结合人群源交通数据和自动交通计数器的特定环境的量延迟曲线:伦敦的案例研究

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gerard Casey, Bingyu Zhao, Krishna Kumar, K. Soga
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引用次数: 3

摘要

摘要世界各地的交通拥堵已达到长期水平。尽管交通建模中最基本、最广泛使用的函数之一是许多技术中断,但自20世纪60年代开发以来,交通量-延迟函数几乎没有变化。传统上采用宏观方法将交通量与车辆行驶时间联系起来。这些功能的一般性质使其易于使用,并具有广泛的适用性。然而,他们缺乏考虑个别道路特征(即几何形状、交通设施的存在、道路质量和周围环境)的能力。本研究调查了使用两种不同数据源重建模型的可行性,即来自谷歌地图的方向应用程序编程接口(API)的交通速度和来自自动交通计数器(ATC)的交通量数据。谷歌的交通速度数据来自道路用户的智能手机全球定位系统(GPS),能够反映道路的实时、特定环境的交通状况。另一方面,ATC能够以同样精细的时间分辨率(每小时或更短)采集车辆体积数据。通过将它们结合用于伦敦不同的道路类型,可以生成新的特定于上下文的交通量-延迟函数。该方法在选定的位置显示出了生成鲁棒函数的前景。在其他地方,它强调需要更好地了解其他影响因素,例如道路停车或天气事件的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-specific volume–delay curves by combining crowd-sourced traffic data with automated traffic counters: A case study for London
Abstract Traffic congestion across the world has reached chronic levels. Despite many technological disruptions, one of the most fundamental and widely used functions within traffic modeling, the volume–delay function has seen little in the way of change since it was developed in the 1960s. Traditionally macroscopic methods have been employed to relate traffic volume to vehicular journey time. The general nature of these functions enables their ease of use and gives widespread applicability. However, they lack the ability to consider individual road characteristics (i.e., geometry, presence of traffic furniture, road quality, and surrounding environment). This research investigates the feasibility to reconstruct the model using two different data sources, namely the traffic speed from Google Maps’ Directions Application Programming Interface (API) and traffic volume data from automated traffic counters (ATC). Google’s traffic speed data are crowd-sourced from the smartphone Global Positioning System (GPS) of road users, able to reflect real-time, context-specific traffic condition of a road. On the other hand, the ATCs enable the harvesting of the vehicle volume data over equally fine temporal resolutions (hourly or less). By combining them for different road types in London, new context-specific volume–delay functions can be generated. This method shows promise in selected locations with the generation of robust functions. In other locations, it highlights the need to better understand other influencing factors, such as the presence of on-road parking or weather events.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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