Optimum Design of Urban Road Intersection Signal Timing Based on VISSIM Simulation

Li, Zhe; Cao, Congyong; Kong, Chaoqun

doi:10.1007/978-981-19-5615-7_40

“…Today, rapid urban development has exerted immense pressure on transportation systems, which led to a series of challenges, such as traffic congestion and environmental pollution 1,2 . The efficacy of urban intersection traffic signal control systems in mitigating escalating traffic issues has gained widespread recognition.…”

Section: Introductionmentioning

confidence: 99%

Traffic Signal Control Using a Novel Multiagent Reinforcement Learning Considering Pedestrian Behavior

rong,

meng,

guo

et al. 2024

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Finding the optimal signal timing strategy poses a formidable challenge in traffic signal control problems. While multi-agent reinforcement learning holds promise for addressing this issue, the majority of studies prioritize vehicle-centric approaches, neglecting crucial pedestrian factors, particularly the unpredictable behavior of pedestrians during street crossings. This research aims to bridge this gap by investigating the effectiveness of a novel Double Q-learning method based on multi-agent collaboration in optimizing the traffic balance between pedestrians and vehicles at multiple intersections. In the context of urban transportation systems, where intersections act as pivotal nodes, this study adopts a macro-level perspective, treating each intersection as an agent capable of collaborative decision-making. The micro-level analysis incorporates a state representation incorporating pedestrian behavior sensitivity and a flexible action space, with the objective of optimizing traffic conditions. Results demonstrate that, under non-ideal conditions, our algorithm outperforms traditional Sarsa and Q-Learning algorithms, reducing vehicle waiting time by 32.8% and pedestrian waiting time by 70.85%. These outcomes underscore the algorithm’s effectiveness in balancing traffic efficiency for both vehicles and pedestrians.

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“…Due to the current myriad of queue length data, an analysis tool is needed to help decision making in traffic light management. However, current analysis tools on a network wide are limited to simulation system such as VISSIM [6], which display traffic queue length in a real time fashion. ISSN: 1686-6576 (Printed) | ISSN 2673-0014 (Online) | © Geoinformatics International Such system, which focuses only on representing the current situation of the queue length, neglected information about the change of queueits uncertainty information.…”

Section: Introductionmentioning

confidence: 99%

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

2023

IJG

0

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Traffic queue length at intersection is one of the most important measures of traffic signal performance and the traffic signal optimization. The spread of the ride-hailing services makes GPS data widely available with large area coverage, allowing us to quantify the queue length in a wide area without the need for additional installation cost like conventional sensors. Hence, queue length can be observed simultaneously and the relationship between intersections can be captured. Due to the current myriad of available queue length data, an analysis tool is needed to turn the data into useful knowledge to support traffic operations and management. However, the current queue length visualization only focuses on a real-time fashion, which cannot provide information about the change of the queue or its uncertainty. Such information is crucial for understanding the probability of the queue to be spillover, as well as determining the lengths of turning lanes to minimize blockage. In this research, we developed a spatial uncertainty-aware visualization by applying a conventional technique called box plot to visualize queue lengths on a map derived from taxi GPS data. A prototype of a web mapping application has been implemented and tested with users. The results indicated that participants with a statistics background have an advantage over those without a statistical background to understand the box plot. However, all of them were able to identify the uncertainty of the queue lengths as well as to compare the uncertainty between intersections and times.

show abstract

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

2023

IJG

0

View full text Add to dashboard Cite

Traffic queue length at intersection is one of the most important measures of traffic signal performance and the traffic signal optimization. The spread of the ride-hailing services makes GPS data widely available with large area coverage, allowing us to quantify the queue length in a wide area without the need for additional installation cost like conventional sensors. Hence, queue length can be observed simultaneously and the relationship between intersections can be captured. Due to the current myriad of available queue length data, an analysis tool is needed to turn the data into useful knowledge to support traffic operations and management. However, the current queue length visualization only focuses on a real-time fashion, which cannot provide information about the change of the queue or its uncertainty. Such information is crucial for understanding the probability of the queue to be spillover, as well as determining the lengths of turning lanes to minimize blockage. In this research, we developed a spatial uncertainty-aware visualization by applying a conventional technique called box plot to visualize queue lengths on a map derived from taxi GPS data. A prototype of a web mapping application has been implemented and tested with users. The results indicated that participants with a statistics background have an advantage over those without a statistical background to understand the box plot. However, all of them were able to identify the uncertainty of the queue lengths as well as to compare the uncertainty between intersections and times.

show abstract

Optimum Design of Urban Road Intersection Signal Timing Based on VISSIM Simulation

Cited by 3 publications

References 0 publications

Traffic Signal Control Using a Novel Multiagent Reinforcement Learning Considering Pedestrian Behavior

Traffic Signal Control Using a Novel Multiagent Reinforcement Learning Considering Pedestrian Behavior

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

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