Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …

Sensing cost and data quality are two primary concerns in mobile crowd sensing. In this
article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which …

Abstract Intelligent Transportation Systems (ITS) face the formidable challenge of large-
scale missing data, particularly in the imputation of traffic data. Existing studies have mainly …

Sparsity and missing data problems are very common in spatiotemporal traffic data collected
from various sensing systems. Making accurate imputation is critical to many applications in …

This paper instantly infers the gas consumption and pollution emission of vehicles traveling
on a city's road network in a current time slot, using GPS trajectories from a sample of …

In traditional mobile crowdsensing applications, organizers need participants' precise
locations for optimal task allocation, eg, minimizing selected workers' travel distance to task …

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and
infer urban-scale sensing data. However, participants risk their location privacy when …

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing
applications. In this paper, we leverage the spatial and temporal correlation among the data …

Traffic volume estimation at the city scale is an important problem useful to many
transportation operations and urban applications. This paper proposes a hybrid framework …

Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the
smartphones with various embedded sensors and user's mobility to sense diverse …