Because existing passenger-finding algorithms do not consider taxi's spatio-temporal context, a collaborative filtering recommendation algorithm of taxi passenger-finding based on spatio-temporal context was proposed. The proposed algorithm mapped potential passenger locations to space network, and introduced time delay factor to similarity measure to get the neighbor set which was similar to a target taxi's driving behavior. Based on location context, the proposed algorithm chose the target taxi's most interest potential passenger location from similar neighbor set. The experimental results on Fuzhou taxi trajectory data show that the proposed algorithm can get the best recommendation result when the time delay factor is 0.7. Meanwhile, compared to the traditional collaborative filtering recommendation algorithms, the proposed algorithm obtains better recommendation result under the neighbor sets with different size, which means the proposed algorithm is more accurate than the traditional collaborative filtering algorithms.

The identification of one-way road state can provide relevant information of road network to the public timely and accurately, improve the efficiency of public travel, and enhance the service level of dynamic traffic information. This paper presented a dynamic identification algorithm of one-way road state based on Floating Car Data (FCD). Firstly the line feature information of maps was got, and the matching of spatial information grid with the traffic roads was pretreated to achieve fast matching for massive FCD; Then statistical characteristics of FCD direction information was analyzed to filter dual-threshold information and direction information; Finally one-way road state information was got dynamically. The actual road network tests show the algorithm can identify one-way road state information effectively.

In order to achieve dynamic update of digital map data to support the geographic information services in traffic network with rapid development, a new-road automatic detection algorithm was proposed based on the Floating Car Data (FCD) technology. In this method, the moving trajectories of massive floating cars were calculated in real-time, then the suspected new road sets were extracted with the image matching between the existing map layers and the trajectories. After applying a filtering algorithm to the data sets for cleaning, the new road detection reports covering the new roads' location and length were generated automatically and saved as temporary map layers. The field test results show that this algorithm can detect the new roads quickly, so far as to detect new road within five minutes. It is a cost-effective solution for the real-time road map layer update.

With the demand of real-time and dynamic scalability processing for multi-source mass data in transportation, this paper proposed a distributed in-memory database experimental platform based on GemFire. The platform used the attributes of GemFire, such as key-value data storage structure and distributed dynamic membership. The actual data from floating car system was used to complete the performance analysis in cloud computing architecture. The experimental results show that the platform can shorten the calculation time of mass data to less than 10% of the existing system and basically satisfy the application requirements of transport data resources integration in cloud computing platform.