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.

In the prediction of moving object trajectory, concerning the low accuracy rate of low order Markov model and the expansion of state space in high order model, a dynamic adaptive Probabilistic Suffix Tree (PST) prediction method based on variable length Markov model was proposed. Firstly, moving objects trajectory path was serialized according to the time; then the probability characteristic of sequence context was trained and calculated from the historical trajectory data of moving objects, the probabilistic suffix tree model based path sequence was constructed, combined with the actual trajectory data, thus the future trajectory information could be predicted dynamically and adaptively. The experimental results show that the highest prediction accuracy was obtained in second order model, with the order of the model increasing, the prediction accuracy was maintained at about 82% and better prediction results were achieved. In the meantime, space complexity was decreased exponentially and storage space was reduced greatly. The proposed method made full use of historical data and current trajectory information to predict the future trajectory, and provided a more flexible and efficient location-based services.

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.