Applying Probabilistic Model to Quantify Influence of Rainy Weather on Stochastic and Dynamic Transition of Traffic Conditions

Kidando, Emmanuel; Kitali, Angela E.; Lyimo, Sia Macmillan; Sando, Thobias; Moses, Ren; Kwigizile, Valerian; Chimba, Deo

doi:10.1061/jtepbs.0000237

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In relation to transport supply, there are papers which analyse in detail the impact of weather conditions on main traffic flow parameters such as traffic volume, speed, density, and capacity [5,[15][16][17][18]. Focus of the previously mentioned papers is the operational level, at intersection or road section.…”

Section: Survey Methodologymentioning

confidence: 99%

Modelling the Impact of Rain in Traffic Assignment Procedures

Ivanović

Celar

Djoric

et al. 2022

Promet

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The efficiency of urban transportation system is un-der the influence of weather conditions. It is necessary to incorporate these impacts into transport system analysis, in order to prepare adequate mitigation measures. Trans-port models are often used in different types of transport system analysis and forecasting of its future characteris-tics. This paper focuses on implementation of the impact of rain in transport modelling, particularly into a traffic assignment process as a part of a macroscopic transport model. This aspect of modelling is important because it can indicate parts of the network where this impact leads to a high volume/capacity ratio, which is a good input for defining mitigation measures. Commonly, transport models do not consider weather impacts in its standard procedures. The paper presents a methodology for cali-brating volume-delay function in order to improve traf-fic assignment modelling in case of rain. The impact of different rain categories on capacity and free-flow speed was quantified and implemented in the volume-delay function. Special attention is given to the calibration of the part of volume-delay function for over-saturated traf-fic conditions. Calibration methodology is applicable for different types of volume-delay functions and presents a solid approach to incorporate weather conditions into common engineering practice.

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Section: Survey Methodologymentioning

confidence: 99%

Modelling the Impact of Rain in Traffic Assignment Procedures

Ivanović

Celar

Djoric

et al. 2022

Promet

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show abstract

“…The global view to the weather effect on the traffic density and speed drop was carried out at a macroscopic level. Other researchers also applied the regression analysis method [ 8 , 14 ] and other statistical methods, such as the Gaussian mixture model [ 27 ], to study the weather impact on traffic flow or speed. Besides the statistical methods, researchers also applied a variety of machine learning methods for traffic studies, such as the k -means clustering method [ 28 ], neurowavelet models [ 29 ], long short-term memory network [ 30 ], Bayesian networks model [ 31 ], deep belief networks [ 32 ], and decision tree [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Yang

Sutrisno

Chan

et al. 2021

Sensors

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Weather change such as raining is a crucial factor to cause traffic congestion, especially in metropolises with the limited sewer system infrastructures. Identifying the roads which are sensitive to weather changes, defined as weather-sensitive roads (WSR), can facilitate the infrastructure development. In the literature, little research focused on studying weather factors of developing countries that might have deficient infrastructures. In this research, to fill the gap, the real-world data associating with Jakarta, Indonesia, was studied to identify WSR based on smartphone sensor data, real-time weather information, and road characteristics datasets. A spatial-temporal congestion speed matrix (STC) was proposed to illustrate traffic speed changes over time. Under the proposed STC, a sequential clustering and classification framework was applied to identify the WSR in terms of traffic speed. In this work, the causes of WSR were evaluated based on the variables’ importance of the classification method. The experimental results show that the proposed method can cluster the roads according to the pattern changes in the traffic speed caused by weather change. Based on the results, we found that the distances to shopping malls, mosques, schools, and the roads’ altitude, length, width, and the number of lanes are highly correlated to WSR in Jakarta.

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“…However, that model does not provide a continuous relationship between two different regimes. Cluster analysis with a K-mean and Gaussian mixture algorithm has also been implemented to determine the number of regimes and their breakpoints ( 25 , 29 ). The study requires the modeler to specify the number of regimes as an input in the K-mean algorithm, which exceeds the desired mathematical simplicity.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Approach for Calibrating Freeway Highway Multi-Regimes Fundamental Diagram

Kidando

Karaer

Kutela

et al. 2020

Transportation Research Record: Journal of the Transportation R

Self Cite

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For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.

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Applying Probabilistic Model to Quantify Influence of Rainy Weather on Stochastic and Dynamic Transition of Traffic Conditions

Cited by 5 publications

References 14 publications

Modelling the Impact of Rain in Traffic Assignment Procedures

Modelling the Impact of Rain in Traffic Assignment Procedures

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Novel Approach for Calibrating Freeway Highway Multi-Regimes Fundamental Diagram

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