Abstract
This paper studies the important factor of sector-based time variable, which is critical to urban mobility patterns in an urban environment. In particular, this study analyzes urban traffic optimization based on modeling analysis of a simulated urban environment. In doing so, we develop and assess the urban traffic model based on three key components of agents, urban map, and mobility pattern. We develop an urban sample based on a medium-to-large city in China, which is represented by Manhattan grid pattern layout. By developing a homogeneous urban layout, we distribute the urban blocks of various sectors across the sample model. Through simulation studies, we model urban traffic based on “conventional operation hours” and “proposed operation hours” of all sectors. This urban traffic model is used to study the impact of the proposed approach on urban traffic based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC). By suggesting a new sector-based time variable, we then evaluate the urban traffic model based on multiple active agent ratio. The findings from this simulation uncover the importance of sector-based time variable in optimizing urban traffic.
Highlights
Urban traffic is recognized as one of the main challenges of cities and city environments
- This paper studies the important factor of sector-based time variable for urban traffic analysis and optimization through computational modeling and scenario analysis of multiple active agent ratio;
- This study proposes urban traffic optimization based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC);
Summary
Urban traffic is recognized as one of the main challenges of cities and city environments. We can argue urban traffic is somehow seen as a by-product of the urbanization process, often tangled with the overarching factors of urban change, land-use change, and urban redevelopment Examples of these are changes in urban layouts or densities, such as from low-rise to high-rise transformation (Cheshmehzangi, 2018), or changes in land uses through modes of urban redevelopment, infill development, etc. The nexus between such changes and growing urban traffic is seen in many contexts, which could lead to optimization of urban land use allocation from multiple perspectives, location-based information or networkedbased location sensing (Campbell et al, 2006), mobility sensing (Hemminki et al, 2013), space-time analysis (Pan and Lai, 2019), spatial simulation (Silveira and Dentinho, 2018), etc
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