Measurement of Occupancy of Heterogeneous Traffic Using Simulation Technique

Arasan, V. Thamizh; Dhivya, G.

doi:10.3182/20090902-3-us-2007.0070

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Maximum speed, v m max 11m/s [21] Maximum speed, v c max 13.8m/s [22] Maximum area occupancy, AO max m 0.85 [23] Maximum area occupancy, AO max c 0.74 [23] γ i , i = m, c 2.23, 2.12 [23] Width of the road 12m [22] Width of a car 1.6m [24] Vehicle class proportion, δ 20%, 90%…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The sought Roe numerical flux is finally derived by evaluating F (U j ), F (U j+1 ) and substituting e | Γ | e −1 for B(U j+1/2 ) into (25). Hence, the solutions at subsequent time steps n + 1, are obtained using the derived flux (25) into (24). Stability of the discretization schem is established by satisfying the Courant, Friedrich and Lewy (CFL) [19].…”

Section: Entropy Fixmentioning

confidence: 99%

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Analysis of Heterogeneous Vehicular Traffic: Using Proportional Densities

Nanyondo,

Kasumba

2023

Preprint

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Entropy Fixmentioning

confidence: 99%

Analysis of Heterogeneous Vehicular Traffic: Using Proportional Densities

Nanyondo,

Kasumba

2023

Preprint

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“…The proposed methodology was developed with the concept that heterogeneous traffic circumstances require a characteristic that can describe how diverse types of vehicles use the same volume of road space. Although direct measuring of density under various traffic situations is difficult, traffic density better reflects the level of utilization of road space [6]. As a result, in this study, area occupancy is used as an alternate quantity of density for heterogenic traffic situations.…”

Section: Methodology and Data Extractionmentioning

confidence: 99%

“…The inadequacy of lane discipline found in real mixed traffic is the subject of this analysis. Again, Arasan and Dhivya [6] have proposed "Measuring Heterogeneous Traffic Density" The validity of the region's inheritance idea of comparing the region's inheritance with the stream and speed below homogeneous traffic surroundings was established using replication. The occupancy zone was created to account for how much street space traffic takes up, and it offers accurate results regardless of whether traffic or road conditions differ during peak hours.…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of Area Occupancy of the Vehicles and its Effectiveness in Heterogeneous Traffic Conditions

Doley,

Kumar,

Ahmed

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Traffic management is becoming a major concern as the number of vehicles on the road increases. Different types of traffic studies are conducted for different traffic management, especially in developing countries like India. To address traffic heterogeneity, only the vehicular composition is not sufficient. In this study, it has tried to include other characteristics like area occupancy for the traffic analysis. The area occupancy of vehicles was obtained, and the relationship between area occupancy and vehicular composition was investigated for various types of vehicles in Silchar. Video graphic traffic data were collected for 30 minutes, and a detection zone length of 30 meters was considered. Three locations in Silchar were considered in this study. The relationship between several characteristics of area occupancy was determined using SPSS statistical software. Relation between area occupancy and vehicular composition was found for each type of vehicle considered. It was observed that faster vehicles have a weaker relationship with area occupancy than slower vehicles. Initially, it was assumed that area occupancy depends only on vehicular composition, and the R2 was found to be 0.79. Finally, it was found that area occupancy depends on the vehicular composition, space mean speed and projected area and the corresponding R2 was found to be 0.88. The values may differ for different roads in the same city.

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“…Sometimes travel time is also added to traffic state variables as an important traffic quality indicator. Also, traffic density is usually replaced by a relevant variable called occupancy because the density is valid only under highly homogeneous traffic conditions with no variations in speeds or dimensions of vehicles [1]. Also, most local detectors e.g.…”

Section: Introductionmentioning

confidence: 99%

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Zada,

Yamnenko,

Antoniou

2023

2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Traffic state prediction models are a crucial element with many applications in intelligent transportation systems. Short-term network-wide modeling of traffic states is a challenging task due to the existence of inherent characteristics such as nonlinearity, periodicity and stochasticity in the traffic state time series. This issue was responded by the evolution of advanced machine learning algorithms, e.g. deep learning. Deep neural networks can cope with high dimensionality, and also, are capable of extracting nonlinearity, comovement patterns, and spatiotemporal interdependencies between the traffic state variables from different locations. Nevertheless, they cannot completely capture the location-specific features of traffic information. Therefore, we propose the Discrete Haar Wavelet Transform (DHWT) as a preprocessing scheme prior to Multilayer Perceptron (MLP) neural networks for one-hour ahead traffic state prediction. DHWT can help MLP to simultaneously learn the network-wide comovement patterns through the trend component time series, and seize the significant characteristics of each unique detector efficiently via the noise component. The results on 20 sensors in Paris indicated that the hybrid DHWT-MLP model with a two-level down decomposition improves the Mean Squared Error (MSE) of a non-preprocessed MLP by 33.73% and 17.58 %, for the six-month and three-month data, respectively. However, the proposed model does not perform well over the one-month period compared to the MLP model.Therefore, it may be helpful to use lower wavelet decomposition levels (higher orders) when dealing with relatively small traffic datasets.

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Measurement of Occupancy of Heterogeneous Traffic Using Simulation Technique

Cited by 7 publications

References 4 publications

Analysis of Heterogeneous Vehicular Traffic: Using Proportional Densities

Analysis of Heterogeneous Vehicular Traffic: Using Proportional Densities

Determination of Area Occupancy of the Vehicles and its Effectiveness in Heterogeneous Traffic Conditions

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

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