Traffic volume prediction on low-volume roadways: a Cubist approach

Das, Subasish

doi:10.1080/03081060.2020.1851452

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…For example, Gu et al used the Bayesian combination model [30], the neural network model [31], and other algorithms to test the possible effect of factors such as economic development on road traffic volume. In general, areas with higher income and economic development have higher road traffic volume, a phenomenon supported by studies from the United States [32] and Greece [33].…”

Section: Income and Economic Developmentmentioning

confidence: 94%

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

et al. 2022

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The impact of COVID-19 on traffic volume makes it essential to study the spatial heterogeneity and impact mechanisms of the recovery of road traffic volume to promote the sustainability of related industries. As the research method, this study used a principal component analysis to evaluate the recovery of road traffic volume in China quantitatively, and further conducted an empirical study using a spatial autocorrelation index and a dynamic spatial panel model. The results show that income has a negative impact on the recovery of road traffic volume, while climate suitability has a positive impact. Economic development and COVID-19 can play moderating and mediating effects, respectively. From the aspect of spatial heterogeneity, the recovery of road traffic volume has a positive spatial spillover effect on the surrounding provinces, while the spread of COVID-19 has a negative short-term indirect spatial spillover effect. Corresponding practical insights are provided for the stakeholders based on the above findings. The results of this study will contribute to the development of effective policies to facilitate the recovery of road traffic volume from the impact of COVID-19 and the revitalization of the transportation industry.

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Section: Income and Economic Developmentmentioning

confidence: 94%

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

et al. 2022

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“…Approaches focusing on defining boundaries between traffic volume categories using random forest or SVM [13] aimed to estimate traffic volumes on low-traffic volume routes in the US. These studies constructed random forest models using variables such as time, date, weather conditions, and traffic volume from the previous day.…”

Section: ) Models For Temporal Similarities In Traffic Fluctuationsmentioning

confidence: 99%

“…These studies constructed random forest models using variables such as time, date, weather conditions, and traffic volume from the previous day. Das [13] compared the estimation of boundaries using random forests, support vectors, and model trees, identifying model trees as the optimal choice among the three. These studies highlight the importance of incorporating route categorization into traffic volume estimation models, as traffic volumes significantly vary across different route categories.…”

Section: ) Models For Temporal Similarities In Traffic Fluctuationsmentioning

confidence: 99%

Development of a Method for Estimating Traffic Volume Fluctuations That Considers Calendar Information and Road Network Geometry

Kobayashi,

Tanishita

2024

IEEE Access

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This study aims to develop a method for estimating the traffic volume at locations and time periods where past data are unavailable. To achieve this, we constructed a model based on seven years of traffic volume observations on 22 main roads in Tokyo. The estimation method comprises two components: a traffic volume fluctuation pattern model that explains pre-clustered patterns and an annual average daily traffic model. Through our analysis, we made three key observations. First, the distance to the city center is a significant explanatory factor for monthly fluctuations, the month number for weekly fluctuations, the dayto-night population ratio for daily fluctuations, and the city center direction angle for hourly fluctuations. These calendar and road geometry variables account for 70-90% of the traffic volume fluctuation. Second, when estimating traffic volume for specific time periods using the traffic volume fluctuation pattern model, we explained approximately 60% of routes where no traffic volume observations were conducted and approximately 80% of routes where observations were made infrequently. Finally, the optimal number of clusters for the traffic volume fluctuation pattern, which maximized the coefficient of determination of the traffic volume model, was 8 for monthly fluctuations, 4 for weekly fluctuations, 6 for daily fluctuations, and 11 for hourly fluctuations. These findings made it possible to estimate traffic volumes on routes where traffic volume observations are not conducted and to interpolate traffic volumes for any given month or date using the results of periodic surveys conducted every few years.

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“…In the past few years, different nonlinear methods such as machine learning algorithms have been used to address problems in which linear methods have not provided the expected results [4,[30][31][32][33]. The Cubist algorithm demonstrates a better performance than other machine learning algorithms like XGboost or Random Forest to solve problems in diverse areas [34,35]. Besides, Cubist does not act as a "black box" like other algorithms do and it is highly interpretable.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach Investigating Consumers’ Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale

Ripoll,

Panea,

Latorre

2023

Foods

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The aim of this study was to elucidate the relations between the visual color perception and the instrumental color of dry-cured ham, with a specific focus on determining the Just Noticeable Color Difference (JNCD). Additionally, we studied the influence of consumer involvement and familiarity on color-related associations and JNCD. Slices of ham were examined to determine their instrumental color and photos were taken. Consumers were surveyed about color scoring and matching of the pictures; they were also asked about their involvement in food, familiarity with cured ham, and sociodemographic characteristics. Consumers were clustered according to their level of involvement and the JNCD was calculated for the clusters. An interpretable machine learning algorithm was used to relate the visual appraisal to the instrumental color. A JNCD of ΔEab* = 6.2 was established, although it was lower for younger people. ΔEab* was also influenced by the involvement of consumers. The machine-learning algorithm results were better than those obtained via multiple linear regressions when consumers’ psychographic characteristics were included. The most important color variables of the algorithm were L* and hab. The findings of this research underscore the impact of consumers’ involvement and familiarity with dry-cured ham on their perception of color.

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Traffic volume prediction on low-volume roadways: a Cubist approach

Cited by 5 publications

References 19 publications

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

Development of a Method for Estimating Traffic Volume Fluctuations That Considers Calendar Information and Road Network Geometry

A Machine Learning Approach Investigating Consumers’ Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale

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