Development of degree of saturation estimation models for adaptive signal systems

Lee, Sang Soo; Lee, Seung Hwan; Oh, Young Tae; Choi, Kee Choo

doi:10.1007/bf02829156

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…In SCATS, Degree of Saturation (DS), which refers to the ratio of effectively used green time to the total available green time, is utilized to evaluate the saturated state of the traffic control system [50]. Similar to the previous study, we acquired the DS data of the intersections with ξ j ≥ 80% and removed outliers whose phase number is significantly less than the illustrated number in the system.…”

Section: Spatial Distribution Characteristics Analysismentioning

confidence: 99%

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Wang

Ma³

et al. 2018

Eur. Transp. Res. Rev.

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Purpose: This paper aims to determine the urban traffic flow spatiotemporal characteristics and correlation with the built environment using SCATS (Sydney Coordinated Adaptive Traffic System) and POIs (Point of Interests) data of Shenyang, China. Methods: A standard analysis framework based on these data is proposed in the paper. The study analyzes the traffic volume spatiotemporal distributions and built environment influence factors determined by the geographical detector. An improved gravity model using simple structural parameters (lanes number and road length) is proposed to estimate the traffic flows of day and peak hour scales for specific flow ranges. Results: The results show that the peak hours of different intersections and roads are heterogeneous and reveal trip time flexibility. The correlation between peak hour flows and day flows is significant in the multidimensional analysis. Based on the investigation of lanes, more interesting conclusions are found. In this case, when the numbers of lanes of intersections and roads are more than 14 and 4 respectively, the lane resources are wasted to a great extent. There is also a certain correlation between these factors. Proposed gravity model establishes the connection between structure and function of urban roads. Conclusions: Flexible work time and places will be effective methods to reduce traffic congestion. The day flows could be estimated via a traffic survey on peak hour flows, especially in developing cities. The traffic flow mainly concentrates in a relatively small part of city roads. The maximum service traffic volumes exhibit segmentation, we should reconsider the maximum optimal lanes number of intersections and roads under better performance and utilization rate of the network. The effect of lanes number on the service traffic volumes is found to be more significant compared with the other factors. Our conclusions will be helpful for policy-makers and sustainable urban planning.

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Section: Spatial Distribution Characteristics Analysismentioning

confidence: 99%

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Wang

Ma³

et al. 2018

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

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“…Four different aggregation period lengths are considered: 2, 15, 30 and 60 min. It is worth noting that while traffic counts reflect the fluctuations in demand, degree of saturation values, defined as the ratio of the effectively used green time to the total available green time for each movement (Lee et al, 2002), capture the changes in both demand and capacity (signal cycle and green time lengths).…”

Section: Datamentioning

confidence: 99%

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Mazloumi

Rose

Currie

et al. 2011

Engineering Applications of Artificial Intelligence

121

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“…These results show a similar trend, although there are differences in values compared to overseas studies. Lee et al (2011) built a bike demand estimation model with features composed of the number of students, excluding elementary school students, and the number of passenger cars. Vogel et al (2014) conducted a case study on strategy establishment of bike sharing systems using data mining and explored activity patterns.…”

Section: Bike Share Demand Predictionmentioning

confidence: 99%

Prediction of Bike Share Demand by Machine Learning

Kim

Park

Shin³

et al. 2021

International Journal of Business Analytics

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In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.

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Development of degree of saturation estimation models for adaptive signal systems

Cited by 7 publications

References 1 publication

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Prediction of Bike Share Demand by Machine Learning

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