Incorporating transportation safety into land use planning: Pre-assessment of land use conversion effects on severe crashes in urban China

Xie, Bo; An, Zihao; Zheng, Yiling; Li, Zhigang

doi:10.1016/j.apgeog.2018.12.003

Cited by 30 publications

(18 citation statements)

References 63 publications

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“…This finding corroborates the finding of Humpel et al [17] that perceptions of safety were significantly correlated to light, moderate and vigorous PA in most of the 19 reviewed empirical studies. The sense of safety is a basic requirement for outdoor PA: people will instinctively reduce their usage of unsafe streets and other open spaces [40,73,74]. Another important finding is that with the increased intensity levels of PA, more neighbourhood perceptual indicators become significant.…”

Section: Discussionmentioning

confidence: 99%

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques

et al. 2019

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Background: Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators-namely, wealthy, safe, lively, depressing, boring and beautiful-and residents' time spent on PA in Guangzhou, China. Methods:A human-machine adversarial scoring system was developed to predict perceptions of neighbourhood environments based on Tencent Street View imagery and deep learning techniques. Image segmentation was conducted using a fully convolutional neural network (FCN-8s) and annotated ADE20k data. A human-machine adversarial scoring system was constructed based on a random forest model and image ratings by 30 volunteers. Multilevel linear regressions were used to examine the association between each of the six indicators and time spent on PA among 808 residents living in 35 neighbourhoods.Results: Total PA time was positively associated with the scores for "safe" [Coef. = 1.495, SE = 0.558], "lively" [1.635, 0.789] and "beautiful" [1.009, 0.404]. It was negatively associated with the scores for "depressing" [− 1.232, 0.588] and "boring" [− 1.227, 0.603]. No significant linkage was found between total PA time and the "wealthy" score. PA was further categorised into three intensity levels. More neighbourhood perceptual indicators were associated with higher intensity PA. The scores for "safe" and "depressing" were significantly related to all three intensity levels of PA. Conclusions:People living in perceived safe, lively and beautiful neighbourhoods were more likely to engage in PA, and people living in perceived boring and depressing neighbourhoods were less likely to engage in PA. Additionally, the relationship between neighbourhood perception and PA varies across different PA intensity levels. A combination of Tencent Street View imagery and deep learning techniques provides an accurate tool to automatically assess neighbourhood environment exposure for Chinese large cities.

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Section: Discussionmentioning

confidence: 99%

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques

et al. 2019

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“…And meanwhile the security hot-zones are identified. Xie [25] applied the traffic safety theory into the land use planning and evaluated the relationship between the traffic accidents and land use conversion. Soltani [26] identified the potential spatio-temporal distribution pattern of road traffic accidents by TAZs.…”

Section: Traffic Incidents On Urban Road Networkmentioning

confidence: 99%

Feature Extraction and Representation of Urban Road Networks Based on Travel Routes

Huang

Shao

et al. 2020

Sustainability

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Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model parameter calibration is performed. We explain the effectiveness of features from visualization, similarity analysis, and link prediction. In visualization, the features of nodes naturally present a clustered pattern, and different clusters correspond to different regions in the road network. Meanwhile, the features of nodes still retain their spatial information in similarity analysis. The proposed method StreetNode2VEC obtains a AUC score of 0.813 in link prediction, which is greater than that obtained from Graph Convolutional Network (GCN) and Node2vec. This suggests that the features of nodes can be used to effectively and credibly predict whether a link should be established between two nodes. Overall, our work provides a new way of representing road nodes in the road network, which have potential in the traffic safety planning field.

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“…Several studies have also emphasized how factors such as the exploratory variables have different impacts for each levels of severity [53]. Huang et al [54] investigated the relations between crash frequency with a variety of aggregate road features, traffic patterns, demographic and socio-economic characteristics.…”

Section: Crash Datamentioning

confidence: 99%

Development of Macro-Level Safety Performance Functions in the City of Naples

et al. 2019

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This paper presents macro-level safety performance functions and aims to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce road crashes. In the past decade, several studies have been conducted for crash modeling at a macro-level, yet in Italy, macro-level safety performance functions have neither been calibrated nor used, until now. Therefore, for Italy to be able to fully benefit from applying these models, it is necessary to calibrate the models to local conditions. Generalized linear modelling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 15,254 crashes which occurred in the period of 2009–2011 in Naples, Italy. Four traffic analysis zones (TAZ) levels were used, as one of the aims of this paper is to check the extent to which these zoning levels help in addressing the issue. The models were developed by the stepwise forward procedure using explanatory Socio-Demographic (S-D), Transportation Demand Management (TDM), and Exposure variables. The most significant variables were: children and young people placed in re-education projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour. An important result of the study is that children and young people placed in re-education projects negatively affects the frequency of crashes, i.e., it has a positive safety effect. This demonstrates the effectiveness of education projects, especially on children from disadvantaged neighbourhoods.

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Incorporating transportation safety into land use planning: Pre-assessment of land use conversion effects on severe crashes in urban China

Abstract: This is a repository copy of Incorporating transportation safety into land use planning: Pre-assessment of land use conversion effects on severe crashes in urban China.

Cited by 30 publications

References 63 publications

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques

Feature Extraction and Representation of Urban Road Networks Based on Travel Routes

Development of Macro-Level Safety Performance Functions in the City of Naples

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