A review of feedforward neural networks in transportation research

Mussone, Lorenzo

doi:10.1007/bf03159196

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Cybenko (1989), and Hornik (1991), described the capability of ANN in approximating any function belonging to the Lebesgue two space (L 2 space) with minimum error. Applications regarding transport, planning, control fields, and crash analysis are numerous starting from the 90's (Dougherty 1995, Mussone 1999). Other contributions have faced the problem of crash prediction or severity (Abdelwahab & Abdel-Aty 2001, Chong et.…”

Section: Back-propagation Neural Network (Bpnn)mentioning

confidence: 99%

The influence of pedestrian crossings features on driving behavior and road safety

Bichicchi¹,

Mazzotta²,

Lantieri³

et al. 2017

Transport Infrastructure and Systems

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The paper deals with the identification of variables and models that can explain why a certain Severity Level (SL) may be expected in the event of a certain type of crash at a specific point of an urban road network. Two official crash records, a weather database, a traffic data source, and information on the characteristics of the investigated urban road segments of Turin (Italy) for the seven years from 2006 to 2012 were used. Examination of the full database of 47,592 crash events, including property damage only crashes, reveals 9,785 injury crashes occurring along road segments only. Of these, 1,621 were found to be associated with a dataset of traffic flows aggregated in 5 minutes for the 35 minutes across each crash event, and to weather data recorded by the official weather station of Turin. Two different approaches, a back-propagation neural network model and a generalized linear mixed model were used. Results show the impact of flow and other variables on the SL that may characterize a crash; differences in the significant variables and performance of the two modelling approaches are also commented on in the manuscript.of the research studies have been focused on the likelihood of a crash without considering the crash outcome severity.

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Section: Back-propagation Neural Network (Bpnn)mentioning

confidence: 99%

The influence of pedestrian crossings features on driving behavior and road safety

Bichicchi¹,

Mazzotta²,

Lantieri³

et al. 2017

Transport Infrastructure and Systems

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“…The application potentials of NNs have been shown in many scientific disciplines as well as in transport geography and modelling [see Dougherty (1995) and Mussone (1999) for a review of NNs in transport geography and traffic engineering, and valuable works by Munakata (2008) and Haykin (1999) for a detailed theoretical framework of NNs].…”

Section: Modelling Trip Distribution With Nnsmentioning

confidence: 99%

Modelling trip distribution with fuzzy and genetic fuzzy systems

Kompil

Çelik

2013

Transportation Planning and Technology

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This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity-and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.

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“…The output from the FCFNN can be expressed in terms of the target and error such as (3) Now, let us define the net function of the FCFNN trained with the changed input vector whose element is expanded by the AIA, and the original targets can be expressed as (4) It can be reorganized as (5) If is small, will be approximated, using the Taylor series expansion (TSE) [25] (6)…”

Section: A Mathematical Representationmentioning

confidence: 99%

Partially Connected Feedforward Neural Networks Structured by Input Types

Kang

Işık

2005

IEEE Trans. Neural Netw.

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This paper proposes a new method to model partially connected feedforward neural networks (PCFNNs) from the identified input type (IT) which refers to whether each input is coupled with or uncoupled from other inputs in generating output. The identification is done by analyzing input sensitivity changes as amplifying the magnitude of inputs. The sensitivity changes of the uncoupled inputs are not correlated with the variation on any other input, while those of the coupled inputs are correlated with the variation on any one of the coupled inputs. According to the identified ITs, a PCFNN can be structured. Each uncoupled input does not share the neurons in the hidden layer with other inputs in order to contribute to output in an independent manner, while the coupled inputs share the neurons with one another. After deriving the mathematical input sensitivity analysis for each IT, several experiments, as well as a real example (blood pressure (BP) estimation), are described to demonstrate how well our method works.

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A review of feedforward neural networks in transportation research

Cited by 6 publications

References 23 publications

The influence of pedestrian crossings features on driving behavior and road safety

The influence of pedestrian crossings features on driving behavior and road safety

Modelling trip distribution with fuzzy and genetic fuzzy systems

Partially Connected Feedforward Neural Networks Structured by Input Types

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