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This study simulates charging infrastructure needs using a large-scale agent-based simulation of Sweden with detailed individual characteristics, including dwelling types and activity patterns.

This study presents a model of synthesising individual mobility data from very sparse movement traces.

This study explores the competition between ride-sourcing and PT through the lens of big data analysis.

Crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016.

This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size.

A data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities at high spatial and temporal resolutions.

The heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement using Twitter user timelines.

Detect traffic participants using cost-effective arrayed ultrasonic sensors.

Data from two simulator studies with 50 participants in total, where the visual vs. the auditory modality was used to present the same type of advisory traffic information under the same driving scenarios.

Driver response patterns, assessed by driving performance and eye fixations, to mental workload increases, as measured by the detection response task.