Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis, Dimitrios I.; Papadimitriou, Eleonora

doi:10.1109/ojits.2023.3237177

Cited by 17 publications

(6 citation statements)

References 64 publications

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“…As the number of segments (two) is known, we group the 1454 vehicles by minimizing the squared Euclidean distances between them and their cluster centroids or means, i.e., k-means clustering [38]. According to Tselentis and Papadimitriou [39], k-means is one of the most commonly used methodologies for driver profile identification and driving pattern detection.…”

Section: Volume mentioning

confidence: 99%

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

Ling,

Thiele,

Setzer

2024

IEEE Open J. Intell. Transp. Syst.

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We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarsergrained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss. Although information loss with each step is well understood, little guidance exists on the overall information loss when conducting both steps sequentially. We use Monte Carlo simulations to regress information loss on the number of bins and PCs, given few parameters of a dataset related to its scale and correlation structure. A sensitivity study shows that information loss can be approximated well given sufficiently large datasets. Using the number of bins, PCs, and two correlation measures, we derive an empirical loss model with high accuracy. Furthermore, we demonstrate the benefits of estimating information losses and the representativeness of total loss in evaluating the accuracy of k-means clustering for a real-world customer fleet dataset. For preprocessing sensor data which are aggregated from sufficient number of samples, continuously distributed, and can be represented by Beta-distributions, we recommend not to coarsen the histogram binning before PCA.

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Section: Volume mentioning

confidence: 99%

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

Ling,

Thiele,

Setzer

2024

IEEE Open J. Intell. Transp. Syst.

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“…Moreover, it is worth noting that, since each specific driver type in Tab. 3 is characterized by a specific maximum acceleration, they will show different consumption performances [52]. Specifically, the higher is a max , the higher could be the delivered power P (t) computed as in Eq.…”

Section: A Fuel Consumption Modelmentioning

confidence: 99%

Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples

Coppola

Costanzo

Pariota

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with by leveraging traffic data such as vehicle flow, occupancy, and speed obtained from loop detectors installed along the road. To evaluate the effectiveness of the proposed Fuzzy-based VSL system and its potential benefits compared to the conventional rule-based VSL system in terms of traffic congestion and environmental impact, we conducted a simulation analysis using the microscopic traffic simulator, VISSIM. Specifically, three simulation scenarios are taken into account: i) no VSL, where the VSL system is not enabled; ii) Rule-based VSL system, where a typical a decision tree-based system is considered; iii) Fuzzy-based VSL system, where the herein proposed approach is appraised. The results demonstrate that the proposed approach enhances road efficiency by decreasing speed variation, increasing average speed and vehicle volume, and reducing fuel consumption.

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“…Road safety constitutes a critical concern for traffic authorities worldwide [1]. Despite the significant advancements achieved, there is still serious concern regarding more than 1.35 million people dying each year around the world in road accidents and over 50 million people suffering injury [2,3]. This critical situation highlights the urgent need for robust traffic safety evaluation methods, designed to deeply understand the underlying causes and recurring patterns of road accidents.…”

Section: Introductionmentioning

confidence: 99%

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

Tang,

Lu,

Liao

et al. 2024

Applied Sciences

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Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems.

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Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Cited by 17 publications

References 64 publications

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

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