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Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

People leave traces of movements that might affect the behavior of others both online in cyberspace and offline in real space. Previous studies, however, have used only questionnaires, network data, or GPS data to study spatio-temporal behaviors, ignoring the relationship between online and offline activities, and overlooking the influence of previous activities on future behaviors. We propose a Pheromone-based Fusion Model, viewing human behaviors as similar to insect foraging behaviors to model spatio-temporal recreational activity patterns, on and offline. In our model, website data were combined with GPS data to evaluate the attractiveness of destinations over time using twenty-nine landscapes in Beijing, China; big website data and GPS trajectories were gathered from 181 users for 57 months. The datasets were portioned into two periods. Online and offline recreational pheromones were calculated from the first period and the visitation rates were extracted from the second period. These data were subsequently applied in a regression analysis to determine unknown parameters and estimate the attractiveness of destinations. The proposed method was compared with two other approaches that use either GPS data or online data alone, in order to verify effectiveness. The results show that the proposed method can estimate future behaviors, based on real world and online past actions. [ABSTRACT FROM AUTHOR]

The unbalanced distribution of taxi passengers in space and time affects taxi driver performance. Existing research has studied taxi driver performance by analyzing taxi driver strategies when the taxi is occupied. However, searching for passengers when vacant is costly for drivers, and it limits operational efficiency and income. Few researchers have taken the costs during vacant status into consideration when evaluating taxi driver performance. In this paper, we quantify taxi driver performance using the taxi's average efficiency. We propose the concept of a high-efficiency single taxi trip and then develop a quantification and evaluation model for taxi driver performance based on single trip efficiency. In a case study, we first divide taxi drivers into top drivers and ordinary drivers, according to their performance as calculated from their GPS traces over a week, and analyze the space-time distribution and operating patterns of the top drivers. Then, we compare the space-time distribution of top drivers to ordinary drivers. The results show that top drivers usually operate far away from downtown areas, and the distribution of top driver operations is highly correlated with traffic conditions. We compare the proposed performance-based method with three other approaches to taxi operation evaluation. The results demonstrate the accuracy and feasibility of the proposed method in evaluating taxi driver performance and ranking taxi drivers. This paper could provide empirical insights for improving taxi driver performance. [ABSTRACT FROM AUTHOR]

With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis inWuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane. [ABSTRACT FROM AUTHOR]