A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

Jain, Amita; Yadav, Sudesh; Vij, Sonakshi; Kumar, Yogesh; Tayal, Devendra K.

doi:10.1007/s11277-019-06787-z

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…We evaluated the inhalation intake of PM 2.5 in the carriage for a further understanding of the impact of in-carriage PM 2.5 on human health, which can be calculated using Equation (2).…”

Section: Inhalation Intake Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Rapid urban population growth brings with it severe ground traffic congestion, which increases citizens' commuting time and causes environmental pollution [1][2][3]. The development of the metro system can effectively alleviate this issue in many countries, especially in China [2,[4][5][6]. By the end of 2020, the metro system existed in 45 cities in China, resulting in a total operating mileage of 6280.8 km and an annual passenger number of 17.6 billion [7].…”

Section: Introductionmentioning

confidence: 99%

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Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu

et al. 2022

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Urbanization promotes the development of the subway system, and the particulate matter (PM) concentrations inside have received increasing attention. This study first measured the dynamic PM2.5 and PM10 concentrations in a metro carriage in Chengdu and explored the dominant influencing factors. The personal inhalation intakes of different routes were evaluated. The results showed that the in-carriage PM2.5 and PM10 concentrations ranged from 11 to 74 μg/m3 (mean: 36.7 μg/m3) and 13 to 89 μg/m3 (mean: 40.1 μg/m3), respectively. When the train passed from the overground to underground, the in-carriage PM2.5 and PM10 concentrations increased by 30.4% and 32.9%, respectively. No specific linear relationship between passenger number and in-carriage PM concentrations was found. In-carriage PM concentrations decreased after the carriage doors were opened on the platforms. PM2.5 inhalation intakes ranged from 1.08 to 9.52, with a mean of 4.24 µg. For the passengers with the same age and sex, the average inhaled PM2.5 intake in the metro carriage on the route with more underground platforms was higher. This study not only revealed the PM characteristics in the Chengdu metro system for the first time, but also provided guidelines for reducing the in-carriage PM concentrations to build a healthier travel environment.

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“…We evaluated the inhalation intake of PM 2.5 in the carriage for a further understanding of the impact of in-carriage PM 2.5 on human health, which can be calculated using Equation (2).…”

Section: Inhalation Intake Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu

et al. 2022

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“…After determining the votes, the weight for each traffic input should be determined by studying the dissimilarity (D) between different outputs using the mean absolute deviation (1-MAD). Equation (21) shows the matrix D with size of I X I (number of inputs X number of inputs).…”

Section: Weighting (W)mentioning

confidence: 99%

“…18 Consequently, smart city applications can use this knowledge to assist urban planners in responding to future traffic conditions (TCs) by leveraging this knowledge to detect and/or predict abnormal behavior through the use of congestion detection and prediction algorithms. 19,20 Based on these algorithms' output, traffic lights can be adjusted to resolve present congestions or avoid future ones, 21 and notifications can be sent out to drivers with suggested alternative routes (through local apps or V2X communication). 22 Furthermore, by detecting the congestion on time, the side effects of this congestion could be estimated early for the neighboring road sections; accordingly, the unwanted effects could be mitigated on time.…”

Section: Introductionmentioning

confidence: 99%

Novel traffic congestion detection algorithms for smart city applications

Bawaneh

Simon

2022

Concurrency and Computation

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Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered a pre-step to address traffic jam problems, providing useful input for traffic management systems to predict and avoid traffic congestions' unwanted effects. In this research article, two novel real-time congestion detection algorithms are proposed, the TCD algorithm and the ensemble based traffic congestion detection (EB-TCD) algorithm. TCD can detect the congestion based on one traffic feature (i.e., speed, occupancy, or flow). TCD first cleans and preprocesses the traffic data. After, it computes the absolute of the first derivative of each sample in the traffic feature to determine its anomaly score. Then, the anomaly likelihood of each sample is computed to classify it as an anomaly or normal sample. On the other hand, EB-TCD utilizes the information contained in multiple traffic features in parallel by proposing an ensemble technique to combine the anomaly scores coming out from the different traffic features in one unified score. EB-TCD computes an anomaly score based on each traffic feature separately. Then, it votes for each sample in each traffic feature as an anomaly or not (from the corresponding traffic feature's point of view). After, the weight for each traffic feature is determined by studying the dissimilarity between their anomaly scores. These weights show how much each traffic feature explains the traffic behavior. Finally, the results are combined to form a unified anomaly score for each sample. The proposed statistical-based techniques are able to detect changes in traffic patterns while mitigating the effect of noise in traffic data. Moreover, the proposed algorithms' parameters were tuned and tailored for fast detection, which is a crucial feature for traffic management systems since fast detection accelerates decision-making. The computational complexity analysis has shown the simplicity of the proposed algorithms. Furthermore, the evaluation results have shown that the two algorithms outperform other widespread methods from the literature in terms of false alarm rate and detection time while keeping the detection rate at the same high level.

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AI and IoT Based Innovative Solutions for Traffic Management System in Smart Cities

Sathuluri,

Sakthivel,

Kumar

et al. 2024

Information Systems Engineering and Management

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A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

Cited by 10 publications

References 23 publications

Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu

Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu

Novel traffic congestion detection algorithms for smart city applications

AI and IoT Based Innovative Solutions for Traffic Management System in Smart Cities

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