Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment

Ma, Changxi; Hao, Wei; Wang, Aobo; Zhao, Hongxing

doi:10.1109/access.2018.2869890

Cited by 117 publications

(67 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, an explanation for this interesting result is that operating speeds in an open space area may tend to be higher than in other areas as open space areas generally have lower traffic densities which increases the likelihood of high level injury severity given an accident happened [48]. For control device types, consistent with previous studies [18,50], passive control was found positively correlated with high level injury severity for drivers with aggressive driving behaviors which is demonstrated by our research. Passive control devices are found to increase the probability of a fatal accident by 7.9% in Table 2 for drivers with aggressive driving behaviors and by 5.2% in Table 3 for drivers with appropriate driving behaviors.…”

Section: Highway-rail Grade Crossing Attributessupporting

confidence: 89%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

Self Cite

117

View full text Add to dashboard Cite

The effect of aggressive driving behavior on driver's injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers' injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers' high level injury severity for drivers with aggressive driving behaviors.

show abstract

Section: Highway-rail Grade Crossing Attributessupporting

confidence: 89%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

Self Cite

117

View full text Add to dashboard Cite

show abstract

“…On the contrary, public transit, with uncertain waiting time and fixed routes, has a limitation to undertake multiple activities in a tour [21,22]. Therefore, the complex trip chains may increase the dependence of travelers on automobiles, which leads to the problems related to auto route choice and optimization, as well as transportation safety [23][24][25][26][27][28][29][30][31]. In order to verify this conclusion, this paper also takes the mode choice of commuters as one of the independent variables.…”

Section: Introductionmentioning

confidence: 94%

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

Guan

Shi

et al. 2019

Sustainability

View full text Add to dashboard Cite

This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters' outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters' non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters' behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.

show abstract

“…22 Different objectives reflect different goal of intersection signal control. 23 At present, the signal timing methods considering only the total vehicle delay have gradually lost recognition. 24 Many researchers constructed multi-objective mathematical models to obtain the better signal schemes.…”

Section: Problem Statementmentioning

confidence: 99%

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

Jia

Lin

Luo

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.

show abstract

Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment

Cited by 117 publications

References 10 publications

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

Contact Info

Product

Resources

About