Analysis and Prediction of Vehicles Speed in Free-Flow Traffic

Maczyński, Andrzej; Brzozowski, Krzysztof; Ryguła, Artur

doi:10.2478/ttj-2021-0020

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Compared with linear interpolation, this can better reflect the change trend of the signal. Maczy ński et al [59] developed an artificial neural network with radial neural functions driven by group parameters (i.e., average hourly traffic, the percentage of vehicles in free-flow traffic, geometric parameters of the road section (lane and hard shoulder width), and type of day and time) for predicting vehicle speeds. To reduce the errors of existing methods in short-term speed prediction and be able to predict medium-to long-term traffic speeds, Zhang et al [60] proposed a traffic speed prediction method named ASTCN that combines attention and spatio-temporal features.…”

Section: Modern Deep Learning Methodsmentioning

confidence: 99%

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

Tian

Zheng

et al. 2023

BDCC

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At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.

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Section: Modern Deep Learning Methodsmentioning

confidence: 99%

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

Tian

Zheng

et al. 2023

BDCC

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show abstract

“…In the first stage, as presented in Figure 3, SSMs should be applied. In Maczyński et al (2021) a correlation between overall traffic volumes and the percentage of vehicles exceeding the speed limit, either for light and heavy vehicles was shown. Among the 13 traffic parameters initially considered (Table 4), both hourly volumes of heavy and light vehicles, as well as hourly statistics on speeds and their distributions for both groups of vehicles were therefore included.…”

Section: Model For Identifying Road Risk Classmentioning

confidence: 97%

A Model for Identifying Road Risk Class

Ryguła

Brzozowski

Maczyński

2023

Transport and Telecommunication Journal

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In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

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“…Based on a survey conducted in Poland, it was found that for a speed limit of 90 km/h in free traffic conditions, 38% of light vehicles exceed the speed limit during the day and 42% at night. For heavy vehicles with a limit of 70 km/h, speed limit exceeding of this class of vehicles in same traffic flow conditions is 83.5% in the day and 86% at night [29]. Research focused on the credibility of the 80 km/h speed limit of two-lane rural roads, where drivers verified the proposed speed for each of 27 road situations, which shows that there were large differences in drivers' attitudes for different road and environmental characteristics related to (non) compliance with the limits in given situations [16].…”

Section: Literature Reviewmentioning

confidence: 97%

Models of Analysis of Credible Deviation from Speed Limits on Two‐Lane Roads of Bosnia and Herzegovina

et al. 2022

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Any deviation of speed in a traffic flow from a speed limit represents a potential risk of traffic accidents, so speed management appears as an imperative. However, an inadequately set speed limit often causes drivers’ noncompliance to it in the conditions of real traffic flow. By determining the value of exceeding the speed limit according to vehicle classes, it is possible to recommend a credible speeding value that can be considered credible up to a value above the speed limit. In this paper, deterministic multistep mathematical models of speed deviation from the speed limit as a function of longitudinal gradient for the proposed vehicle classes were developed. A total of 11 measuring sections with different traffic flow types were analyzed. Based on a detailed analysis of speeding, models for the deviation of the 15th, 50th, and 85th percentiles were obtained, with the aim of adjusting the credible deviation to control measures. The results obtained in this study were compared with a survey of traffic flow speeding on two-lane roads conducted in Serbia.

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Analysis and Prediction of Vehicles Speed in Free-Flow Traffic

Cited by 5 publications

References 32 publications

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data

A Model for Identifying Road Risk Class

Models of Analysis of Credible Deviation from Speed Limits on Two‐Lane Roads of Bosnia and Herzegovina

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