Public transportation route planning is crucial for both traffic management authority and residents. Current procedures for deciding new routes are time-consuming and ineffective due to the complicate simulation process or overwhelming numbers of opinions from stockholders. In this paper, we propose a novel decision supporting tool, Quantitative and Qualitative Itinerary Planner (QQIP), to help governments pre-evaluate new route services in the city in a timely manner. The function of QQIP is three-fold: visualization of urban informatics, a flexible interface for sketching designate routes, and passenger flows estimation in certain time intervals. With acquired relevant urban information, user can pre-estimate the effectiveness of designed routes using QQIP. To capture the spatial-temporal factors correlated with passenger flows, we propose route-affecting region (RAR) and adopt Deep Neural Network (DNN) framework to combine several dynamic and static features. According to our experimental results on bus-ticket data of Tainan city, the proposed RAR-based feature engineering methods are effective for handling and combining high-correlated dynamic and static data; meanwhile, QQIP can help decision makers infer the passenger flow effectively and efficiently for given designated routes.