Real‐time estimation of freeway travel time with recurrent congestion based on sparse detector data

Lu, Lili; Wang, Jian; He, Zhengbing; Chan, Ching-Yao

doi:10.1049/iet-its.2016.0356

Cited by 27 publications

(11 citation statements)

References 39 publications

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“…Travel time estimation is defined as the method which approximates the travel time of vehicles on a given link during a given period. The existing travel time estimation methods can be classified as direct or indirect methodologies [14]. In the direct method, travel time is estimated based on data samples that are obtained from moving observers e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Mathematical and statistical methodologies usually perform less accurate in urban traffic network where the traffic condition can be complex. There are also approaches that utilise artificial neural networks [14], support vector machines [23], linear regression [23] and non-linear least square [24]. They can learn relationships and create models using unstructured dataset.…”

Section: Related Workmentioning

confidence: 99%

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Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Passow

Paluszczyszyn

et al. 2021

IEEE Intell. Transport. Syst. Mag.

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Travel time is a basic measure based on which intelligent transportation systems such as traveller information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilise labelled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Passow

Paluszczyszyn

et al. 2021

IEEE Intell. Transport. Syst. Mag.

View full text Add to dashboard Cite

show abstract

“…Chen et al [24] proposed a PCNN framework to predict short-term traffic congestion, which comprises two components, i.e., transformation of the time series into a two-dimensional (2-D) matrix as the input of the PCNN model and convolutional operation. Lu et al [25] proposed a methodology that utilized the data obtained from sparse detectors to estimate the freeway travel time in realtime because of the vulnerability associated with loop detectors. Singh and Mohan [26] utilized a model containing a stacked autoencoder to automatically detect road accidents using surveillance videos exhibiting raw pixel intensity and evaluated the effectiveness of the model in the Hyderabad City, India.…”

Section: Related Workmentioning

confidence: 99%

Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks

Liu

Cai²,

Zhong

et al. 2020

IEEE Access

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The timely and accurate detection of traffic incidents is beneficial to reduce associated economic losses and avoid secondary crashes. Inspired by the impressive success of the image classification algorithms, especially convolutional neural networks (CNNs), this study proposes a novel framework to detect highway traffic incidents by learning the traffic state as images. In such a framework, the probe vehicles equipped with the global positioning system equipment are used to obtain data. The Gramian Angular Difference Fields and Piecewise Aggregation Approximation algorithms are used to convert the link speed time series data into images. CNNs can extract the traffic features based on these images and consider an incident detection problem as a binary classification task. Further, the effectiveness of the proposed framework is evaluated by applying it to detect the traffic in a real-world environment, i.e., the Guangzhou Airport Expressway. The results illustrate that the proposed model outperforms several other algorithms with respect to almost all the performance indexes, including the detection rate with different false alarm rates and the area under the receiver operating characteristic curve.

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“…Researchers have made efforts to combine statistical methods [ 19 ] and machine learning methods [ 20 , 21 ]. Data-driven traffic surveillance methods are now mainly faced with the data sparsity problem [ 22 , 23 ], which cannot be solved by statistical models and machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Yang

Liu

Huang

et al. 2020

Sensors

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Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

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Real‐time estimation of freeway travel time with recurrent congestion based on sparse detector data

Cited by 27 publications

References 39 publications

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks

Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

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