Developing a Strategy for Imputing Missing Traffic Volume Data

Chen, Mei; Xia, Jingxin; Liu, Ronghui

doi:10.5399/osu/jtrf.45.3.616

Cited by 6 publications

(9 citation statements)

References 8 publications

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“…ANN approaches have been widely used in traffic modeling. An ANN is a modeling technique inspired by the human nervous system (Chen, Xia, & Liu, 2010). A typical ANN model has three layers, including input, hidden, and output layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

Ganji

Shekarrizfard

Harpalani³

et al. 2019

Computer aided Civil Eng

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On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

Ganji

Shekarrizfard

Harpalani³

et al. 2019

Computer aided Civil Eng

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“…To prove the effect of missing data imputation when applied to environmental sensor data, the evaluation method was measured with the mean absolute error (MAE) and the root mean square error (RMSE). MAE and RMSE are the most widely used evaluation methods for the imputation of missing values [49][50][51][52]. The formulas for these methods are shown in Table 3.…”

Section: Evaluation Methodsmentioning

confidence: 99%

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

Choi

Jung

Cho

2021

Sensors

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With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.

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“…First, historical imputation methods [5,14,15] replace missing data points with the mean value of previous values collected at the same position at the same time of day and day of the week. Second, temporal interpolation imputes a missing value by replacing it with the average of its previous and following values at the same site.…”

Section: Related Workmentioning

confidence: 99%

“…In the former approach, travel time can be directly determined from the difference in the timestamps at the beginning and ending points on the selected corridor. Any of several systems can be used to match instances of an individual vehicle passing two points of interest: in-vehicle global positioning systems [1,2], automatic license plate recognition, automatic vehicle identification systems [3], and Bluetooth scanners [4][5][6]. The accuracy of this approach is affected by the penetration rate [7], defined as the ratio between the number of observed vehicles and the total number of vehicles passing the two points of interest.…”

Section: Introductionmentioning

confidence: 99%

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

Jedwanna,

Athan,

Boonsiripant

2023

Sustainability

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Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.

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Developing a Strategy for Imputing Missing Traffic Volume Data

Cited by 6 publications

References 8 publications

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

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