A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

“…Traditionally, methods that focus on designing new routes rely on origin-destination matrices to evaluate their expected efficacy [32,72]. In addition, various approaches such as transfer learning or other machine learning methods also have been introduced to deal with similar tasks [40,71,74]; among them, a state-of-the-art deep-neural-network-based PF inference module [51,53] that relies on data engineering with a route-affecting region (RAR) is proposed. The work [51,53] proposes a PF estimation model that emphasizes integrating the feature information of the route's influential region and combining the city's heterogeneous data, including but not limited to the point of interests, human mobility, and competitive and transferable relationship with existing routes.…”

“…In addition, various approaches such as transfer learning or other machine learning methods also have been introduced to deal with similar tasks [40,71,74]; among them, a state-of-the-art deep-neural-network-based PF inference module [51,53] that relies on data engineering with a route-affecting region (RAR) is proposed. The work [51,53] proposes a PF estimation model that emphasizes integrating the feature information of the route's influential region and combining the city's heterogeneous data, including but not limited to the point of interests, human mobility, and competitive and transferable relationship with existing routes. Thereafter, this neural-network-based inference module, with a grid-like graph that stores urban features and road network connectivity, is modified and adopted for evaluating the expected performance (PF) of arbitrary circular routes and constructing the graph needed in subsequent route planning.…”

“…In other words, one requires to re-organize and re-calculate so as to retain the exact effect on PF. In this work, the method to quantify the spatial relationship among routes and to evaluate the PF is based on a route-based inference module [51,53]. Admittedly, adopting an inference model will cause some error in the final PF; however, this adaptation and formulation do meet our targeted scenario in the real world since the current procedure relying on simulation also faces the uncertainty in PF.…”

“…As one of the key factors that affect the quality of life [66], traffic deployment dealing with spatio-temporal cyclical traffic flow between satellite cities or inside the city center turns out to be crucial [57]. Thereafter, approaches such as inference for effectiveness/efficiency of either deployed or arbitrary routes [53] and constrained route planning with multiple criteria [5] have been addressed over the past decades. Moreover, due to the rise of deep learning, hybrid systems of computational intelligence synergizing learning-based inference modules and route planning strategies have thrived in recent years [34,41].…”

“…In particular, as illustrated in Figure 1, the framework is composed of preprocessing and computational phases. First, based on a deep-neural-network-based PF inference module built on urban data [51,53], the PF of arbitrary routes can be accurately inferred. Next, in the second phase, stations are first recommended based on the Gaussian-Poisson Mixture Model with negative feedback, which is a modification that models the spatial attractiveness of PF for deployed stations and thereafter puts both PF of a single station and its herding effect into consideration.…”