Optimization of Traffic Flow in a Road Network

Liu, Lixin

doi:10.1109/icicta.2008.21

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…However, the study makes no mention of traffic management explicitly or the problems with their strategy. [1] Because of this, even while the evaluated papers demonstrate how successfully machine learning and the YOLO approach may be used to recognized vehicles and control traffic, the flaws in the solutions that are now being supplied are generally not addressed. In order to improve smart traffic management that uses machine learning and the YOLO approach, future research may concentrate on these systems' drawbacks, such as their accuracy, scalability, and real-time performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Traffic System Using Machine Learning

2023

IRJMETS

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Today, everything is increasing quickly, including people and technology, and we live in a fast-paced environment. Traffic is a major problem, especially in nations with huge populations, therefore as the population grows, the government must manage and construct roadways. It becomes challenging for a single officer to control heavy traffic during holidays and other busy times. We looked at automated traffic control and techniques to make it easier for ambulances to move through heavy traffic to solve these circumstances. In this research, we suggest employing Machine Learning (ML) technology to address these issues. Python is a programming language that allows object identification, image processing, and video processing, so we made use of several techniques, datasets, and mathematical operations that could be put into practice. We created algorithms that can efficiently handle high traffic. It simultaneously checks to see if there are any traffic snarls at the intersection and modifies the timing to avoid them. The entire system operates autonomously and responds fast.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Traffic System Using Machine Learning

2023

IRJMETS

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“…charge a fee within a certain radius of the city centre) instead of dynamic, and do not follow from an optimisation procedure based on actual movement data. Approaches for optimising road networks and traffic flow from a different viewpoint, unrelated to road pricing policies, include metaheuristic optimisation of road improvements [3], development of intelligent traffic light systems [10], optimisation of road graph architectures with evolutionary algorithms [12], and prediction of optimal traffic flow through maximum-entropy methods [9]. A more exhaustive list of methods is provided by Kumar Shukla and Agrawal [8].…”

Section: Related Workmentioning

confidence: 99%

Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data

Arp

Vreumingen

Gawehns

et al. 2020

2020 21st IEEE International Conference on Mobile Data Management (MDM)

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How can urban movement data be exploited in order to improve the flow of traffic within a city? Movement data provides valuable information about routes and specific roads that people are likely to drive on. This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion. Redistributing some of the traffic to avoid unnecessary use of these roads could be a key factor in improving traffic flow.Many proposed approaches to combat congestion are either static or do not incorporate any movement data. In this work, we present a method to redistribute traffic through the introduction of externally imposed variable costs to each road segment, assuming that all drivers seek to drive the cheapest route. We use a metaheuristic optimisation approach to minimise total travel times by optimising a set of road-specific variable cost parameters, which are used as input for an objective function based on traffic flow theory. The optimisation scenario for the city centre of Tokyo considered in this paper was defined using public spatial road network data, and movement data acquired from Foursquare. Experimental results show that our proposed scenario has the potential to achieve a 62.6% improvement of total travel time in Tokyo compared to that of a currently operational road network configuration, with no imposed variable costs.

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“…This causes considerable increase in information size, which is difficult to lessen in the successive stages of the transmission process. Methods of saving information bringing its size closer to entropy are rarely used [22]. They can be found e.g.…”

Section: A Applicationmentioning

confidence: 99%

Traffic reduction in computer networks by agent technology

Bartczak

Paszczynski

Korniak

2009

2009 2nd Conference on Human System Interactions

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The rapid growth of band requirements for data transmission in computer networks is commonly known. The growth of the network infrastructure in many countries is much slower than expected. Therefore this paper presents some introductory research towards effective and inexpensive algorithmic ways for improving the efficiency of data transmission in all network layers. In this research human behavior is also taken into account.

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Optimization of Traffic Flow in a Road Network

Cited by 4 publications

References 1 publication

Smart Traffic System Using Machine Learning

Smart Traffic System Using Machine Learning

Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data

Traffic reduction in computer networks by agent technology

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