The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews.

Car re-identification, searching a specific car object from a large-scale car image database, is investigated in this paper. Previous work mainly focuses on fixed pose and overlooks the special appearance. However, avoiding matching other poses would lead to coarse results of the car retrieval. And some special attributes like individual paintings which are greatly helpful for car retrieval have not drawn enough attention. This paper addresses these problems through multi-poses matching and re-ranking based on special attributes. Our core idea lies in query expansion method that can capture weighted attributes to build the retrieval model, which allows us to estimate invisible attributes by the visible ones to construct complete attributes vectors to car retrieval in any poses. Furthermore, we divide all attributes into two groups, special attributes and common attributes. Here special attributes represent the abnormal appearance like individual paintings or car damage while common attributes denote the intrinsic appearance of car. Using special attributes to re-rank results turns out to be beneficial to improve the retrieval performance. In the end, the experiments demonstrate the effectiveness of our approach on the car datasets.