Generalized Additive Models for Estimating Motorcycle Collisions on Collector Roads

Machsus, Machsus; Basuki, Rachmad; Mawardi, Amalia Firdaus

doi:10.1016/j.proeng.2015.11.105

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Although most of the crash prediction models adopted Linear Regression [15,16], recent studies are found to prefer applying Generalize Additive Model (GAM) to predict crash frequencies [17][18][19][20][21][22]. However, very few studies were found to apply Random Forest (RT) to predict crash occurrences on highways [23,24].…”

Section: State-of-the-art Approachesmentioning

confidence: 99%

Crash Prediction on Road Segments Using Machine Learning Methods

Guerra

Gadhiya

Srisurin

2022

AEJ

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This study adopted the Highway Safety Information System’s (HSIS) data for crashes occurred on road segments to develop supervised machine learning prediction models. Five machine learning models are developed: Linear Regression (LR), Generalize Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). A comparison among the five model was performed using the root mean square error (RMSE) and the mean absolute error (MAE) as quality model indicators. The results indicated that the RF model was found to produce the best crash prediction results. The findings suggested that the increase in Annual Average Daily Traffic (AADT) exponentially increased the number of crashes on highway segments. In addition, roadway segments with the higher design speed induced the lower number of crashes, compared to the segments with the lower design speed. For segments of shorter than 5-mile long, the number of crashes rapidly increased as the segment length increased. However, there was no substantial increase in the number of crashes as the segment length increased for segments of longer than 5 miles. Also, the greater number of lanes on a roadway segment, the greater chance for increasing the number of crashes. Finally, the moderate grades showed the highest risk for occurrences of crashes, respectively followed by flat and rolling grades. These findings are useful for transportation professionals to consider when designing highways.

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Section: State-of-the-art Approachesmentioning

confidence: 99%

Crash Prediction on Road Segments Using Machine Learning Methods

Guerra

Gadhiya

Srisurin

2022

AEJ

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“…Sebesar 80% jumlah kecelakaan lalu lintas di Kota Semarang didominasi oleh kendaraan pribadi [6]. Jenis kendaraan yang mengalami kecelakaan lalu lintas terbanyak adalah sepeda motor, hal ini dikarenakan sepeda motor memiliki risiko kecelakaan lalu lintas yang tinggi [7]- [13]. Badan Pusat Statistik Provinsi Jawa Tengah mencatat jumlah kecelakaan di Kota Semarang pada tahun 2019 ada sebanyak 1.365 jumlah kejadian dengan jumlah korban meninggal 193 korban, luka berat 2 korban, dan luka ringan 1.434 korban, bahkan kerugian materil mencapai Rp 1.306.480.000.000 [5].…”

Section: Pendahuluanunclassified

Analisis Biaya Kecelakaan Lalu Lintas Pengguna Kendaraan Pribadi di Kota Semarang

Budhi

Utanaka

2022

JITS

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“…A GAM can be considered as an extension of a GLM. GAMs have been used for estimating motorcycle collisions (Machsus et al., 2015) and for accident frequency analysis (Xie and Zhang, 2008).…”

Section: Approaches Commonly Used For Modelling Rtcs and Related Phenomenamentioning

confidence: 99%

Trend shifts in road traffic collisions: An application of Hidden Markov Models and Generalised Additive Models to assess the impact of the 20 mph speed limit policy in Edinburgh

Popov

Nightingale

Williams

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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Empirical study of road traffic collision (RTCs) rates is challenging at small geographies due to the relative rarity of collisions and the need to account for secular and seasonal trends. In this paper, we demonstrate the successful application of Hidden Markov Models (HMMs) and Generalised Additive Models (GAMs) to describe RTCs time series using monthly data from the city of Edinburgh (STATS19) as a case study. While both models have comparable level of complexity, they bring different advantages. HMMs provide a better interpretation of the data-generating process, whereas GAMs can be superior in terms of forecasting. In our study, both models successfully capture the declining trend and the seasonal pattern with a peak in the autumn and a dip in the spring months. Our best fitting HMM indicates a change in a fast-declining-trend state after the introduction of the 20 mph speed limit in July 2016. Our preferred GAM explicitly models this intervention and provides evidence for a significant further decline in the RTCs. In a comparison between the two modelling approaches, the GAM outperforms the HMM in out-of-sample forecasting of the RTCs for 2018. The application of HMMs and GAMs to routinely collected data such as the road traffic data may be beneficial to evaluations of interventions and policies, especially natural experiments, that seek to impact traffic collision rates.

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Generalized Additive Models for Estimating Motorcycle Collisions on Collector Roads

Cited by 7 publications

References 6 publications

Crash Prediction on Road Segments Using Machine Learning Methods

Crash Prediction on Road Segments Using Machine Learning Methods

Analisis Biaya Kecelakaan Lalu Lintas Pengguna Kendaraan Pribadi di Kota Semarang

Trend shifts in road traffic collisions: An application of Hidden Markov Models and Generalised Additive Models to assess the impact of the 20 mph speed limit policy in Edinburgh

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