A multi-class urban traffic model considering heterogeneous vehicle composition: An extension of the S model

Portilla, Christian; Espinosa, Jairo; Schutter, Bart De

doi:10.1016/j.trc.2020.102613

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…However, it is crucial to note that these factors have mainly been applied to homogeneous traffic, specifically single vehicle types such as cars (Bhaskar et al, 2014;Zheng and Su, 2016). In reality, highway traffic is heterogeneous and consists of various vehicle types, including trucks (Portilla et al, 2020). Trucks, in particular, have lower handling stability than passenger cars, resulting in higher casualty rates in traffic accidents (Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Wang,

Dong,

Shao

et al. 2024

Front. Eng. Manag.

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Accurate traffic state estimations (TSEs) within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies. Traditional TSE methods often assume homogeneous traffic, where all vehicles are considered identical, which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics. In this study, we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows. We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements: basic traffic parameters, heterogeneous vehicle speeds, heterogeneous vehicle flows, and mixed flow rates. This system aids in capturing the unique traffic characteristics of different vehicle types. Our proposed high-dimensional fuzzy TSE model, termed HiF-TSE, integrates three main processes: feature selection, which eliminates redundant traffic features using Spearman correlation coefficients; dimension reduction, which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data; and FCM clustering, which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters. The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing. We validate our model through a real-world case study, demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.

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Section: Introductionmentioning

confidence: 99%

Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Wang,

Dong,

Shao

et al. 2024

Front. Eng. Manag.

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show abstract

“…Road users are made up of many modes of transportation rather than just one. As a result, the vehicle flow in urban traffic cannot be regarded homogeneous since there are many different types of vehicles, such as cars, vans, buses, lorries, and motorcycles that has its own set of characteristics [1]. The interactions between these vehicles within their environments, for instance the road network and traffic rules, may cause traffic congestion to happen.…”

Section: Introductionmentioning

confidence: 99%

“…The vehicle flow is a vehicle composition interacting with one another, and their operational attribute can be described mathematically [1]. Thus, analytical approaches are challenging to be used in simulating traffic flow due to the complexity of driver behaviour, vehicular interactions and movements.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, simulation techniques are often used to mimic such mixed traffic patterns [7]. Kanagaraj et al [7], Arkatkar et al [8], Dang et al [9] and Portilla et al [1] have used simulation method to simulate the mixed traffic condition prior to the vehicle composition. The characteristics of traffic on highways evolve throughout time, with a significant level of unpredictability and simultaneous interactions.…”

Section: Introductionmentioning

confidence: 99%

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Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach

Shaharuddin

Misro

2023

IJGI

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Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver’s behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users.

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A combined probabilistic-fuzzy approach for dynamic modeling of traffic in smart cities: Handling imprecise and uncertain traffic data

Jamshidnejad,

De Schutter

2024

Computers and Electrical Engineering

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A multi-class urban traffic model considering heterogeneous vehicle composition: An extension of the S model

Cited by 6 publications

References 28 publications

Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach

A combined probabilistic-fuzzy approach for dynamic modeling of traffic in smart cities: Handling imprecise and uncertain traffic data

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