On‐line modelling and planning for urban traffic control

Pozanco, Alberto; Fernández, Susana; Borrajo, Daniel

doi:10.1111/exsy.12693

Cited by 13 publications

(8 citation statements)

References 60 publications

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“…Similarly, traffic management of a city is another big challenge for most of the big metropolitan cities. Techniques like declarative automated planning and relational learning can be used to execute, monitor, and implement traffic control systems in cities (Pozanco et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature

Muhammad Mashhood,

Hamna Salman,

Romaiza Amjad

et al. 2023

JSCIR

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Urban planning plays a crucial role in shaping communities for the future, analyzing their strengths and weaknesses, and proposing improvement solutions.This systematic literature review process used in this study ensured the inclusion of a wide range of sources, providing a comprehensive overview of the advantages of using artificial intelligence in urban planning. After a careful review of the literature, it is found that artificial intelligence is primarily used to enhance efficiency in urban planning processes, contributing to sustainability. The potential for future challenges has accelerated the adoption of artificial intelligence in urban planning by emphasizing the need for resilient, technology-driven cities. It supports more efficient urban design by automating tasks through generative design software, which subdivides plots and applies housing solutions. Artificial intelligence's role expands to multiple areas within the urban planning obligations of a city, including transportation, environment, waste management, education, healthcare, agriculture, risk management, and security. It guides urban planners by improving analytical models for understanding future physical and social community conditions. Artificial intelligence empowers planners to explore new data collection and analysis methods for predicting urban behavior, saving time and resources. It addresses climate change challenges by supporting transportation, waste management, and water use applications. Artificial intelligence technologies continue to advance, urban planning stands to benefit from increased efficiency, data-driven decision-making, and the creation of more sustainable, resilient cities. Urban planners must adapt themselves by acquiring the necessary skills to ensure successful city planning for the future. Overall, Artificial intelligence is contributing heavily to achieving sustainability by integrating past practices, trends, and new AI-based models.

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Section: Resultsmentioning

confidence: 99%

The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature

Muhammad Mashhood,

Hamna Salman,

Romaiza Amjad

et al. 2023

JSCIR

View full text Add to dashboard Cite

show abstract

“…This approach enables scalability to regions with thousands of vehicles. The work by Pozanco, Fernández, and Borrajo (2021) builds upon this approach, introducing also the ability for continuous learning and knowledge model evolution for improved network adaptation.…”

Section: Related Workmentioning

confidence: 99%

PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation

El Kouaiti,

Percassi,

Saetti

et al. 2024

ICAPS

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The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal strategies remain, as existing approaches tend not to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.

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“…In the recent few years, TLBO has indicated promising results to solve large‐scale and complex engineering problems like the fuzzy‐PID controller design problem (Sahu et al, 2015), the robotic assembly sequence planning problem (Gunji et al, 2018), the optimization problem of carbon emissions and operation time for machining systems (Lin et al, 2015), etc. The authors in Pozanco et al (2021) identified two complicated problems. Then, they presented Automated Planning for Traffic Control for urban traffic control to solve these main difficulties.…”

Section: Related Workmentioning

confidence: 99%

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Yektamoghadam,

Nikoofard,

Behzadi

et al. 2023

Expert Systems

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Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi‐criteria decision‐making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub‐optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching‐Learning‐based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real‐world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.

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On‐line modelling and planning for urban traffic control

Cited by 13 publications

References 60 publications

The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature

The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature

PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

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