Simulation model of speed–density characteristics for mixed bicycle flow—Comparison between cellular automata model and gas dynamics model

AbstractThe mixed bicycle flow refers to the bicycle flow containing electric bicycles. The traffic characteristics data of the mixed bicycle flow was collected by the virtual coil method in Nanjing and Ningbo, China. And the speed–density characteristics of the mixed bicycle flow with different proportions of electric bicycles were obtained. The results show that the overall speed of the mixed bicycle flow containing electric bicycles is higher than that of pure bicycle flow when the density is relatively low. The speed decreases when the density is higher than 0.08 bic/m2; the speed–density characteristics of the bicycles and the electric bicycles tend to be the same when the density is higher than 0.25 bic/m2. And when the density reaches 0.58 bic/m2, the mixed bicycle flow becomes blocked and the speed is zero. The cellular automata model and gas dynamics model were also adopted to simulate the speed–density characteristics of the mixed bicycle flow. The simulation results of the cellular automata model are effectively consistent with the actual survey data when the density is lower than 0.225 bic/m2; the simulation results of the gas dynamics model are effectively consistent with the actual survey data when the density is higher than 0.300 bic/m2; but both of the two types of simulation models are inapplicable when the density is between 0.225 and 0.300 bic/m2. These results will be used in the management of mixed bicycles and the research of vehicle–bicycle conflict and so on

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers’ decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers’ complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers’ decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers’ behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on. Document type: Articl

Interfacial Activation of Catalytically Inert Au (6.7 nm)-Fe3O4 Dumbbell Nanoparticles for CO Oxidation

通讯作者地址: Zheng, NF (通讯作者), Xiamen Univ, Coll Chem & Chem Engn, State Key Lab Phys Chem Solid Surfaces, Xiamen 361005, Peoples R China 地址: 1. Xiamen Univ, Coll Chem & Chem Engn, State Key Lab Phys Chem Solid Surfaces, Xiamen 361005, Peoples R China 2. Xiamen Univ, Coll Chem & Chem Engn, Dept Chem, Xiamen 361005, Peoples R China 电子邮件地址: [email protected] nanoparticles epitaxially grown on Fe3O4 in Au (6.7 nm)-Fe3O4 dumbbell nanoparticles exhibit excellent stability against sintering, but display negligible catalytic activity in CO oxidation. Starting from various supported Au (6.7 nm)-Fe3O4 catalysts prepared by the colloidal deposition method, we have unambiguously identified the significance of the Au-TiO2 interface in CO oxidation, without any possible size effect of Au. In situ thermal decomposition of TiO2 precursors on Au-Fe3O4 was found to be an effective way to increase the Au-TiO2 interface and thereby optimize the catalytic performance of TiO2-supported Au-Fe3O4 dumbbell nanoparticles. By reducing the size of Fe3O4 from 15.2 to 4.9 nm, the Au-TiO2 contact was further increased so that the resulting TiO2-supported Au (6.7 nm)-Fe3O4 (4.9 nm) dumbbell particles become highly efficient catalysts for CO oxidation at room temperature.National Natural Science Foundation of China 20871100 20721001 Distinguished Young Investigator Grant 20925103 Research Fund for the Doctoral Program of Higher Education of China 200803841010 Natural Science Foundation of Fujian 2009J06005 Key Scientific Project of Fujian Province 2009HZ0002-

PMMA/PDMS valves and pumps for disposable microfluidics

Poly(methyl methacrylate) (PMMA) is gaining in popularity in microfluidic devices because of its low cost, excellent optical transparency, attractive mechanical/chemical properties, and simple fabrication procedures. It has been used to fabricate micromixers, PCR reactors, CE and many other microdevices. Here we present the design, fabrication, characterization and application of pneumatic microvalves and micropumps based on PMMA. Valves and pumps are fabricated by sandwiching a PDMS membrane between PMMA fluidic channel and manifold wafers. Valve closing or opening can be controlled by adjusting the pressure in a displacement chamber on the pneumatic layer via a computer regulated solenoid. The valve provides up to 15.4 mu L s(-1) at 60 kPa fluid pressure and seals reliably against forward fluid pressure as high as 60 kPa. A PMMA diaphragm pump can be assembled by simply connecting three valves in series. By varying valve volume or opening time, pumping rates ranging from nL to mL per second can be accurately achieved. The PMMA based valves and pumps were further tested in a disposable automatic nucleic acid extraction microchip to extract DNA from human whole blood. The DNA extraction efficiency was about 25% and the 260 nm/280 nm UV absorption ratio for extracted DNA was 1.72. Because of its advantages of inexpensive, facile fabrication, robust and easy integration, the PMMA valve and pump will find their wide application for fluidic manipulation in portable and disposable microfluidic devices.NSFC [20805038, 20620130427]; MOE [200803841013]; 973 Program of China [2007CB935603, 2010CB732402]; XMU ; NIH [P01 CA077664

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers’ decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers’ complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers’ decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers’ behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on

Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

Short-term prediction of on-street parking occupancy is essential to the ITS system, which can guide drivers in finding vacant parking spaces. And the spatial dependencies and exogenous dependencies need to be considered simultaneously, which makes short-term prediction of on-street parking occupancy challenging. Therefore, this paper proposes a deep learning model for predicting block-level parking occupancy. First, the importance of multiple points of interest (POI) in different buffers is sorted by Boruta, used for feature selection. The results show that different types of POI data should consider different buffer radii. Then based on the real on-street parking data, long short-term memory (LSTM) that can address the time dependencies is applied to predict the parking occupancy. The results demonstrate that LSTM considering POI data after Boruta selection (LSTM (+BORUTA)) outperforms other baseline methods, including LSTM, with an average testing MAPE of 11.78%. The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance, which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM. When there are more restaurants on both sides of the street, the prediction performance of LSTM (+BORUTA) is significantly better than that of LSTM

Modeling the Distribution Characteristics of Urban Public Bicycle Rental Duration

In order to model the distribution characteristics of public bicycle rental durations, individual journey data for three cities in China (Ningbo, Hangzhou, and Beijing), for weekdays, was obtained. The distribution curves for public bicycle rental duration in the three cities were found to be extremely similar, with small differences among the weekdays. The basic parameters such as the average rental duration, the rental duration corresponding to the maximum rental frequency, and the rental duration corresponding to 75% degree were then calculated. On this basis, the radioactive decay law from physics was used to establish a theoretical model for the relationship between rental frequency and rental duration. The data on public bicycle rental duration in Ningbo, Hangzhou, and Beijing were used to test the model and produce a corrected theoretical model. The results indicate that the relationship between rental frequency and rental duration obeys the decay law. The study results provide important theoretical support for the rental station planning of bicycle sharing systems, as well as the allocation, operation, and dispatch of public bicycles

Restriction Analysis of Transport Policy for Bridges Using the Trajectory Data

Roads are becoming increasingly congested with continuous rise in the number of vehicles. Restriction policies are selected to alleviate congestion in many cities. However, conclusions regarding the substantial effects of restriction policies have not been fully demonstrated. This study primarily aims to demonstrate whether traffic restrictions can control the driving habits of people to alleviate traffic pressure. Furthermore, this study investigates the effect on the traffic on the premise of a normalized restriction policy. Data were collected by bayonet systems in Ningbo. Results showed that vehicles restricted by the restriction policy only accounted for approximately 13%. Most drivers bypass restricted roads to avoid restrictions. The method proposed can effectively amend the trajectory deviation caused by the inaccuracy from the bayonet. Based on the results, some suggestions about the policy of restrictions were discussed

Map Matching for Fixed Sensor Data Based on Utility Theory

Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications