Modeling Large-Scale Walking and Cycling Networks: A Machine Learning Approach Using Mobile Phone and Crowdsourced Data
Walking and cycling are known to bring substantial health, environmental, and economic advantages. However, the development of evidence-based active transportation planning and policies has been impeded by significant data limitations, such as biases in crowdsourced data and representativeness issues of mobile phone data. In this study, we develop and apply a machine learning based modeling approach for estimating daily walking and cycling volumes across a large-scale regional network in New South Wales, Australia that includes 188,999 walking links and 114,885 cycling links. The modeling methodology leverages crowdsourced and mobile phone data as well as a range of other datasets on population, land use, topography, climate, etc. The study discusses the unique challenges and limitations related to all three aspects of model training, testing, and inference given the large geographical extent of the modeled networks and relative scarcity of observed walking and cycling count data. The study also proposes a new technique to identify model estimate outliers and to mitigate their impact. Overall, the study provides a valuable resource for transportation modelers, policymakers and urban planners seeking to enhance active transportation infrastructure planning and policies with advanced emerging data-driven modeling methodologies.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Dynamics, stability, and energy efficiency of an energy-recycling rimless wheel with spring-clutch legs
This paper proposes an energy-recycling rimless wheel with spring-clutch legs. The proposed mechanism uses a lockable clutch to store part of the impact-induced elastic energy after foot contact and reinject it in the ne…
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Obtaining data on active travel activities such as walking, jogging, and cycling is important for refining sustainable transportation systems (STS). Effectively monitoring these activities not only requires sensing solut…
Federated LearningMeta-LearningPrivacy PreservingSTSActivity-based and agent-based Transport model of Melbourne (AToM): an open multi-modal transport simulation model for Greater Melbourne
Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation …
Upcycling Large Language Models into Mixture of Experts
Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However, optimal techniques for upcycling at scal…
Mixture-of-ExpertsMMLUComputational Graph Approach for Detection of Composite Human Activities
Existing work in human activity detection classifies physical activities using a single fixed-length subset of a sensor signal. However, temporally consecutive subsets of a sensor signal are not utilized. This is not opt…
Action DetectionActivity Detection