Comprehensive Transportation Key Laboratory of Sichuan Province

Chengdu, China

Comprehensive Transportation Key Laboratory of Sichuan Province

Chengdu, China
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Yun L.,Southwest Jiaotong University | Jiang Y.,Southwest Jiaotong University | Jiang Y.,Comprehensive Transportation Key Laboratory of Sichuan Province | Jiang Y.,National United Engineering Laboratory of Integrated and Intelligent Transportation | And 2 more authors.
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) | Year: 2014

Existing queuing theory based researches of hub taxi off-site system are mostly for single-desk issue, where is a wide gap with the actual situation. In order to solve this problem, we first abstracted the system into an asynchronous single vacation queuing system and got the steady state indicators; then we created a service-desk number optimization model for hub taxi off-site queuing system considering the level of service, and wrote the computer program based on MATLAB software. Numerical example shows that the calculated results of optimization model are of a higher level of service than the planning scheme.


Zheng C.,Comprehensive Transportation Key Laboratory of Sichuan Province | Zheng C.,Hohai University | Zheng C.,Key Laboratory of Transportation Planning and Management of Jiangsu Province | Gui L.,Hohai University | Yuan L.,Hohai University
Proceedings - 2010 WASE International Conference on Information Engineering, ICIE 2010 | Year: 2010

Bicycling is one of the most important modes of urban transportation in China. The objective of this study is to develop a procedure to identify the factors affecting travelers' choice of the park-and-ride trip mode. Two trip alternatives were considered, including the bicycling and the park-and-ride trip mode. In this study, a multinomial logit model structure was proposed and the use of the model was explained using a case study. The factors considered in the model include the travel time, travel cost, walking distance, the need for second transfer, and the health condition of travelers. Even though the data used for model specification was not real survey data, the modeling procedure proposed by this study can be followed by transportation planners to develop discrete choice models to help make various design decisions regarding the application of the park-and-ride mode. © 2010 IEEE.


Qiu X.-P.,Southwest Jiaotong University | Qiu X.-P.,Comprehensive Intelligent Transportation National and Local Joint Engineering Laboratory | Qiu X.-P.,Comprehensive Transportation Key Laboratory of Sichuan Province | Liu Y.-L.,Southwest Jiaotong University | And 3 more authors.
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2015

Lane change behavior is one of the most foundational driving behaviors in microscopic traffic flow. Researching the lane change behavior contributes to improving the simulation accuracy of lane change models and reducing traffic accidents caused by improper lane change behavior. The current lane change model is the decision model mostly based on the way of driver's thinking. The shortcoming of current models is difficult to catch certain potential decision-making model and influencing factors in the driver's decisionmaking process. In view of this, this paper introduces a typical artificial intelligence method, Bayesian networks, to establish a new lane change model, and tries to improve the accuracy of the lane change model by machine learning. It uses a segmented discrete method to preprocess vehicle trajectory measurement data, and uses the processed data to training and verification this model. The verification results show that, this model's recognition rate to lane change behavior can reach more than 88%. In addition, this model can be further applied to the development of a driver assistance system. ©, 2015, Science Press. All right reserved.


Qiu X.-P.,Southwest Jiaotong University | Qiu X.-P.,Comprehensive Intelligent Transportation National and Local Joint Engineering Laboratory | Qiu X.-P.,Comprehensive Transportation Key Laboratory of Sichuan Province | Yu D.,Southwest Jiaotong University | And 3 more authors.
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2015

With the traffic congestion increasing significantly, traffic safety level declines and traffic accident rate increases gradually. To improve driving safety, the length of the cellular cells is fined, and the Gipps' safe distance rule is introduced to improve the NaSch model, further, a new cellular automata traffic flow model is proposed. The Gipps' safe distance rule is widely proved to have good performance in describing the vehicle driving behavior. In addition, we use the field data to calibrate and evaluate the proposed model. The numerical simulation analysis is carried out to analyze the model. Model evaluation results show that the performance of the new model is better than NaSch model. The simulation results show that the improved model can describe the traffic flow characteristics well and can reproduce free flow, synchronized flow, congestion and other traffic phenomenon in the real traffic flow. Furthermore, the study also found that the drivers' overestimation of the maximum deceleration of vehicle ahead will lead to decreased road capacity. However, the drivers' overestimation of their own vehicle maximum deceleration will increase the capacity of the road, but is likely to cause unsafe driving behaviors and increase accident rate. Copyright ©2015 by Science Press.


Qiu X.-P.,Southwest Jiaotong University | Qiu X.-P.,Comprehensive Transportation Key Laboratory of Sichuan Province | Sun R.-X.,Southwest Jiaotong University | Ma L.-N.,Southwest Jiaotong University | Yang D.,Southwest Jiaotong University
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2016

The capacity of urban signalized intersection will decrease and traffic flow will run chaotically because of a work zone sets in the intersection. In order to study urban signal intersection work zone traffic flow at a micro level, it puts forward a new applicable social force model of intersection traffic flow for the first time, based on initial social force model. Factors affecting the capacity of island work zone are analyzed. A data collection program is designed to obtain data of work zone intersection. The measured data and genetic algorithm (GA) are used to do the presented model calibration, and then it is evaluated with several statistical indicators. The results show that the average absolute relative error of simulation traffic data and real data is 0.028, so it can make a reference to analyze the traffic flow of urban signal intersection work zone. Copyright © 2016 by Science Press.

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