Bar graphs are routinely used in academic works, official reports, and mass media. Prior studies have focused on the comprehension of numerical information in bar graph design but have largely ignored the semantic information representation. Actually, along with the escalating need to convey semantic information beyond numerical data, unconventional bar graphs emerge and catch increasing eyes, highlighting the necessity of unlocking semantic information representation in bar graph design. In this paper, we attempt to address these gaps through examining the impact of three visual channels-color, shape, and orientation-on viewers' comprehension of semantic information. Drawing from prior research, we formulate a series of research hypotheses and conduct two experiments. Results show that by evoking sensorimotor experiences, conceptually relevant colors and shapes of bars facilitate the representation of semantic information. This facilitation is more pronounced in conveying concrete concepts than abstract concepts. Similarly, by evoking emotional experiences, colors and orientation aligned with the affective valence of concepts aid the representation of semantic information, with a more noticeable enhancement in conveying abstract concepts compared to concrete concepts. Additionally, we find that shape-embellished bars somewhat hinder the judgment of specific numerical values. These findings provide a renewed perspective on how semantic information is represented in bar graphs, offering valuable practical guidance for scientifically representing semantic information.

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.

Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks.