On Decentralized Route Planning Using the Road Side Units as Computing Resources
On Decentralized Route Planning Using the Road Side Units as Computing Resources
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DOI：
10.1109/icfc49376.2020.00009
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
J. P. Talusan;Michael Wilbur;A. Dubey;K. Yasumoto
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文献类型：
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作者：
J. P. Talusan;Michael Wilbur;A. Dubey;K. Yasumoto
城市居民通常使用谷歌地图等第三方平台来获取路线规划服务。虽然这些先进的集中式部署能够提供近乎实时的处理，但它们仅限于数据中心的多处理环境。这引发了隐私问题，增加了关键数据的风险，并导致容易受到网络故障的影响。在本文中，我们提议使用分散式的路边单元（RSU，归城市所有）来进行路线规划。我们将城市道路网络划分为网格，每个网格分配一个RSU，交通数据在本地保存，从而提高了安全性和弹性，即使某些RSU出现故障，系统也能运行。路线生成分两步进行。首先，生成一个最优的网格序列，优先考虑最短路径计算的准确性，而非RSU负载。其次，我们按照该序列将路线规划任务分配给各个网格。考虑到RSU负载和约束条件，任务可以分配并在任何非最优网格中执行，但准确性会降低。我们使用纳什维尔大都会的道路交通数据对该系统进行了评估。我们将该区域划分为613个网格，配置负载和邻域大小以满足延迟约束，同时使模型准确性最大化。结果表明，通过简单地将搜索区域扩大到最优网格的直接邻域，处理时间减少了30%，而模型准确性从99%下降到92.3%。
Residents in cities typically use third-party platforms such as Google Maps for route planning services. While providing near real-time processing, these state of the art centralized deployments are limited to multiprocessing environments in data centers. This raises privacy concerns, increases risk for critical data and causes vulnerability to network failure. In this paper, we propose to use decentralized road side units (RSU) (owned by the city) to perform route planning. We divide the city road network into grids, each assigned an RSU where traffic data is kept locally, increasing security and resiliency such that the system can perform even if some RSUs fail. Route generation is done in two steps. First, an optimal grid sequence is generated, prioritizing shortest path calculation accuracy but not RSU load. Second, we assign route planning tasks to the grids in the sequence. Keeping in mind RSU load and constraints, tasks can be allocated and executed in any non-optimal grid but with lower accuracy. We evaluate this system using Metropolitan Nashville road traffic data. We divided the area into 613 grids, configuring load and neighborhood sizes to meet delay constraints while maximizing model accuracy. The results show that there is a 30% decrease in processing time with a decrease in model accuracy of 99% to 92.3%, by simply increasing the search area to the optimal grid’s immediate neighborhood.