@inproceedings{ae249ca7d2b7478090d84b663ca33d50,
title = "A new spatial segmentation method to modelling freeway crash frequency",
abstract = "Due to roadway heterogeneity, it is essential to divide a study network into an appropriate number of segments prior to modelling the crash frequency of that network. To this end, this study proposes a genetic algorithm (GA)-based segmentation method to determine the optimal number of spatial segments and then introduces appropriate count models to estimate the crash frequency on the network. The logic of the proposed GA-based segmentation method is to divide a study network into smallest spatial units (say, 1km), and then cluster the neighbouring spatial units with similar crash occurring patterns into a newly-formed segment, keeping the crash occurring patterns among the formed segments as diverse as possible. The empirical results from Taiwan freeway case study has validated the proposed GA-based segmentation method and the results show that the Negative Binomial model with the proposed segmentation method has outperformed over the other two segmentation methods-Fixedlength method and Interchange method.",
keywords = "Count models, Crash frequency, Genetic algorithms, Optimal spatial segmentation",
author = "Yu-Chiun Chiou and Lan, {Lawrence W.} and Liu, {De Cheng} and Chiang Fu",
year = "2014",
month = dec,
language = "English",
series = "Proceedings of the 19th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2014 - Transportation and Infrastructure",
publisher = "Hong Kong Society for Transportation Studies Limited",
pages = "117--124",
editor = "Z. Leng and Wang, {Y. H.}",
booktitle = "Proceedings of the 19th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2014 - Transportation and Infrastructure",
note = "19th International Conference of Hong Kong Society for Transportation Studies: Transportation and Infrastructure, HKSTS 2014 ; Conference date: 13-12-2014 Through 15-12-2014",
}