A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Ou, Jishun; Xia, Jingxin; Wang, Yuqing; Wang, Chen; Lu, Zhenbo

doi:10.1111/mice.12484

Cited by 15 publications

(4 citation statements)

References 55 publications

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“…The factors identified with the highest significance were the road alignment, upstream and downstream geometry, collision type, vehicles speed, and heavy vehicles percent. Furthermore, Ou et al (2020) presented a data-driven approach to automatically determine the spatiotemporal impacts of freeway incidents using the fuzzy approach and graph-theory clustering.…”

Section: Predictive Models Using Significant Factors Setmentioning

confidence: 99%

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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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Section: Predictive Models Using Significant Factors Setmentioning

confidence: 99%

“…Furthermore, Ou et al. (2020) presented a data‐driven approach to automatically determine the spatiotemporal impacts of freeway incidents using the fuzzy approach and graph‐theory clustering.…”

Section: Related Workmentioning

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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“…To identify the commuting vehicles, a clustering technique was utilized to analyze temporal-spatial features extracted from ALPR data. Many clustering algorithms and strategies, such as K-means [24,31], DBSCAN [32], GMM (Gaussian Mixture Model) [33], nested clustering [34], online agglomerative clustering [35], hierarchical clustering [36], and other algorithms [37,38] had been proposed in the past decades. Hierarchical clustering, as a typical unsupervised machine learning algorithm, has been applied to a wide spectrum of transportation researches.…”

Section: Commuting Vehicles Identification Using Ward's Hierarchical mentioning

confidence: 99%

Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters' route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

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“…Traffic state identification (TSI) aims to provide a qualitative and comprehensible description of the traffic conditions in a given time period on specific road segments or areas according to collected traffic flow observations. Reliable TSI is of great significance for numerous theoretical research and practical applications in the transportation field, such as traffic phenomenon understanding ( 1 – 4 ), traffic state estimation and forecasting ( 5 – 8 ), crash risk evaluation ( 9 , 10 ), and congestion evolution characterization ( 11 , 12 ) to name a few. As a result, TSI provides critical support for efficient traffic operation, management, and control, and has gained wide attention in recent decades ( 13 ).…”

mentioning

confidence: 99%

Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

Nie

Wang³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network–level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment–based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming–based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.

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A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Cited by 15 publications

References 55 publications

Factorial design–machine learning approach for predicting incident durations

Factorial design–machine learning approach for predicting incident durations

Commuting Pattern Recognition Using a Systematic Cluster Framework

Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

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