Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression

Khattak, Muhammad Wisal; Backer, Hans De; Winne, Pieter De; Brijs, Tom; Pirdavani, Ali

doi:10.1061/9780784484333.010

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…The proper design of the cross-section of the road is critical because of its impact on the facility's safety, capacity, and functioning. Although assessing the impact on its capacity and functioning is generally easy, it takes work to evaluate safety (Ministry of Public Works and Public Housing of the Republic of Indonesia, 2021); (Khattak, et al, 2021).…”

Section: Geometric Roadsmentioning

confidence: 99%

The Horizontal Curved Geometric Design with Autocad® Civil 3D on Jalan Muara Wahau, East Kalimantan

Megarestya¹,

Rifai²,

Isradi³

2023

IJOMS

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Roads connect one area to another. Therefore, an excellent geometric design of the road can reduce the percentage of accidents. For this reason, AutoCAD Civil 3D® is used as a means of planning. This road planning aims to plan the horizontal alignment of the road connecting the main road with kpc Tanjung bara (coal port). This street is located on Jalan Poros Muara Wahau, East Kalimantan. This study used secondary data obtained through google earth and global mapper. Then the data is processed using qualitative research methods. This study found that the road section length was 6,086 m or 6,086 km (STA 0 + 000 – 6 + 086). Bends P1 on (STA 2+680 – 3+260) are planned with the type S-C-S bend. The bend has an R of 600 meters, an e of 4.3%, and a Ls of 119 meters.

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Section: Geometric Roadsmentioning

confidence: 99%

The Horizontal Curved Geometric Design with Autocad® Civil 3D on Jalan Muara Wahau, East Kalimantan

Megarestya¹,

Rifai²,

Isradi³

2023

IJOMS

View full text Add to dashboard Cite

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“…Typical urban area characteristics, such as high-density development, aggressive land-use planning, local regulations, on-street parking, bike lanes, and mixed traffic, generally make traffic safety problems, solutions, and analysis more complex [22]. Despite that, numerous studies have developed models for urban roads, exploring the effects of traffic and roadway infrastructure attributes, including average annual daily traffic (AADT), number of lanes, lane width, on-street parking, speed limits, etc., on crash occurrence [23][24][25][26]. For example, Liu et al [23] estimated crash prediction models (SPFs and crash prediction models are used interchangeably in this text) for urban segments and reported that AADT per lane, the number of lanes, and segment length had significant non-positive effects on crashes and that segments with lower speed limits were associated with more crashes than those with higher speed limited (45 mph (70 km/h) or above).…”

Section: Introductionmentioning

confidence: 99%

“…The applications of GP models for analysing count data could be found in other fields; for instance, vehicle insurance claims [44], shipping damage incidents [45], environmental sciences [46], transport demand management [47] and medical sciences [48]. However, only a handful of studies have used the GP model for developing SPFs in transportation safety literature [21,26,49]. Those studies reported that GP models are equally capable of crash data analysis and, in some cases, can even outperform NB models [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models

Khattak,

De Backer,

De Winne

et al. 2024

Infrastructures

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This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios.

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Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression

Cited by 2 publications

References 30 publications

The Horizontal Curved Geometric Design with Autocad® Civil 3D on Jalan Muara Wahau, East Kalimantan

The Horizontal Curved Geometric Design with Autocad® Civil 3D on Jalan Muara Wahau, East Kalimantan

Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models

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