Lane-Level Regional Risk Prediction of Mainline at Freeway Diverge Area

Lyu, Nengchao; Wen, Jiaqiang; Hao, Wei

doi:10.3390/ijerph19105867

Cited by 8 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improved algorithm, the least-square support vector machine (LSSVM), can fit the data in segments, which allows to reduce the calculation dimensions for data fitting, and save the calculation time and increase the fitting accuracy simultaneously as a result [ 39 , 40 ]. The LSSVM and its improved models [ 41 , 42 ] have been used for traffic flow prediction [ 43 , 44 ], real-time traffic information extraction [ 45 ], importance evaluation of nodes in complex networks [ 46 ], regional risk prediction [ 47 ], and so on. Specially, in the field of metro system, the LSSVM has been applied to predict the time change law of passenger flows [ 32 , 36 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

View full text Add to dashboard Cite

The station-level ridership during the peak hour is one of the key indicators for the design of station size and relevant facilities. However, with the operation of metro system, it cannot be ignored that, in many cities, the station peak and the city peak may not be simultaneously occurred. As the current ridership forecasting methods use the city peak as the point of reference, stations with wide differences of ridership in between would experience disorders due to serious underestimates of passenger demand during the actual peak. Accordingly, this study fully considers the phenomenon that the metro station peak is not identical to the city peak and focuses on the concept of the peak deviation coefficient (PDC), the ratio of the station peak ridership to the city peak ridership. It investigates how metro ridership determinants affects the PDC using the least square support vector machine (LSSVM) model. A land-use function complementarity index is employed as one of the independent variables, which is newly proposed in this study that describes the relationship of the commute land use around an individual station with that along the whole network. This method can help to resolve the ridership amplification indicator for a fine-grained station-level forecasting. The results for Xi’an metro indicate that the LSSVM is an effective method to scrutinize the nonlinear effects of e.g., land use attributes, on the temporal distribution features of the metro ridership. Compared to the ratio of commute land use measured for individual stations, the land-use function complementarity index can better explain and predict the severity of peak deviation phenomenon, controlling other independent variables in the model.

show abstract

Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, for an accident vehicle whose main objective is to stop near the curb, turning left towards the center of the road would be detrimental because it would take a longer time to reach the emergency lane. In an emergency hedge event, the surrounding risk is anisotropic [35]. The side closer to the curb should have relatively less risk.…”

Section: Field Superpositionmentioning

confidence: 99%

An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field

Wang

Yang

et al. 2023

IJERPH

View full text Add to dashboard Cite

Driver disability has become an increasing factor leading to traffic accidents, especially for commercial vehicle drivers who endure high mental and physical pressure because of long periods of work. Once driver disability occurs, e.g., heart disease or heat stroke, the loss of driving control may lead to serious traffic incidents and public damage. This paper proposes a novel driving intervention system for autonomous danger avoidance under driver disability conditions, including a quantitative risk assessment module named the Emergency Safety Field (ESF) and a motion-planning module. The ESF considers three factors affecting hedging behavior: road boundaries, obstacles, and target position. In the field-based framework, each factor is modeled as an individual risk source generating repulsive or attractive force fields. Individual risk distributions are regionally weighted and merged into one unified emergency safety field denoting the level of danger to the ego vehicle. With risk evaluation, a path–velocity-coupled motion planning module was designed to generate a safe and smooth trajectory to pull the vehicle over. The results of our experiments show that the proposed algorithms have obvious advantages in success rate, efficiency, stability, and safety compared with the traditional method. Validation on multiple simulation and real-world platforms proves the feasibility and adaptivity of the module in traffic scenarios.

show abstract

“…If the correct number of inputs and outputs is used, then the indicators are able to reveal the positive/negative development of the system as well as major events within the system, including periods of system development to be investigated by the theory of catastrophes (see, [26][27][28] for details). Long periods of time in which the dynamic intensity indicator has a value of zero or a negative value indicates that the system was hit or has experienced problems, including serious catastrophes such as sudden sharp changes in the price of the inputs or outputs, [29], traffic accidents [30], etc.…”

Section: Derivation Of Dynamic Indicators Of Extensity and Intensity ...mentioning

confidence: 99%

Mathematical Modelling of Qualitative System Development

2022

View full text Add to dashboard Cite

Many scientific fields need to know how human systems develop. From an economic point of view, the main factors of system output change are changes in the quantity of inputs (extensive factors) and changes in efficiency (input quality and productivity, intensive factors). The growth accounting (GA) method is used for the calculation of the impact of both factors on GDP change. However, its interpretation is sometimes difficult, and GA does not cover all of the possible situations of system (country economy) development. This article uses mathematical tools to derive new indicators (dynamic intensity indicator and dynamic extensity indicator) that clearly count and express how the changes in intensive or extensive factors contribute to the output change in any system. The indicators come from the complex system development typology analyzed in the text. The typology covers all of the relationships among the inputs, their efficiency, and their output. The article shows the use of these indicators in macroeconomics when examining the intensity of GDP development in the World’s major economies during the period of 1961–2021 and in microeconomics while investigating the intensity of the development Apple in the period of 1999–2021. We further discuss how indicators reduce managerial risk and uncertainty and their pros and cons.

show abstract

Lane-Level Regional Risk Prediction of Mainline at Freeway Diverge Area

Cited by 8 publications

References 30 publications

Deviation of peak hours for metro stations based on least square support vector machine

Deviation of peak hours for metro stations based on least square support vector machine

An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field

Mathematical Modelling of Qualitative System Development

Contact Info

Product

Resources

About