Hybrid Traffic Route Visual Recommendation Based on Multilayer Complex Networks

Tangyuan Zou, Song Wang, Hanglin Li, Yadong Wu
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引用次数: 3

Abstract

Urban traffic congestion is a major nuisance for residents' daily commute, but few studies have focused on the effective combination between urban traffic condition visualization and hybrid traffic travel route recommendation. In this paper, the visualization exploration of urban transportation patterns is realized by multilayer complex transportation networks, which are constructed by taxi transportation network, bike-sharing transportation network, and urban transportation community network. Based on multilayer complex traffic networks, a genetic algorithm modified by A * search algorithm is used to generate multi-modal travel routes. The case studies prove that this method can effectively reduce the time cost of commuting between congested areas by generating hybrid traffic routes.
基于多层复杂网络的混合交通路径视觉推荐
城市交通拥堵是困扰居民日常出行的一大问题,但将城市交通状况可视化与混合交通出行路线推荐有效结合的研究却很少。本文通过构建出租车交通网络、共享单车交通网络、城市交通社区网络等多层次复杂交通网络,实现城市交通模式的可视化探索。基于多层复杂交通网络,采用a *搜索算法改进的遗传算法生成多模式出行路径。实例研究表明,该方法通过生成混合交通路径,可以有效降低拥堵区域间通勤的时间成本。
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