Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach

Yang, Yang; Yuan, Zhenzhou; Meng, Ran

doi:10.1061/jtepbs.0000698

Cited by 48 publications

(40 citation statements)

References 15 publications

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“…Most of the association rules extracted above include driver factors, so it indicates that human is the main operator in traffic and the key factor that determines whether a crash occurs or not. Some of the above results are consistent with those of some previous studies [1,7].…”

Section: Mining Results and Analysissupporting

confidence: 93%

“…The multi-dimensional and multi-layer traffic crash coupling mechanism analysis system is the basic framework of association rule mining. Based on the analysis principle of traffic engineering, comprehensive assessment is usually carried out from the four dimensions of "human-vehicle-roadenvironment" [7]. When conducting traffic safety analysis, factors of "crash" dimension can be added.…”

Section: The Establishment Of Multi-dimensional and Multi-layer Data ...mentioning

confidence: 99%

“…Mining association rules for freeway traffic crash data can facilitate figuring out the potential association relationship among the influencing factors. Yang [7,8] proposed a multi-dimensional interaction improved Apriori association rules mining algorithm considering directional constraints and index weights: WOMID-Apriori, based on subjective and objective combined weighting model, using the the interval analytic hierarchy Process (AHP) and grey relational degree, the weight of data fields was calculated and optimized, then, the algorithm was applied to address the cause analysis of freeway crashes. Chen et al [9] applied AHP approach to the risk analysis of road engineering projects, they comprehensively measured the project risk via both a qualitative way and a quantitative way.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Yang

Tian

Wang

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently.

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Section: Mining Results and Analysissupporting

confidence: 93%

Section: The Establishment Of Multi-dimensional and Multi-layer Data ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Yang

Tian

Wang

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Visible light communication (VLC)-based intelligent transportation communication system has received extensive research owing to the possibility of integration of lighting, traffic indication, and communication [1,2]. e application scenario diagram of the VLC communication system for intelligent transportation (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

The Improved Precoding Method in the VLC-Based Intelligent Transportation System

Cheng

Zhou

et al. 2022

Journal of Advanced Transportation

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Visible light communication (VLC) transmission system, combined with lighting and communication, has a great application value in the field of intelligent transportation. To efficiently improve the performance of VLC-based intelligent transportation system, an improved precoding scheme is proposed and experimentally verified. Partial data-carrying subcarrier (PDS)-precoding scheme can balance the signal-to-noise ratio (SNR) and combat frequency selective fading (FSF) in the VLC-based intelligent transportation system. For ∼3.52 Gb/s DMT-VLC system with 1.9-m free space transmission, the OCT-based PDS-precoding signal shows almost the same BER performance as full data-carrying subcarriers precoding ones. Compared with the conventional scheme, the BER is reduced from 2e-3 to 8.14e-5 by using the proposed PDS-precoding. PDS-precoding is a nice choice to degrade the unbalanced damage for data-carrying subcarriers (SCs) in the bandwidth-limited VLC-based intelligent transportation system.

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“…Li et al [9] measured the traffic benefits attributed to carpooling by using integer program and found the total trip volumes and commuter trips in peak hours were both reduced when adopting carpooling for travelling. With the rapidly development of big data in transportation, machine learning and data mining approach were also widely used in this field [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Investigating Evaluation Indicators of Intelligent Vehicle Sharing Based on Operation Efficiency: A Case Study in Xiong’an New Area, China

Wang

Shi

et al. 2022

Journal of Advanced Transportation

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Under the background of implementing green travel in response to the national energy conservation and emission reduction policy, the concept of shared travel as a new transportation mode has been promoted. This paper aims at providing a more scientific and quantitative method to explore the shared travel traffic mode and evaluating efficiency with data mining technology. Based on the analysis of the evaluation index system of the existing intelligent shared mobility, this paper firstly points out some issues in reflecting the operation efficiency of a specific shared mobility service. In order to find out the characteristics of passenger flow and propose recommend indicators, this study presents a detail analysis using data in Xiong’an new area from September 2020 to September 2021. Then, this paper proposes a time-series algorithm to identify ridesharing behavior of demand responsive (DR) bus and to recommend indicators on operation efficiency considering capacity, turnover, and time. Moreover, the definition of indicators and calculation of case study are carried out. Results show that the utilization rate of vehicle seat for DR bus was increased by 1.6–2.5 times, and the turnover efficiency was increased by about 2 times compared to private cars and taxi. In general, this paper quantitatively describes the improvement of operation efficiency brought by bus sharing, which shows that this kind of shared mobility has the attributes of public transport in a certain sense. Also, this paper shows that the above indicators are quantifiable and comparable, which is a useful supplement to the existing evaluation index system.

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Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach

Cited by 48 publications

References 15 publications

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

The Improved Precoding Method in the VLC-Based Intelligent Transportation System

Investigating Evaluation Indicators of Intelligent Vehicle Sharing Based on Operation Efficiency: A Case Study in Xiong’an New Area, China

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