A comparative analysis of black spot identification methods and road accident segmentation methods

Ghadi, Maen; Török, Árpád

doi:10.1016/j.aap.2019.03.002

Cited by 65 publications

(38 citation statements)

References 23 publications

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“…This cycle repeats itself until the circuit is closed. The same procedure applies to all the starting points, with exception of the quasistarting points, which requires suitable modification of the route [26][27].…”

Section: Order Of Customer Points On Route IImentioning

confidence: 99%

Optimisation of Distribution Routes: A Case Study

Kampf¹,

Hlatká²,

Gross³

2021

Komunikácie

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This article focuses on optimisation of the distribution routes for a company that produces carpets, floor coverings and tapestries. The optimisation process is based on operational research methods including, without limitations, Mayer’s method and the single linkage method. The obtained results are subsequently compared to the current situation and evaluated in terms of time and costs. The conclusion provides the optimised economic solution.

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Section: Order Of Customer Points On Route IImentioning

confidence: 99%

Optimisation of Distribution Routes: A Case Study

Kampf¹,

Hlatká²,

Gross³

2021

Komunikácie

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“…These models usually analyze the distributions of the already existing historical data from several aspects, and give predictions about the expected accident state. In the Empirical Bayesian method, the existing historical accident count and the expected accident count predicted by the model are added using different weights (Ghadi and Török, 2019). Because of this, this process requires an accurate accident prediction model.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #2 of "Analysis of historical road accident data supporting autonomous vehicle control strategies (v0.1)"

2021

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It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actuators. However, it is worth noting that this process flow cannot handle unexpected accident situations like a child running out in front of the vehicle or an unexpectedly slippery road surface. A comprehensive analysis of historical accident data can help to forecast these situations. For example, it is possible to localize areas of the public road network, where the number of accidents related to careless pedestrians or bad road surface conditions is significantly higher than expected. This information can help the control of the autonomous vehicle to prepare for dangerous situations long before the real-time sensors provide any related information. This manuscript presents a data-mining method working on the already existing road accident database records to find the black spots of the road network. As a next step, a further statistical approach is used to find the significant risk factors of these zones, which result can be built into the controlling strategy of self-driven cars to prepare them for these situations to decrease the probability of the potential further incidents. The evaluation part of this paper shows that the robustness of the proposed method is similar to the already existing black spot searching algorithms. However, it provides additional information about the main accident patterns.

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“…Additionally, in order to ensure higher reliability of the procedure, the Empiric Bayesian Adjustments are generally employed. Indeed, the combination of this technique with Safety Performance Functions allow considering the Regression-to-the-Mean phenomenon [6][7][8]; therefore, it provided significant benefits and improvements in the identification of blackspots [2,9].…”

Section: Assumptionsmentioning

confidence: 99%

Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms

Fiorentini

Losa

2020

Sustainability

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Screening procedures in road blackspot detection are essential tools for road authorities for quickly gathering insights on the safety level of each road site they manage. This paper suggests a road blackspot screening procedure for two-lane rural roads, relying on five different machine learning algorithms (MLAs) and real long-term traffic data. The network analyzed is the one managed by the Tuscany Region Road Administration, mainly composed of two-lane rural roads. An amount of 995 road sites, where at least one accident occurred in 2012–2016, have been labeled as “Accident Case”. Accordingly, an equal number of sites where no accident occurred in the same period, have been randomly selected and labeled as “Non-Accident Case”. Five different MLAs, namely Logistic Regression, Classification and Regression Tree, Random Forest, K-Nearest Neighbor, and Naïve Bayes, have been trained and validated. The output response of the MLAs, i.e., crash occurrence susceptibility, is a binary categorical variable. Therefore, such algorithms aim to classify a road site as likely safe (“Accident Case”) or potentially susceptible to an accident occurrence (“Non-Accident Case”) over five years. Finally, algorithms have been compared by a set of performance metrics, including precision, recall, F1-score, overall accuracy, confusion matrix, and the Area Under the Receiver Operating Characteristic. Outcomes show that the Random Forest outperforms the other MLAs with an overall accuracy of 73.53%. Furthermore, all the MLAs do not show overfitting issues. Road authorities could consider MLAs to draw up a priority list of on-site inspections and maintenance interventions.

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A comparative analysis of black spot identification methods and road accident segmentation methods

Cited by 65 publications

References 23 publications

Optimisation of Distribution Routes: A Case Study

Optimisation of Distribution Routes: A Case Study

Peer Review #2 of "Analysis of historical road accident data supporting autonomous vehicle control strategies (v0.1)"

Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms

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