港口散粮定量装车工艺简介

介绍港口散粮装火车工艺、装车楼结构形式。

Bus transit travel time reliability evaluation based on automatic vehicle location data

In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed and evaluated based on automatic vehicle location （AVL） data. Based on the statistical analysis of the bus transit travel time, six indices including the coefficient of variance, the width of travel time distribution, the mean commercial speed, the congestion frequency, the planning time index and the buffer time index are proposed. Moreover, a framework for evaluating bus transit travel time reliability is constructed. Finally, a case study on a certain bus route in Suzhou is conducted. Results show that the proposed evaluation index system is simple and intuitive, and it can effectively reflect the efficiency and stability of bus operations. And a distinguishing feature of bus transit travel time reliability is the temporal pattern. It varies across different time periods.

Vehicle Real-time Location Based on Visual Perception Model

Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.

Real-time vehicle tracking for traffic monitoring systems

A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.

Application of interacting multiple model in integrated positioning system of vehicle

To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.

Vehicle Tracking in the Process of Transport of Dangerous Goods GPS/GSM Technology

In the paper it has reviewed the monitoring of vehicles in the transport of hazardous substances based on the using of GPS/GSM technology. The basic principles of the GPS concept and hardware have been exhibited which are implemented in locating the vehicle. They have also identified the locating techniques underlying the monitoring process. It has presented an active system for monitoring parameters of the means of transport.

Vehicle Speed Observation Models Based on the Data on the Smartphone GPS

The development of GPS （global positioning system） receiver now can be integrated on a smartphone. GPS receiver on smartphones has been developed for location-based applications. Smartphones are very suitable to be used as an experimental tool, because smartphones are usually equipped with various types of sensors. This paper proposes a model observation vehicle speed on a road section based on the GPS data on the smartphone. Observations made by calculating the speed of the speed of vehicles moving through the data transfer at the GPS location of the smartphone, the data are then sent periodically to the server and server processing and storage of vehicle speed data. After tested with test reliability indicators use RMSE, observations with model observations speed, speed based on GPS data on a smartphone are relevant when compared with the speed directly from the vehicle＇s speedometer with the difference between the value of the difference of speed that is 3.1785 km/h.

Measurement of Emissions from Passenger-Picking-up Vehicles at Airport Terminals

Rather than parking at nearby hourly parking lots, many passenger-picking-up vehicles prefer to idle at terminals and/or drive cycling around terminal facilities. As a result, extra vehicle emissions may be produced in an airport area. Even though there are limited studies on such emissions at airports, these estimations were normally based on the date emission models, which might cause bias in emission estimations. This paper proposes an approach to employ the floating car method and Global Positioning System （GPS） to record speeds and acceleration rates of idling and cycling vehicles at airport terminals. The tests were conducted under different time periods and traffic demands with different waiting time. The speeds and acceleration rates are synthesized to yield Vehicle Specific Power＇s （VSP） and Operational Mode （OM） distributions. Utilizing the Environmental Protection Agency （EPA） emission estimation model Motor Vehicle Emission Simulator （MOVES）, pollutants and green house gas emission indexes （e.g. NOx, CO, CO2 and HC） and fuel consumptions can be easily estimated. As an illustration of the proposed approach, the research team collected GPS data at a terminal in Houston William Hobby Airport （HOU）, and calculated the VSP distributions and OM distributions. Emissions of passenger-picking-up vehicles around these congested airport terminals.

The Application and Research on μC/OS operating system in GPS

The article design a vehicle monitoring terminal system based on μ C/OS and introduces the hardware system design of STM32F407 as the control core. On the basis of transplant μ C/OS operating system, the system software architecture, application-layer multi- task and the priority has been designed, focuses on the use GPRS tasks, preparation methods GPS tasks. After experimental tests, the system is stable and reliable, the realization of real-time vehicle monitoring. System design methodology for embedded system application development has a certain rcfcrence value.

Intelligent GPS-Less Speed Detection and Clustering in VANET

Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.