudarso - Pahlawan 一侧的 JPL 138 过境点的交通密度较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inventarisasi Kepadatan Perlintasan Sebidang Kereta Api Berdasarkan Warna Peta Jalan pada Aplikasi Google Maps di Kota Madiun”
Through this research, it is expected to know the characteristics of the level of traffic density around railway crossings and contribute to improving the safety and efficiency of railway crossings in Indonesia.How is the inventory of vehicle traffic density at railway crossings in Madiun City? Based on the color condition of the highway map in the Google Maps application. The method carried out is to collect historical data on traffic conditions on the google map application at a specified JPL point with a certain period of time, namely during the morning, afternoon and evening rush hours, traffic density condition data is shown with certain color indications. As comparison data, samples of live-traffic data are taken on Google Maps which will be analyzed and identified the level of density of railway crossings. Data analysis was carried out to identify railway crossing points that are prone to congestion in the city of Madiun. Through analysis of railway crossing density data, crossing points that have high, medium and low density levels will be identified. Color condition data shows that the plot crossing on JPL 138 has a frequency percentage of 50.37% and 47.71% of the distance is green, with the level of vehicle traffic density is not dense to rather dense. JPL 138 crossing on the side of Jalan Yos Sudarso – Pahlawan has a higher level of traffic density.
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