Neighbouring link travel time inference method using artificial neural network

Vu, Luong; Passow, Benjamin N.; Paluszczyszyn, Daniel; Deka, Lipika; Goodyer, E. N.

doi:10.1109/ssci.2017.8285221

Cited by 2 publications

(10 citation statements)

References 19 publications

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“…In [11], the NLIM was introduced to deal with the datasets with high sparsity and irregularity, which have entries only for major links or entries collected at highly irregular intervals. Having embedded knowledge about the temporal and spatial dependencies between travel times of a target link and its adjacent links the model can overcome sparsity in input data and provide accurate estimations.…”

Section: Set C=0mentioning

confidence: 99%

“…In [11] the Neighbouring Link Inference Method (NLIM) was introduced to deal with the highly sparse data collected from moving observers in a large urban traffic network. The NLIM learns the relationship between travel time of a road segment (link) and traffic parameters (travel time, vehicle class, time of day, day of week) of its nearby links using feed forward back propagation neural network.…”

Section: Introductionmentioning

confidence: 99%

“…An outlier detection based on the Gaussian mixture model was proposed to remove anomalies from travel time data. Results in [11] demonstrated that the NLIM method outperforms the statistic-based and linear least square estimation methods. However, it was observed that the NLIM performs better on major links than minor links; it produces higher Mean Absolute Percentage Error (MAPE) for the minor links than for the major links.…”

Section: Introductionmentioning

confidence: 99%

“…As a substantial extension to methods described in [11], this paper aims to improve the performance of the NLIM, especially for the minor links, as the vast majority (i.e. 70%) of links in the UK fall within the minor link category [12].…”

Section: Introductionmentioning

confidence: 99%

“…70%) of links in the UK fall within the minor link category [12]. Compared to work in [11] this paper contains the following new contributions: i) the new Similar Model Searching (SMS) algorithm; ii) utilisation of travel time data of similar models to improve relationship between links in a target model; iii) a much larger case study area and additional performance indicators. Our results demonstrate that the proposed SMS method can work more effectively with highly sparse data and improve the performance of the original method especially for the minor links.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Set C=0mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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Deep Learning-Based Dynamic Traffic Assignment With Incomplete Origin–Destination Data

Fan

Tang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Dynamic traffic assignment (DTA) methods have been developed fruitfully in theory but are limited in application. Reasons may include the high computational complexity, the difficulty in model calibration, and the reliance on accurate and complete origin–destination (OD) data. Recognizing the incompleteness of OD data in the real world, this paper proposes a deep learning-based DTA model. Specifically, a convolution neural network (CNN) is chosen to account for the spatial correlation of OD pairs. The CNN-based DTA model is trained with the input of historical OD data (incomplete because of limited survey tools) and the output of link flow data (complete thanks to detection technologies). These data are obtained first by simulations in experimental networks and then from an empirical survey in Dazhou, China. Extensive experiments are done about various levels of data incompleteness (measured by the percentage of missing OD data). Comparisons show that the trained CNN-based DTA model performs better with higher accuracy than other common statistical/machine learning algorithms (e.g., feed-forward neural network, k-nearest neighbor, and Kriging). The proposed model also shows robustness to the small-sized dataset, data noise, and network changes. Additional examinations include employing the proposed framework to learn and estimate traffic flow characteristics (i.e., average speed and travel time) and the dynamic flow of turning movements at intersections. Lastly, a case study demonstrates the application of the proposed model using real data. Overall, this study indicates the promising prospect of the CNN-based DTA model as a supplement to traditional ones.

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Neighbouring link travel time inference method using artificial neural network

Cited by 2 publications

References 19 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

Deep Learning-Based Dynamic Traffic Assignment With Incomplete Origin–Destination Data

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