Transportist: December 2018

Welcome to the December 2018 issue of The Transportist, especially to our new readers. As always you can follow along at the  blog or on Twitter



We launched this month. It’s a local organisation aimed at promoting walking. I have put up several posts on the site (others have as well). While the details are Sydney-based, the logic is sadly universal. If you want to make Sydney a better place to walk (scoot, stride, perambulate, and so on), you should join. We are bike-friendly, unlike some other Australian pedestrian ‘advocacy’ groups. You can follow on Twitter, Facebook, and Instagram as well.





Automated, Autonomous, Driverless, and Self-Driving Vehicles, and Semi-Autonomous Systems 

Electric Vehicles [and Renewable Energy]

Human-Driven Vehicles, Signs, Signals, Sensors, and Markings, and Roads


Shared Vehicles/Ride-sharing/Ride-hailing/Taxis/Car Sharing


Human-Powered Vehicles/Bikes/Pedestrians/Scooters/eBikes/Last-Mile/First-Mile/etc

Technology History

Intercity Trains

Aviation and Space

Maritime and Ferries

Research & Data

Papers by Us

  • Jie HuangDavid LevinsonJiaoe WangJiangping Zhou, and Zi-jia Wang (2018) Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences  (Open Access)
Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants.


by Others