Abstract
As serious traffic problems have increased throughout the world, various types of studies, especially traffic simulations, have been conducted to investigate this issue. Activity-based traffic simulation models, such as MATSim (Multi-Agent Transport Simulation), are intended to identify optimal combinations of activities in time and space. It is also necessary to examine commuting-based traffic simulations. Such simulations focus on optimizing travel times by adjusting departure times, travel modes or travel routes to present travel suggestions to the public. This paper examines the optimal departure times of metro users during rush hour using a newly developed simulation tool. A strategy for identifying relatively optimal departure times is identified. This study examines 103,637 person agents (passengers) in Shenzhen, China, and reports their average departure time, travel time and travel utility, as well as the numbers of person agents who are late and miss metro trips in every iteration. The results demonstrate that as the number of iterations increases, the average travel time of these person agents decreases by approximately 4 min. Moreover, the latest average departure time with no risk of being late when going to work is approximately 8:04, and the earliest average departure time with no risk of missing metro trips when getting off work is approximately 17:50.
Highlights
With the development of national economies and the acceleration of urbanization, traffic congestion has become a common problem around the world [1,2]
In view of the two challenges identified above, this paper aims to explore a strategy for adjusting departure times to obtain relatively optimal travel utility values, using metro travel as an example
We present the departure time adjustments that result in every iteration, as well as the number of person agents who arrive late, the occurrence of congestion at metro stops and the average travel time, travel score and departure times for going to work and getting off work for the person agents
Summary
With the development of national economies and the acceleration of urbanization, traffic congestion has become a common problem around the world [1,2]. Traffic congestion produces broad social and environmental costs, such as delays in the delivery of goods, air pollution, changes in microclimates, increases in the urban heat island effect, increases in respiratory problems and lost productivity [3]. To address this severe challenge, intelligent transportation system (ITS)-based studies have become increasingly prevalent, especially in the field of traffic simulation. Traffic simulations, which represent an important component of ITSs and differ from traditional mathematical modelling, can simulate traffic-related phenomena, including the flow of traffic and traffic accidents They can reproduce the spatial and temporal variations in traffic flow and help to analyze the characteristics of vehicles, drivers, pedestrians, roads and traffic [4,5]. Using virtual reality technology, traffic simulations can intuitively show the status of traffic in real time, including whether roads are
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