Investigating the safety impact of roadway network features of suburban arterials in Shanghai

Wang, Xuesong; Yuan, Jinghui; Schultz, Grant G.

doi:10.1016/j.aap.2018.01.029

Cited by 46 publications

(25 citation statements)

References 52 publications

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“…Therefore, the significant variables in four time-slice models were investigated. Average speed was found to have significant negative effect on the odds of crash occurrence, which is consistent with previous studies , Ahmed et al 2012a, b, Ahmed and Abdel-Aty 2012, Xu et al 2012, Shi and Abdel-Aty 2015, Yu et al 2016, Yuan et al 2018. The left turn volume was surprisingly found to be negatively correlated with the odds of crash occurrence, which might be explained as the higher left turn volume may increase the driver awareness when approaching the entering approach, which may improve the safety performance.…”

Section: Discussionsupporting

confidence: 89%

“…Substantial efforts have been made by previous researchers to reveal the relationship between crash frequency of signalized intersections and all the possible contributing factors such as roadway geometric, signal control, and traffic characteristics, etc. (Chin and Quddus 2003, Wang et al 2009, Guo et al 2010, Lee et al 2017, Wang and Yuan 2017, Cai et al 2018a, Cai et al 2018b).…”

Section: Introductionmentioning

confidence: 99%

“…However, little research has been conducted on the real-time safety of urban arterials (Theofilatos 2017, Yuan et al 2018, especially signalized intersections (Mussone et al 2017). Mussone et al (2017) examined the factors which may affect the crash severity level at intersection based on real-time traffic flow and environmental characteristics, and they found that the real-time traffic flow characteristics have a relevant role in predicting crash severity.…”

Section: Introductionmentioning

confidence: 99%

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Approach-level real-time crash risk analysis for signalized intersections

Yuan

Abdel-Aty

2018

Accident Analysis & Prevention

Self Cite

100

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Intersections are among the most dangerous roadway facilities due to the complex traffic conflicting movements and frequent stop-and-go traffic. However, previous intersection safety analyses were conducted based on static and highly aggregated data (e.g., annual average daily traffic (AADT), annual crash frequency). These aggregated data may result in unreliable findings simply because they are averages and cannot represent the real conditions at the time of crash occurrence. This study attempts to investigate the relationship between crash occurrence at signalized intersections and real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Central Florida. The intersection and intersection-related crashes were collected and then divided into two types, i.e., within intersection crashes and intersection entrance crashes. Bayesian conditional logistic models were developed for these two kinds of crashes, respectively. For the within intersection models, the model results showed that the through volume from "A" approach (the traveling approach of at-fault vehicle), the left turn volume from "B" approach (near-side crossing approach), and the overall average flow ratio (OAFR) from "D" approach (far-side crossing approach), were found to have significant positive effects on the odds of crash occurrence. Moreover, the increased adaptability for the left turn signal timing of "B" approach and more priority for "A" approach could significantly decrease the odds of crash occurrence. For the intersection entrance models, average speed was found to have significant negative effect on the odds of crash occurrence. The longer average green time and longer average waiting time for the left turn phase, higher green ratio for the through phase, and higher adaptability for the through phase can significantly improve the safety performance of intersection entrance area. In addition, the average queue length on the through lanes was found to have positive effect on the odds of crash occurrence. These results are important in real-time safety applications at signalized intersections in the context of proactive traffic management.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approach-level real-time crash risk analysis for signalized intersections

Yuan

Abdel-Aty

2018

Accident Analysis & Prevention

Self Cite

100

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“…Xu et al [54] explored the impacts of urban rail transit on commercial property value in which spatial autoregressive models were used to estimate commercial value capture. The spatial autocorrelation is also proven to exist in accidents that happen on suburban highways [55]. Moreover, the relationship between transport emissions and land use is estimated and the CAR model is found to be effective in representing the spatial autocorrelation [27].…”

Section: Spatial Effects In City Contextmentioning

confidence: 99%

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

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Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

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“…A free lane change considers a lane change of a vehicle to obtain speed advantages or more space [43]. Some previous studies have focused on the relationship between speed variations and crash rates or crash risk [19,31,[44][45][46][47], while other studies have examined the number of lane changes under different traffic flow parameters to analyze the capacity of multilane roads [39,[48][49][50]. However, these studies lacked research specifically regarding the identification and quantification of significant traffic parameters that affect lane changing using real data by considering lane changing as one of the essential subjects of sustainable traffic safety.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Farooq

Juhász

2019

Sustainability

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Lane changing of traffic flow is a complicated and significsant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.

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Investigating the safety impact of roadway network features of suburban arterials in Shanghai

Cited by 46 publications

References 52 publications

Approach-level real-time crash risk analysis for signalized intersections

Approach-level real-time crash risk analysis for signalized intersections

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

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