Adapting traffic simulation for traffic management: A neural network approach

Passow, Benjamin N.; Elizondo, David; Chiclana, Francisco; Witheridge, Simon; Goodyer, E. N.

doi:10.1109/itsc.2013.6728427

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Therefore detecting outliers is necessary before utilising data to obtain a reasonable solution to a problem [19]. Several approaches have been used to detect and remove outliers; these range from statistics, to ANNs and fuzzy algorithms [19]- [21].…”

Section: A Outlier Travel Time Data Detectionmentioning

confidence: 99%

“…In this section, we propose a method to model the relationship between travel time of a link and traffic parameters (travel time, vehicle class, time of day, the day of a week) of its nearby links based on feed forward back propagation neural network (FF-BP-ANN). Feed Forward ANN with Back Propagation algorithm has been widely used in solving various classification, estimation and forecasting problems [6], [16], [17], [21].…”

Section: B Learning Relationship Between Linksmentioning

confidence: 99%

“…example, it is seen that in [21], number of hidden layers and number of hidden nodes heavily affects the performance of FF-BP-ANN that was trained on the original data set as well as on the data that were applied to an adaptive filter.…”

mentioning

confidence: 99%

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Neighbouring link travel time inference method using artificial neural network

Passow

Paluszczyszyn

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods.

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Section: A Outlier Travel Time Data Detectionmentioning

confidence: 99%

Section: B Learning Relationship Between Linksmentioning

confidence: 99%

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Neighbouring link travel time inference method using artificial neural network

Passow

Paluszczyszyn

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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“…The presence of outliers can lead to unpredictable results [14], [15]. Microsimulation tasks are sensitive to outliers [16], and conducting outlier detection before any analysis is therefore important. Outlier removal methods are often selected by the underlying assumptions and the actual condition of the data.…”

Section: B Phase 1: Outlier Removalmentioning

confidence: 99%

SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

Li¹,

Zhao

2020

Preprint

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A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this paper, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution, harderto-collect information (e.g., individual level records) from many low-resolution, easy-to-collect sources, and propose a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula). For given low-resolution datasets, the central idea of SYNC is to fit Gaussian copula models to each of the lowresolution datasets in order to correctly capture dependencies and marginal distributions, and then sample from the fitted models to obtain the desired high-resolution subsets. Predictive models are then used to merge sampled subsets into one, and finally, sampled datasets are scaled according to low-resolution marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-ofthe-art machine learning and statistical techniques, 2) perform simulation studies to validate SYNC's performance as a synthetic data generation algorithm, 3) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 4) release an easy-to-use framework implementation for reproducibility and scalability at the production level that easily incorporates new data.

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“…These errors are referred to as outliers, which need to be identified and eliminated from the data sets in order to improve the models performance. In this work a Self-Organizing Map (SOM) is proposed for filtering and handling outliers [12].…”

Section: Data Sourcesmentioning

confidence: 99%

Infering Air Quality from Traffic Data Using Transferable Neural Network Models

Molina‐Cabello

Passow

Domínguez

et al. 2019

Advances in Computational Intelligence

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This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.

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Adapting traffic simulation for traffic management: A neural network approach

Cited by 10 publications

References 31 publications

Neighbouring link travel time inference method using artificial neural network

Neighbouring link travel time inference method using artificial neural network

SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

Infering Air Quality from Traffic Data Using Transferable Neural Network Models

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