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Wei Wu
Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science & Technology, Changsha 410114, Hunan, China

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Journal article
Published: 29 December 2019 in Sensors
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Most existing signal timing plans are optimized given vehicles’ arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. “Speed guidance model at a red light” and “speed guidance model at a green light” are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions.

ACS Style

Wei Wu; Ling Huang; Ronghua Du. Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment. Sensors 2019, 20, 191 .

AMA Style

Wei Wu, Ling Huang, Ronghua Du. Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment. Sensors. 2019; 20 (1):191.

Chicago/Turabian Style

Wei Wu; Ling Huang; Ronghua Du. 2019. "Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment." Sensors 20, no. 1: 191.

Journal article
Published: 29 December 2018 in Applied Sciences
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Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

ACS Style

Kejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences 2018, 9, 104 .

AMA Style

Kejun Long, Wukai Yao, Jian Gu, Wei Wu, Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences. 2018; 9 (1):104.

Chicago/Turabian Style

Kejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. 2018. "Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration." Applied Sciences 9, no. 1: 104.

Journal article
Published: 22 November 2018 in Sustainability
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The mechanisms of traffic congestion generation are more than complicated, due to complex geometric road designs and complicated driving behavior at urban expressways in China. We employ a cell transmission model (CTM) to simulate the traffic flow spatiotemporal evolution process along the expressway, and reveal the characteristics of traffic congestion occurrence and propagation. Here, we apply the variable-length-cell CTM to adapt the complicated road geometry and configuration, and propose the merge section CTM considering drivers’ mandatory lane-changing and other unreasonable behavior at the on-ramp merge section, and propose the diverge section CTM considering queue length end extending the expressway mainline to generate a dynamic bottleneck at the diverge section. In the new improved CTM model, we introduce merge ratio and diverge ratio to describe the effect of driver behavior at the merge and diverge section. We conduct simulations on the real urban expressway in China, with results showing that the merge section and diverge section are the original location of expressway traffic congestion generation, and on/off-ramp traffic flow has a great effect on the expressway mainline operation. When on-ramp traffic volume increases by 40%, the merge section delay increases by 35%, and when off-ramp capacity increases by 100 veh/hr, the diverge section delay decreases about by 10%, which proves the strong interaction between expressway and adjacent road networks. Our results provide the underlying insights of traffic congestion mechanism in urban expressway in China, which can be used to better understand and manage this issue.

ACS Style

Kejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability 2018, 10, 4359 .

AMA Style

Kejun Long, Qin Lin, Jian Gu, Wei Wu, Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability. 2018; 10 (12):4359.

Chicago/Turabian Style

Kejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. 2018. "Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China." Sustainability 10, no. 12: 4359.

Preprint
Published: 25 October 2018
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Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

ACS Style

Kejun Long; Wukai Yao; Jian Gu; Wei Wu. Predicting Freeway Travelling Time Using Multiple-Source Data. 2018, 1 .

AMA Style

Kejun Long, Wukai Yao, Jian Gu, Wei Wu. Predicting Freeway Travelling Time Using Multiple-Source Data. . 2018; ():1.

Chicago/Turabian Style

Kejun Long; Wukai Yao; Jian Gu; Wei Wu. 2018. "Predicting Freeway Travelling Time Using Multiple-Source Data." , no. : 1.

Journal article
Published: 01 January 2018 in Journal of Asian Architecture and Building Engineering
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Growth in car ownership has significant impacts on the use of urban space and management of urban environments, especially in rapidly developing countries such as China. However, among voluminous literature, few studies have investigated the correlates of household car ownership in the Chinese context, leading to the lack of effective measures to tackle rapid motorization. This study explored the impacts of household-level socioeconomic characteristics and neighborhood-level built environment on household car ownership, with data collected from 25,325 households in Zhongshan, China. The Zero-inflated Poission regression models detect that, all else being equal, living in a neighborhood with a compact and mixed urban form, better bus service and adjacency to CBD is associated with fewer household cars and higher probability to own zero cars. The models also suggest that household size and income, number of employed or students, and availability of competitive vehicles are significantly related to the number of cars in a household. The findings facilitate our understanding of the effects of socioeconomic characteristics and built environment on household car ownership and provide insights into an effective design of measures to slow down the rapid motorization in China.

ACS Style

Yi Zhang; Chaoyang Li; Qixing (Eddie) Liu; Wei Wu. The Socioeconomic Characteristics, Urban Built Environment and Household Car Ownership in a Rapidly Growing City: Evidence from Zhongshan, China. Journal of Asian Architecture and Building Engineering 2018, 17, 133 -140.

AMA Style

Yi Zhang, Chaoyang Li, Qixing (Eddie) Liu, Wei Wu. The Socioeconomic Characteristics, Urban Built Environment and Household Car Ownership in a Rapidly Growing City: Evidence from Zhongshan, China. Journal of Asian Architecture and Building Engineering. 2018; 17 (1):133-140.

Chicago/Turabian Style

Yi Zhang; Chaoyang Li; Qixing (Eddie) Liu; Wei Wu. 2018. "The Socioeconomic Characteristics, Urban Built Environment and Household Car Ownership in a Rapidly Growing City: Evidence from Zhongshan, China." Journal of Asian Architecture and Building Engineering 17, no. 1: 133-140.

Journal article
Published: 12 November 2016 in Sustainability
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Developing public transportation and giving priority to buses is a feasible solution for improving the level of public transportation service, which facilitates congestion alleviation and prevention, and contributes to urban development and city sustainability. This paper presents a novel bus operation control strategy including both holding control and speed control to improve the level of service of transit systems within a connected vehicle environment. Most previous work focuses on optimization of signal timing to decrease the bus signal delay by assuming that holding control is not applied; the speed of buses is given as a constant input and the acceleration and deceleration processes of buses can be neglected. This paper explores the benefits of a bus operation control strategy to minimize the total cost, which includes bus signal delay, bus holding delay, bus travel delay, acceleration cost due to frequent stops and intense driving. A set of formulations are developed to explicitly capture the interaction between bus holding control and speed control. Experimental analysisand simulation tests have shown that the proposed integrated operational model outperforms the traditional control, speed control only, or holding control only strategies in terms of reducing the total cost of buses. The sensitivity analysis has further demonstrated the potential effectiveness of the proposed approach to be applied in a real-time bus operation control system under different levels of traffic demand, bus stop locations, and speed limits.

ACS Style

Wei Wu; Wanjing Ma; Kejun Long; Heping Zhou; Yi Zhang. Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment. Sustainability 2016, 8, 1170 .

AMA Style

Wei Wu, Wanjing Ma, Kejun Long, Heping Zhou, Yi Zhang. Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment. Sustainability. 2016; 8 (11):1170.

Chicago/Turabian Style

Wei Wu; Wanjing Ma; Kejun Long; Heping Zhou; Yi Zhang. 2016. "Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment." Sustainability 8, no. 11: 1170.