Business Process Analysis and Optimization on Road Traffic Law Enforcement of the Beijing Intelligent Traffic Management

Li, Baowen; Zou, Meng; Guo, Yufei

doi:10.1016/j.sbspro.2014.07.252

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…For example, if the analysis reveals that a certain demographic group is more likely to commit violations during certain times of the day, toll authorities can tailor education and outreach efforts to that group during those specific times. Similarly, if the analysis shows that certain traffic patterns or infrastructure configurations contribute to higher violation rates, authorities can consider making adjustments to mitigate these factors [4].…”

Section: In-depth Analysis Of Toll Violation Patterns and Causesmentioning

confidence: 99%

Data Analytics and Predictive Modeling for Toll Violation Prevention

Lembhe

2022

J Arti Inte & Cloud Comp

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This study aims to utilize data analytics and predictive modeling techniques to prevent toll violations. By analyzing various sources of data such as travel datasets, unstructured data, and financial data, the study will develop algorithms that can accurately predict toll violations and identify potential fraud patterns. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy. The study will also explore the use of machine learning algorithms to decipher the large textual data contained in transportation research papers. Additionally, the study will investigate the accuracy and performance of different predictive algorithms, such as linear regression, analysis of variance, and artificial neural networks, in predicting toll violations based on various factors such as category, purpose, total distance, and speed of travel. Furthermore, the study will examine the forensic aspects of big data in order to enhance the capabilities of forensic investigators in case of a network attack. In today’s rapidly changing world, the significance of accurate toll violation prevention using data analytics and predictive modeling techniques cannot be overstated. In today’s rapidly changing world, the significance of accurate toll violation prevention using data analytics and predictive modeling techniques cannot be overstated. By leveraging data analytics and predictive modeling, this study aims to develop an effective toll violation prevention system

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Section: In-depth Analysis Of Toll Violation Patterns and Causesmentioning

confidence: 99%

Data Analytics and Predictive Modeling for Toll Violation Prevention

Lembhe

2022

J Arti Inte & Cloud Comp

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show abstract

“…They concluded that the issuance and management of traffic are possible by implementing and utilizing the integrated traffic management systems. "Road, congestion and cordon pricing: tolling, distance pricing, or pricing based on time-of-day or congestion levels" [9], [11], [16], [18]- [25] Low/zero emission zones [26] Vehicle operating [27] Supply and pricing strategies [28] Lane managem ent [10], [14], [29] Speed enforcement devices & programs [24], [30], [31] Eco-driving, eco-routing (not requiring significant new technology) [10], [14], [29]…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Affecting Traffic Management using Two Step Cluster

Yaduvanshi,

Bansal,

Kumar

2019

IJEAT

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The concept of traffic congestion and traffic management is ambiguous in nature. The traffic and congestion is dependent on a number of factors that might impact the stretch of road or the framework of the traffic management systems. As the evolution of internet in last one decade and its reach to the very last person on this planet, this provides the basket of new opportunity of managing the traffic and its patterns on the basis of live traffic data from the onsite cameras, sensors, and the google maps traffic forecast the situation of traffic congestion would be avoided, which directly helps in reducing the load on environment and saving some valuable time of the commuters, and indirectly having large savings on the countries resources. In this research paper the authors have identified the factors affecting the management, flow, and working of traffic on the toll roads, national highways, and dedicated fright corridors in specific from the literature. The identified factors have been analyzed using quantitative statistical tools such as: relative importance index, Cronbach’s alpha, and cluster analysis to know the predictor importance. For this study a total of 192 valid responses were received using structured questionnaire survey. On the basis of data analysis the recommendation have be drawn and shared with the government authorities to be implemented on the highways to facilitate the commuters and all the other stakeholders associated with the traffic and traffic management. The findings of the relative importance index conclude that the most significant attributes of traffic management are No tolling for e-vehicles, use of information boards to avoid any traffic situations, and savings on fuels. Furthermore the findings of the cluster analysis concludes that the most important predictor is no-tolling for e-vehicles, followed by savings on fuels.

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“…For the intelligent transportation management, with wireless sensor networks technology, Chakraborty et al [ 2 ] propose an algorithm to determine the green light duration for dispatching of emergency vehicle. Li et al [ 3 ] put forward the optimization idea of Beijing road traffic law enforcement business process by using information technology, which provides reference to develop the traffic law enforcement business of the Beijing intelligent traffic management system. The operating pattern and service mode of transportation also developed with intelligent technology.…”

Section: Introductionmentioning

confidence: 99%

Vertical Suspension Optimization for a High‐Speed Train with PSO Intelligent Method

Qiu

Han

et al. 2021

Computational Intelligence and Neuroscience

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Intelligent methods and algorithms have promoted the development of the intelligent transportation system in many ways. In the rail transportation, the vertical performance of a high-speed train suspension system has a great impact on the riding comfort of the train. Based on the intelligent optimization method of particle swarm optimization (PSO) algorithm, different inerter-spring-damper (ISD) suspension layouts are proposed for better riding comfort. A 10-degree-of-freedom (10-DOF) vertical dynamic model of a high-speed train is established, and the new suspension layouts are applied to the primary and secondary suspension of the train at the same time. Optimizations are carried out for the suspension parameters of the high-speed train. Performances of different suspension layouts at different running speeds are analysed and compared. The best layout for suspension is concluded. What is more, the virtual prototype simulation and analysis of a high-speed train with consideration of nonlinear inerters are carried out. Friction of a rack-pinion inerter is simulated in the virtual prototype simulation. And the influence of nonlinearity is discussed compared with the ideal suspensions. All the results can represent a guidance for future train suspension design and help the intelligent rail transportation system to be more comfortable.

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Business Process Analysis and Optimization on Road Traffic Law Enforcement of the Beijing Intelligent Traffic Management

Cited by 7 publications

References 2 publications

Data Analytics and Predictive Modeling for Toll Violation Prevention

Data Analytics and Predictive Modeling for Toll Violation Prevention

Factors Affecting Traffic Management using Two Step Cluster

Vertical Suspension Optimization for a High‐Speed Train with PSO Intelligent Method

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