A Novel Approach for Operating Speed Continuous Predication Based on Alignment Space Comprehensive Index

Yan, Ying; Li, Gengping; Tang, Jinjun; Guo, Zhongyin

doi:10.1155/2017/9862949

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…This is supported by the fact that most of the research was conducted on extra-urban roads, where drivers can develop higher speeds due to less presence of intersections, vulnerable road users, geometric characteristics and layout of the traffic network as well as higher speed limits [36]. In addition to the above, the largest number of included studies were conducted on single-carriageway roads with one road lane in each direction, while a smaller number investigated the operating speed on dual-carriageway roads with two and/or more road lanes in each direction [31,[37][38][39][40][41][42][43][44][45]. In doing so, the operating speeds of passenger cars were most often examined, while a part included heavy-good vehicles (sometimes also including buses) [33,34,38,[40][41][42][43][46][47][48][49][50][51][52][53][54].…”

Section: Operating Speed Prediction Modelsmentioning

confidence: 99%

Operating Vehicles’ Speed Prediction Models

VERTLBERG,

ŠVAJDA,

JAKOVLJEVIĆ

et al. 2024

PROMTT

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Vehicle speed is one of the main factors that influence the occurrence and severity of the consequences of road traffic accidents. Operating speed can be defined, among other things, as the actual speed at which the largest number of road users drive in conditions of free traffic flow. It can be measured on existing roads, however, on newly designed roads it can only be predicted. For this reason, many researchers have examined the correlation between the elements of the road as well as its surroundings and operating speed. By determining the correlation, models for predicting operating speed were created. As part of this paper, the most significant models for predicting operating speed were analysed. Of course, the largest number of models are stochastic, but in recent years, models based on artificial intelligence, more precisely on deep learning, have also been created. Accordingly, the goal of this paper is to review the model for predicting the operating speed of vehicles while identifying opportunities for further research and improvement in this area.

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Section: Operating Speed Prediction Modelsmentioning

confidence: 99%

Operating Vehicles’ Speed Prediction Models

VERTLBERG,

ŠVAJDA,

JAKOVLJEVIĆ

et al. 2024

PROMTT

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show abstract

“…In the driving process of freight vehicles, driving speed is one of the most important indicators in safety evaluation and e ciency analysis. Currently, the speed prediction model plays an important role in obtaining the driving speed of vehicles [1]. On mountainous freeways, the poor terrain conditions allowed relatively unfavorable road geometry design, and combined vertical and horizontal alignments are not uncommon in mountainous freeways, which directly produces many accident-prone road sections [2].…”

Section: Introductionmentioning

confidence: 99%

Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods

Chen

2020

Journal of Advanced Transportation

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Driving speed is one of the most critical indicators in safety evaluation and network monitoring in freight transportation. Speed prediction model serves as the most efficient method to obtain the data of driving speed. Current speed prediction models mostly focus on operating speed, which is hard to reveal the overall condition of driving speed on the road section. Meanwhile, the models were mostly developed based on the regression method, which is inconsistent with natural driving process. Recurrent neural network (RNN) is a distinctive type of deep learning method to capture the temporary dependency in behavioral research. The aim of this paper is to apply the deep learning method to predict the general condition of driving speed in consideration of the road geometry and the temporal evolutions. 3D mobile mapping was applied to obtain road geometry information with high precision, and driving simulation experiment was then conducted with the help of the road geometry data. Driving speed was characterized by the bimodal Gauss mixture model. RNN and its variants including long short-term memory (LSTM) and RNN and gated recurrent units (GRUs) were utilized to predict speed distribution in a spatial-temporal dimension with KL divergence being the loss function. The result proved the applicability of the model in speed distribution prediction of freight vehicles, while LSTM holds the best performance with the length of input sequence being 400 m. The result can be related to the threshold of drivers’ information processing on mountainous freeway. Multiple linear regression models were constructed to be a contrast with the LSTM model, and the results showed that LSTM was superior to regression models in terms of the model accuracy and interpretability of the driving process and the formation of vehicle speed. This study may help to understand speed change behavior of freight vehicles on mountainous freeways, while providing the feasible method for safety evaluation or network efficiency analysis.

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“…Yan et al established an alignment description model based on the principle of spatial geometry, and they set up the relationship between alignment comprehensive indices and operating speed for continuous prediction. This relationship makes it possible to predict a reliable and continuous operating speed profile at each point along the alignment and to significantly improve the performance of consistency alignment design and safety evaluations [12]. Consistency evaluation method was proposed as a surrogate measure to estimate the safety of a highway segment [13].…”

Section: Introductionmentioning

confidence: 99%

An Optimization Design Method of Combination of Steep Slope and Sharp Curve Sections for Mountain Highways

Yue

Wang

2019

Mathematical Problems in Engineering

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As it was found steep slope and sharp curve sections account for 14% of the accident-prone sections according to the accidents data in Chongqing. Regular design indices with certain thresholds are found not enough for this kind of mountain highways. The goal of this paper is to find an optimization design method for combination of steep slope and sharp curve sections based on the analysis of vehicle driving stability. The overall safety model of steep slope and sharp curve combined section is established, and the relevant coordination relationship between design indices of the front and rear alignment unit is established by using the operating speed estimation model. Taking the maximum slope length corresponding to the specified design speed as the design condition of the front segment, the threshold values and variation rules of the design indices of the rear segment under different design speeds are calculated ensuring driving safety. The safety model is simulated by CarSim software, the trajectory offset and lateral acceleration are used to indirectly reflect the degree of lateral instability, and the results are compared to verify the effectiveness of the simplified safety model established in this paper.

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A Novel Approach for Operating Speed Continuous Predication Based on Alignment Space Comprehensive Index

Cited by 10 publications

References 29 publications

Operating Vehicles’ Speed Prediction Models

Operating Vehicles’ Speed Prediction Models

Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods

An Optimization Design Method of Combination of Steep Slope and Sharp Curve Sections for Mountain Highways

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