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Effect of road structure on the capacity of a signalized road intersection

Chinese Physics B 2009年第10期18卷 4169-4176页

作者：盘佳秀薛郁梁玉娟唐铁桥 Institute of Physics Science and Engineering Guangxi University Shanghai Institute of Applied Mathematics and Mechanics Shanghai University School of Automobile Engineering Beijing University of Astronautics and Aeronautics

In this paper, we use the stochastic Nagel-Schreckenberg （NaSch） model to investigate the influence of a special right-turning lane connecting two main roads on the capacity of a signalized road intersection. It is found that the magnitude of right-turning traffic flow and the linking position of the special right-turning lane can greatly influence the capacity of the signalized road intersection. The relation between traffic flow and entering probability for different distances between the entrance （exit） of the special right-turning lane and the road intersection is simulated and analysed. The corresponding spatiotemporal pattern and phase diagram on different sections of the main road are given under the condition of close proximity to the signalized road intersection, stop-and-go traffic occur and obstruct the intersection. On the contrary, unchanged flux is maintained as the distance exceeds a critical values. All the studies indicate that setting a special right-turning lane by choosing a suitable location near a signalized road intersection can relieve the load of current traffic on the main road and maintain traffic flow.

关键词： traffic flow cellular automaton phase diagram road intersection

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Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

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Frontiers of Earth Science 2017年第1期11卷 63-75页

作者： Zhanyong WANG Qing-Chang LU Hong-Di HE Dongsheng WANG Ya GAO Zhong-Ren PENG Center for ITS and UAV Applications Research State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai 200240 China Logistics Research Center Shanghai Maritime University Shanghai 201306 China Department of Urban and Regional Planning University of Florida Gainesville FL 32611-5706 USA

The minute-scale variations of fine particulate matter （PM2.5） and carbon monoxide （CO） concentrations near a road intersection in Shanghai, China were investigated to identify the influencing factors at three traffic periods. Measurement results demonstrate a synchronous variation of pollutant concentrations at the roadside and setbacks, and the average concentration of PM2.5 at the roadside is 7% （44% for CO） higher than that ofsethacks within 500 m of the intersection. The pollution level at traffic peak periods is found to be higher than that of off-peak periods, and the morning peak period is found to be the most polluted due to a large amount of diesel vehicles and unfavorable dispersion conditions. Partial least square regressions were constructed for influencing factors and setback pollutant concentrations, and results indicate that meteorological factors are the most significant, followed by setback distance from the intersection and traffic factors. CO is found to be sensitive to distance from the traffic source and vehicle type, and highly dependent on local traffic conditions, whereas PM2.5 originates more from other sources and background levels. These findings demonstrate the importance of localized factors in understanding spatiotemporal patterns of air pollution at intersections, and support decision makers in roadside pollution management and control.

关键词： traffic-related pollutants fine-scale variation distance gradient meteorology road intersection

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Estimation of Vehicle Pose and Position with Monocular Camera at Urban road intersections

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Journal of Computer Science & Technology 2017年第6期32卷 1150-1161页

作者： Jin-Zhao Yuan Hui Chen Bin Zhao Yanyan Xu School of Information Science and Engineering Shandong University Jinan 250100 China Beijing Xuanlongding-xun Technology Co. Ltd. Beijing 100041 China Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge 02139 U.S.A.

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

关键词： vehicle pose and position estimation road sign detection homograph matrix road intersection urban envi-ronment

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Enhancing road Drainage Systems for Extreme Storms:Integration of a High-Precision Flow Diversion Module into SWMM Code

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International Journal of Disaster Risk Science 2024年第5期15卷 789-802页

作者： Yuting Ren Zhiyu Shao Qi Zhang Wang Feng Lei Xu Huafeng Gong Scott Yost Lei Chen Hongxiang Chai Key Laboratory of Ecological Environment of Ministry of Education of Three Gorges Reservoir Area Chongqing University College of Environment and Ecology Chongqing University T.Y.Lin International Engineering Consulting (China) Co.Ltd Department of Civil Engineering University of Kentucky

Urban road networks function as surface passage for floodwater transport during extreme storm events to reduce potential risks in the city. However, precise estimation of these flow rates presents a significant challenge. This difficulty primarily stems from the intricate three-dimensional flow fields at road intersections, which the traditional one-dimensional models, such as Storm Water Management Model(SWMM), fail to precisely capture. The two-dimensional and three-dimensional hydraulic models are overly complex and computationally intensive and thus not particularly efficient. This study addresses these issues by integrating a semiempirical flow diversion formula into the SWMM source code. The semiempirical formula, derived from hydraulic experiments and computational fluid dynamics simulations, captures the flow dynamics at T-shaped intersections. The modified SWMM's performance was evaluated against experimental data, and the original SWMM, the two-dimensional MIKE21, and the three-dimensional FLUENT models. The results indicate that the modified SWMM matches the precision of the two-dimensional MIKE21, while significantly reducing computational time. Compared to MIKE21, this study achieved a Nash-Sutcliff e efficiency of 0.9729 and a root mean square error of 0.042, with computational time reduced by 99%. The modified SWMM is suitable for real-sized urban road networks. It provides a high-precision tool for urban road drainage system computation that is crucial for eff ective stormwater management.

关键词： Extreme storms Major drainage system road intersection SWMM (Storm Water Management Model) Urban road flooding

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