A New Travel Time Prediction Method for Intelligent Transportation Systems

Lee, Hyunjo; Chowdhury, Nihad Karim; Chang, Jae-Woo

doi:10.1007/978-3-540-85563-7_61

Cited by 14 publications

(19 citation statements)

References 9 publications

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“…A real data set that used was collected by Pusan National University (PNU) generator [12] to measure the performance of different predictors. This generator is based on real traffic situation in Pusan city .South Korea.…”

Section: Simulations Resultsmentioning

confidence: 99%

“…In previous an efficient method for predicting travel time by using NBC was proposed by Lee et al [12] which had also been scalable to road networks with arbitrary travel routes.The main idea of NBC was that it would give probable velocity level for any road segment based on historical traffic data. It was shown from experiments that NBC could reduce MARE significantly rather than the other predictors.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

“…For example, the traffic condition of weekend may differ from that of weekday. So, time-varying feature of traffic flow is one of the major issues to estimate accurate travel time [12]. In this study, we focus a new method that is able to predict travel time reliably and accurately.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we focus a new method that is able to predict travel time reliably and accurately. In this research we have tried to combine the advantages of our previous methods namely NBC [12],SMA and CA [13] by eliminating the shortcomings of those methods. Proposed ANN method is able to address the arbitrary route on road networks that is given by user.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Travel Time Prediction Method for Intelligent Transportation System

Nahar¹,

Sultana²

2014

IOSRJCE

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Section: Simulations Resultsmentioning

confidence: 99%

Section: Literature Review and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Travel Time Prediction Method for Intelligent Transportation System

Nahar¹,

Sultana²

2014

IOSRJCE

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show abstract

“…Information based on cellular systems can be gathered in milliseconds compared to traffic data collected from detectors. Conceptually, these traffic data may fall into one of three categories: historical information, real-time information, and predictive information (Lee et al, 2008;Xia, 2010). …”

Section: Travel Data Generationmentioning

confidence: 99%

Road Traffic Congestion Management Based on a Search-Allocation Approach

Raiyn

2017

Transport and Telecommunication Journal

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This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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