Taxi drivers drive on the roads every day and become very knowledgeable of the spatiotemporal traffic patterns in a city.It therefore is reasonable to assume that the routes chosen by taxi drivers often work out better than those selected by other drivers.Since dynamic navigation assistance based on real-time traffic information faces limitations such as the spatial coverage of real-time data collection sites,performance of real-time data processing and communications,and accuracy of short-term traffic forecasts in a large urban area,experiences gained by taxi drivers can be a valuable data source for improving the quality of vehicle navigation guidance.This paper develops a vehicle navigation guidance system based on taxis drivers’ knowledge derived from floating car data collected over an extended time period.We then classify road segments based on the spatiotemporal characteristics of taxi tracking data.A case study using taxi tracking data collected in Wuhan,China is presented in this paper to demonstrate the performance of this vehicle navigation system based on taxi tracking data.
This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-bone intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co. , Ltd. and has been successfully put into use. The latter is an ongoing resealch.
