Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey)

Kaygisiz, Ömür; Senbil, Metin; Yildiz, Ahmet

doi:10.1016/j.cstp.2017.02.002

Cited by 22 publications

(19 citation statements)

References 21 publications

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“…Several studies found land uses as the most dominating risk factor for traffic fatalities. The urban built environment is a product of land-use policies, and land-use decisions affects many concerns of the built environment including urban sprawl, air pollution, noise pollution, climate change, and traffic accidents [38,39]. Land-use types are apparently linked with the traffic crashes.…”

Section: Datamentioning

confidence: 99%

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Aljoufie

Tiwari

2021

Sustainability

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Investigating the connections between pedestrian crashes and various urban variables is critical to ameliorate the prediction of pedestrian fatalities, formulate advisories for the stakeholders, and provide an evidence base for policy change to mitigate the occurrence and intensity of pedestrian fatalities. In this paper, we aim to explore the geographically varying association between the pedestrian fatalities and other associated factors of an urban environment in Jeddah city, which is a car-dependent city in Saudi Arabia. At first, Global Moran’s I and Local Indicators of Spatial Association (LISA) were applied to visualize the clustering of pedestrian fatalities in the various districts of Jeddah. Subsequently, we developed Poisson regression models based on their geographically weighted indicators. Both the global and geographically weighted regression models attempt to assess the association between the pedestrian fatalities and the geographically relevant land use and transport infrastructure factors. The results indicate that geographically weighted Poisson regression (GWPR) performed better than the global Poisson counterparts. It is also revealed that the existing transportation infrastructure in Jeddah was significantly associated with the higher pedestrian fatalities. The results have shown that the proposed model in this study can inform transport policies in Jeddah in prioritizing more safety measures for the pedestrians, including expanding pedestrians’ infrastructure, and cautious monitoring of pedestrian footpaths. It can facilitate the analysis and improvement of road safety for pedestrians in car-dependent cities.

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Section: Datamentioning

confidence: 99%

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Aljoufie

Tiwari

2021

Sustainability

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“…Reference [19] analyzed the effects of freeway geometry and traffic characteristics on traffic accidents. Scholars also studied the impact of the urban built environment, weather, geographical distribution and traffic sign deficits on road traffic accidents [20]- [22], [23], [24]. Vehicles are the main components of traffic accidents.…”

Section: Million In 2015mentioning

confidence: 99%

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Lin

et al. 2019

IEEE Access

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The identification of the characteristics of urban road traffic accidents is of great significance for reducing traffic accidents and the corresponding losses. In the context of big data, to accurately understand the characteristics of traffic accidents, the feature set of urban road traffic accidents is proposed, the XGBoost model is used to classify traffic accidents into minor accidents, general accidents, major accidents and serious accidents, and a GA-XGBoost feature recognition model is built. The GA-XGBoost feature recognition model is based on the genetic algorithm (GA) as a factor search algorithm and is verified by applying the big data of traffic accidents in a Chinese city from 2006 to 2016; in addition, the model is compared with the GA-RF, GA-GBDT and GA-LightGBM models. The results show that the GA-XGBoost model can accurately identify the features of the traffic accidents in 7 cities, including driving experience, illegal driving behavior, vehicle age, road intersection type, weather conditions, traffic flow and time interval. Compared with the GA-RF, GA-GBDT and GA-LightGBM models, the recognition features are more accurate, and the performance is better. INDEX TERMS Urban traffic, feature recognition, traffic accidents, XGBoost algorithm, genetic algorithm (GA).

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“…Generally, different models are adopted according to the collected data and research needs. Traditional statistical methods were used to study the field of traffic safety, such as the binary logit model [26,27], the ordered probability model (both probit and logit) [28][29][30][31][32], and the multinomial logit model [12,33]. The binary logit model is also widely used in various fields of sustainability to obtain the influencing factors of an event [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

Xie

Shu

2021

Sustainability

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In recent years, the proportion of rural road fatalities in the country is gradually increasing, among which the traffic safety problems are particularly prominent in the town-rural area and the town-center area. Based on the relevant accident data in Hunan Province in recent years, the chi-square test was conducted to obtain the correlation degree between each risk factor and accident severity based on gender stratification. Then, a binary logistic model was established to obtain the significant factors that affect the accident severity in the town-rural area and the town-center area, respectively. Based on the significant factors, relevant safety improvement measures were proposed for the key areas. The results show that severe accidents were significantly related to single-vehicle factors, motorcycle factors, and intersections factors in the town-rural area. In the town-center area, severe accidents were significantly related to elderly age, single-vehicle factors, and nighttime factors. The study obtained the risk factors in key areas, which can provide a reference for the improvement of traffic safety in key areas of rural roads, to ensure the safety and sustainability of rural traffic.

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Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey)

Cited by 22 publications

References 21 publications

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

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