• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.13-22
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• 2012

Estimating directional design hourly volume (DDHV) is an important aspect of traffic or road engineering practice. DDHV on highway without permanent traffic counters (PTCs) is usually determined by the annual average daily traffic (AADT) being multiplied by the ratio of DHV to AADT (K factor) and the directional split ratio (D factor) recommended by Korea highway capacity manual (KHCM). However, about the validity of this method has not been clearly proven. The main intent of this study is to develop more accurate and efficient DDHV estimation models for national highway in Korea. DDHV characteristics are investigated using the data from permanent traffic counters (PTCs) on national highways in Korea. A linear relationship between DDHV and AADT was identified. So DDHV estimation models using AADT were developed. The results show that the proposed models outperform the KHCM method with the mean absolute percentage errors (MAPE).

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.795-804
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• 2003

In this article, a tomography for the estimation of the origin-destination(OD) traffic intensities in dynamic routing network is considered. Vardi(1996)'s approach based on fixed route is not directly applicable to dynamic routing protocols, which arises from the fact that we cannot access the route at every observation time. While it uses link-wise traffics as the observations, the proposed method considers the triple of ingress/outgress/relayed traffics data at each node so that we can transform the problem into a routing-independent tomography. An EM algorithm for implementation and some simulated experiments are provided.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.7-26
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• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4268-4289
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• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.81-88
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• 2008

This paper presents the travel demand estimation using interval estimation methods during the trip generation stage, and then followed the other three stages of the four stage trip estimation. We have used real data of Dae-jun City. To estimate travel demand using the interval estimation method, a reliability level was set to 95% by a upper bound value, a middle value and a lower bound value. The four stage traffic demand analysis procedure was equally applied and finally interval traffic was estimated. The result showed a difference between maximum values and middle values depending on the destination during the trip generation stage. It depends on an explanation ability of regression analysis. Most of interval estimation ratio resulted in the traffic assignment stage showed ${\pm}5{\sim}18%$ difference on the average and ${\pm}30{\sim}50%$ at the most.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2002.11a
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• pp.85-90
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• 2002

Traffic specification plays a crucial role in the resource reservation for video services over the packet-switching networks. The current development of QoS-guaranteed service still leaves a wide space for the selection of traffic specification. We propose a new method to estimate the traffic specification of variable-bit-rate (VBR) video for deterministic service. The method is based on the concept of empirical envelope and the delay bound. The solution shows to be simple yet it provides excellent network utilization.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.45-53
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• 2003

Traffic specification plays a crucial role in the resource reservation for video services over the packet-switching networks. The current development of QoS-guaranteed service still leaves a wide space for the selection of traffic specification. We propose a new method to estimate the traffic specification of variable-bit-rate (VBR) video for deterministic service. The method is based on the concept of empirical envelope and the delay bound. The solution shows to be simple yet it provides excellent network utilization.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.167-183
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• 1995

The objective of study is to evaluate highway capaicty estimation alternative and to develop capacity from statistical distribution of observed traffic flow. Speed-Volume relation is analyzed from vehicle's headway distribution eliminating the long headway by confidence intervals 99%, 95%, 90%. Capacity estimate alternatives were evaluated from 95% , 90%, 85% level of cummulative distribution of observed hourly traffic flow adjusted to confidence intervals. The result of investigation revealed that maximum hourly rate of flow is 2, 130pcu at confidence interval of 995, 2, 233pcu at 95%, 2, 315pcu at 90% respectively. Compared to the capacity of 2, 200pcu per hour per lane used in HCM and KHCM(Korea Highway Capacity Manual), capa챠y appears to correspond to confidence interval of 95%. Using the traffic flow rate at confidence interval of 95% the maximum hourly flow rate is 2, 187pcu at 95% of cummulative volume distribution, 2, 153pcu at 90%, 2, 215pcu at 85%. The study suggests that raional capacity esimation alternative is to take the 95% of cummulative distribution of observed hourly traffic flow at 95% confidence headway interval eliminating 5% long headway.(i.e. 95-95 rule)