Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network

Chang, Li-Yen

doi:10.1016/j.ssci.2005.04.004

Cited by 276 publications

(162 citation statements)

References 23 publications

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“…The reason for this is that in highways with more lanes there are more interactions between drivers so the probability of crashing increases. This result is in line with the findings by Noland and Oh (2004), Chang (2005) and Milton and Mannering (1998).…”

Section: Modelling Accidents Injuries and Fatalitiessupporting

confidence: 93%

“…However, some authors seem to suggest that the Poisson regression and the NB models possess most of the desirable statistical properties in describing vehicle accident events. The Poisson and NB regression models have been widely used to analyze count data where the dependent variable is discrete and defined for non-negative integers corresponding to the number of events occurring in a given intervals (Miaou and Lum, 1993;Noland and Oh, 2004;Chang, 2005;Caliendo et al, 2007;Arenas et al, 2009). In light of these findings, negative binomial models were applied in this study.…”

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

“…Other studies have been carried out in recent years to establish the relationships between accidents and the effect of different vehicle mix (Tay, 2003;Hiselius, 2004;Wong et al, 2007;Haleem, 2011, Pei et al, 2011), the frequency of intersections (Ivan and O'Mara, 1997), environmental factors (Chang, 2005;Caliendo et al, 2007;Fridstrøm et al, 1995;Shankar et al, 1995), geometric infrastructure characteristics (Chang, 2005;Hauer et al, 2004), number of lanes (Noland and Oh, 2004;Chang, 2005;Milton and Mannering, 1998), and speed limits (Fridstrøm et al, 1995;Ossiander and Cummings, 2002;Wong et al, 2005).…”

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

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The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Rangel

Vassallo

Herraiz

2013

Accident Analysis & Prevention

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The goal of this paper is to evaluate whether the incentives incorporated in toll highway concession contracts in order to encourage private operators to adopt measures to reduce accidents are actually effective at improving safety. To this end, we implemented negative binomial regression models using information about highway characteristics and accident data from toll highway concessions in Spain from 2007 to 2009. Our results show that even though road safety is highly influenced by variables that are not managed by the contractor, such as the annual average daily traffic (AADT), the percentage of heavy vehicles on the highway, number of lanes, number of intersections and average speed; the implementation of these incentives has a positive influence on the reduction of accidents and injuries. Consequently, this measure seems to be an effective way of improving safety performance in road networks.

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Section: Modelling Accidents Injuries and Fatalitiessupporting

confidence: 93%

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

See 1 more Smart Citation

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Rangel

Vassallo

Herraiz

2013

Accident Analysis & Prevention

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“…The national freeway of Taiwan was analyzed with the model of ANN by Chang [15]. The other study considered the relationship between the probabilities of involvement in a traffic accident and driver characteristics using ANN [16].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Çodur¹,

Tortum²

2015

PROMET

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“…It had been successfully implemented in the real world application which are accounting and finance [1], [2], health and medicine [3], [4], engineering and manufacturing [5], [6], marketing [7], [8] and general applications [9], [10], [11]. Multilayer perceptron is one of the most popular neural network models due to its clear architecture and comparably algorithm [12].…”

mentioning

confidence: 99%

The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems

Hamid

Nawi

Ghazali

2011

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-The back propagation algorithm has been successfully applied to wide range of practical problems. Since this algorithm uses a gradient descent method, it has some limitations which are slow learning convergence velocity and easy convergence to local minima. The convergence behaviour of the back propagation algorithm depends on the choice of initial weights and biases, network topology, learning rate, momentum, activation function and value for the gain in the activation function. Previous researchers demonstrated that in 'feed forward' algorithm, the slope of the activation function is directly influenced by a parameter referred to as 'gain'. This research proposed an algorithm for improving the performance of the current working back propagation algorithm which is Gradien Descent Method with Adaptive Gain by changing the momentum coefficient adaptively for each node. The influence of the adaptive momentum together with adaptive gain on the learning ability of a neural network is analysed. Multilayer feed forward neural networks have been assessed. Physical interpretation of the relationship between the momentum value, the learning rate and weight values is given. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and current Gradient Descent Method with Adaptive Gain was verified by means of simulation on three benchmark problems. In learning the patterns, the simulations result demonstrate that the proposed algorithm converged faster on Wisconsin breast cancer with an improvement ratio of nearly 1.8, 6.6 on Mushroom problem and 36% better on Soybean data sets. The results clearly show that the proposed algorithm significantly improves the learning speed of the current gradient descent back-propagatin algorithm.

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Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network

Cited by 276 publications

References 23 publications

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems

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