Modeling of incident type and incident duration using data from multiple years

Tirtha, Sudipta Dey; Yasmin, Shamsunnahar; Eluru, Naveen

doi:10.1016/j.amar.2020.100132

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…In 2020, scholars proposed a model system that recognized the distinct traffic incident duration profiles based on incident types for 326,348 incidents collected in the Grand Orlando region from 2012 to 2017. Specifically, a copula-based joint framework was estimated with a scaled multinomial logit model system for the incident type and a grouped generalized ordered logit model system for the incident duration to accommodate the impact of observed and unobserved effects on the incident type and incident duration [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Shang

Xie

2022

IJERPH

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Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables.

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Section: Introductionmentioning

confidence: 99%

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Shang

Xie

2022

IJERPH

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“…On the other hand, employing a continuous variable representation is not appropriate with rounded values. Thus, in our proposed research we employ a hybrid framework that ties the continuous delay measure to a categorical variable allowing us to estimate the model as a discrete outcome system with the inherent ability to predict as a continuous variable ( 29 – 31 ) (more details in the Econometric Methodology section).…”

Section: Contributions Of the Current Studymentioning

confidence: 99%

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

Tirtha

Bhowmik

Eluru

2022

Transportation Research Record: Journal of the Transportation R

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The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays. The proposed model is estimated using 2019 marketing carrier on-time performance data compiled by the Bureau of Transportation Statistics (BTS) for 67 airports in the continental U.S. The delay data is augmented with a comprehensive set of independent variables including traffic conditions at the origin and destination airports in the hours preceding flight departure and arrival, trip-level attributes, weather variables for the entire flight duration, and spatial and temporal factors. The model estimation results highlight that the Joe copula model with parameterization provides the best data fit. The model performance is further established to be excellent using a holdout sample. Finally, to illustrate the applicability of the model for prediction and highlight the impact of independent variables, we perform a prediction exercise under a host of hypothetical scenarios. The illustration provides a mechanism for employing the proposed model as a tool for airline-carrier-level or airport-level delay prediction analysis using weather forecasts while controlling for a host of independent variables.

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“…traffic incidents are considered the leading causes of nonrecurring congestion (Tirtha et al, 2020). It was estimated that traffic incidents contribute to around 25% of roadway traffic congestion .…”

Section: Introductionmentioning

confidence: 99%

“…Interest in traffic incidents has increased over the past two decades due to the critical safety, operational, social, and environmental impacts caused by such events. As unexpected (random) events, traffic incidents are considered the leading causes of nonrecurring congestion (Tirtha et al., 2020). It was estimated that traffic incidents contribute to around 25% of roadway traffic congestion (Li et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Factorial design–machine learning approach for predicting incident durations

Hamad

Obaid

Haridy

et al. 2022

Computer aided Civil Eng

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This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best‐performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.

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Modeling of incident type and incident duration using data from multiple years

Cited by 17 publications

References 22 publications

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

Factorial design–machine learning approach for predicting incident durations

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