Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

Park, Dongjoo; Rilett, Laurence R.; Gajewski, Byron J.; Spiegelman, Clifford H.; Choi, Cheol Woong

doi:10.1007/s11116-008-9180-x

Cited by 19 publications

(10 citation statements)

References 23 publications

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“…Further, the form of the dependent variables in the statistical models, whether aggregated or disaggregated as well as the aggregation interval is proven to impact the study results [ 9 ]. In fact, the optimum aggregation interval for loop detector data (i.e., speed data) depends on the purpose of the application and traffic conditions [ 22 ]. Moreover, many researchers queried about the functional form to be used in modelling the impacts of road-weather conditions on traffic stream characteristics of uninterrupted flow [ 9 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions

2021

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This study proposes a methodical approach to model desired speed distributions under different road-weather and traffic conditions followed by identification of road-weather conditions with potentially higher safety risks in rural divided highways located in extremely cold regions. Desired speed distributions encompassing unique combinations of adverse road-weather and traffic conditions are modelled as normal distributions characterized by their means and standard deviations formulated based on two principal statistical theorems and techniques i.e., Central Limit Theorem and Minimum Variance Unbiased Estimation. Combination of the precipitation conditions, road surface conditions, time of the day, temperature, traffic flow and the heavy vehicle percentage at the time of travel were considered in defining the combinations of road-weather and traffic conditions. The findings reveal that simultaneous occurrence of particular precipitation and pavement conditions significantly affect the characteristics of the desired speed distribution and potentially expose drivers to elevated safety risks. Jurisdictions experiencing extreme road-weather conditions may adapt the proposed methodology to assess speed behaviour under different road-weather conditions to establishing and deploying weather-responsive traffic management strategies such as variable speed limit to regulate speeding and improve traffic safety in winter.

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Section: Introductionmentioning

confidence: 99%

Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions

2021

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“…Although studies in the existing literature predominantly use data aggregated over 5 min and 15 min intervals, some prior studies have investigated the effect of data resolution on the reliability of the predictions provided by the corresponding models; the results have, however, been inconclusive. For instance, Park et al [7] investigated the effect of aggregation on travel time prediction and considered aggregation levels from 2 min to 60 min in the context of an ARIMA model. ey concluded that higher levels of aggregation were required to forecast route travel time than when forecasting link travel times.…”

Section: Introductionmentioning

confidence: 99%

Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

Weerasekera

Sridharan

Ranjitkar

2020

Journal of Advanced Transportation

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Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.

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“…Moreover, it is necessary to consider the optimal data aggregation interval size for various lev-els (link, corridor, etc.) of estimation given the individual samples of probes (39).…”

Section: Improved Travel Timementioning

confidence: 99%

Improved Travel Time Estimation for Reliable Performance Measure Development for Closed Highways

Yang

Özbay

Xie

2015

Transportation Research Record: Journal of the Transportation R

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Accurate travel time information not only is valuable for travelers but is critical to transportation agencies for quantifying the performance of their systems. Interest has been increasing in the development of reliable approaches for estimating travel time from various sensor data. Unlike the extensively studied estimation approaches based on point sensor measurements, the use of probe data from closed highway systems has been limited. To complement current understanding, this study developed an approach that used probe data from an electronic toll collection (ETC) system on closed freeways to estimate travel time. This approach differs from studies relying on automatic vehicle identification systems deployed on main lines as well as those estimated from point detectors. The proposed approach breaks down individual journey time into section travel time and fuses the probe data from vehicles that have used the links. The results, which are based on real-world case studies, illustrate the potential of mining ETC data for travel time estimation for both incident-free and incident conditions. In addition, the estimated results capture traffic dynamics better than instantaneous travel time estimates based on point sensor data. More accurate information is thus provided for deriving reliable performance measures to depict travel time reliability.

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Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

Abstract: Travel time estimation, Travel time forecasting, Aggregation Interval, Traffic information,

Cited by 19 publications

References 23 publications

Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions

Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions

Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

Improved Travel Time Estimation for Reliable Performance Measure Development for Closed Highways

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