Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. MULTIMODAL EVALUATION CRITERIA <s> The Red Light Camera (RLC) program in Fairfax County, VA, had 13 cameras in operation in year 2003. This update study is conducted to evaluate the Fairfax County RLC program after 2 years of operation. The results of the violation analysis indicate that the RLC program did reduce the violation rate by up to 63% in the 22nd- to 27th-month period of its operation. The results also show that the increase of the intersection amber time combined with RLC produced a higher reduction of up to 72% in violation rate. The reduction in violation rate was found to be statistically significant. The accident rate was reduced by 27% after 2 years of RLC operation. However, the reduction in accidents was not found to be statistically significant at this point in time <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. MULTIMODAL EVALUATION CRITERIA <s> The itinerary planning problem in an urban public transport system constitutes a common routing and scheduling decision faced by travelers. The objective of this paper is to present a new formulation and an algorithm for solving the itinerary planning problem, i.e., determination of the itinerary that lexicographically optimizes a set of criteria (i.e., total travel time, number of transfers, and total walking and waiting time) while departing from the origin and arriving at the destination within specified time windows. Based on the proposed formulation, the itinerary planning problem is expressed as a shortest path problem in a multimodal time-schedule network with time windows and time-dependent travel times. A dynamic programming-based algorithm has been developed for the solution of the emerging problem. The special case of the problem involving a mandatory visit at an intermediate stop within a given time window is formulated as two nested itinerary planning problems which are solved by the aforementioned algorithm. The proposed algorithm has been integrated in a Web-based journey planning system, whereas its performance has been assessed by solving real-life itinerary planning problems defined on the Athens urban public transport network, providing fast and accurate solutions. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. MULTIMODAL EVALUATION CRITERIA <s> We introduce a framework for evaluating human detectors that considers the practical application of a detector on a full image using multisize sliding-window scanning. We produce detection error tradeoff (DET) curves relating the miss detection rate and the false-alarm rate computed by deploying the detector on cropped windows and whole images, using, in the latter, either image resize or feature resize. Plots for cascade classifiers are generated based on confidence scores instead of on variation of the number of layers. To assess a method's overall performance on a given test, we use the average log miss rate (ALMR) as an aggregate performance score. To analyze the significance of the obtained results, we conduct 10-fold cross-validation experiments. We applied our evaluation framework to two state-of-the-art cascade-based detectors on the standard INRIA person dataset and a local dataset of near-infrared images. We used our evaluation framework to study the differences between the two detectors on the two datasets with different evaluation methods. Our results show the utility of our framework. They also suggest that the descriptors used to represent features and the training window size are more important in predicting the detection performance than the nature of the imaging process, and that the choice between resizing images or features can have serious consequences. <s> BIB003
It is important to measure the effectiveness of any method using different evaluation criteria. The data in moving objects is highly dynamic and varies over time so a static metric may not provide the complete picture BIB002 . There are different algorithms that consider multimodal evaluation criteria and argue that a single or a certain group of criteria might not be effective for all cases BIB003 . Hussein et al. BIB003 evaluated different algorithms using detection error tradeoff curves. It was observed that the detection performance is not influenced due to different sensors however; the window size chosen for modeling the classifiers has significant impact on detection performance. In order to identify the problem in detail and propose a model for solution, we are required to establish a multimodal evaluation criterion considering the dynamic nature of data sources as well as the moving objects BIB001 . Multimodal evaluation criterion is not important for identifying the problem however, it helps in effective design of the system.