28,219 research outputs found
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.Comment: Under revision in Computer Communication Journa
A Personalized Trip Planner For Vulnerable Road Users
This research presents an adaptive and personalized routing model that enables individuals with disabilities to save their route preferences to a mobility assistant platform. The proactive approach based on anticipated user need accommodates vulnerable road users’ personalized optimum dynamic routing rather than a reactive approach passively awaiting input. Most of the currently available trip planners target the general public’s use of simpler route options prioritized based on static road characteristics. These static normative approaches are only satisfactory when conditions of intermediate intersections in the network are consistent, a constant rate of change occurs per each change of the segment condition, and the same fixed routes are valid every day regardless of the user preference. In this study, we model the vulnerable road user mobility problem by accommodating personalized preferences changing by time, sidewalk segment traversability, and the interaction between sidewalk factors and weather conditions for each segment contributing to a path choice. The developed reinforcement learning solution presents a lower average cost of personalized, accessible, and optimal path choices in various trip scenarios and superior to traditional shortest path algorithms (e.g., Dijkstra) with static and dynamic extensions
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