When I arrived in Australia to start my PhD in April 2014, I became obsessed with the goCard data since it includes both touch-on and touch-off information, despite many other transit systems. Despite my PhD was about freight transportation, I spent a fair amount of time exploring what can I do with this amazing database. So the followings are a number of research that was produced as a result of this interest.

Transfer Estimation with No Transit Fare Data

The transfer rate is considered a metric for the evaluation of the efficiency of current or proposed transit networks. Assessing transfers is very important in the redesign process, in which it is necessary to have a simple method to calculate the transfer rate in the modelling process. With this transfer information, an unreasonably high transfer rate may require the transit system to consider redesign of the network.

The detailed transit data, however, does not exist in many cities. Hence, it is important to develop a simple model and validate it with a case study like goCard data. So, in this research, I proposed a simple method for transfer calculation and validated the results of the proposed transfer model with real transfer data obtained from Translink, the transit authority in south east Queensland (SEQ).

The most time-consuming task was building a detailed street network from Open Street Map that consisted pathways and walkways. Well, one may argue that you just had to download Open Street Map, but at the time it was impossible to download the whole network of South East Queensland in one spot, and I had to apply several steps to make a connected and integrated to be ready for assignment and shortest path analysis. Ultimately, my polished network included about 340,000 links and 250,000 nodes.

My proposed method answers to two following questions without a need for transit fare data:

• How many transfers are necessary for every person to travel from any location to another location in the city?

• how many trips are made with how many transfers in the transit network?

This method can assist with transit network design, but without the need for detailed data. Please refer to this publications for further details:

• Irannezhad, Elnaz; Hickman, Mark; Mousavi, Seyed Hossein (2015); New approach to calculation of a transfer matrix in a transit network; The 37^th Australian Transport Research Forum; 30 September – 2 October 2015; Sydney, Australia.

Download Full Text, Download Poster Slide

Accessessibility and Transit Stop Choice Study

My detailed network came of use in the other research undertaken by Dr Neema Nassir, who was researching on accessibility and public transport stop choice at the time. I applied my GIS and spatial analysis skill to supply the data required for developing the access top choice model and also to measure the accessibility across South East Queensland transit network.

For further details, please refer to the following publications:

• Nassir, Neema; Hickman, Mark; Malekzadeh, Ali; Irannezhad, Elnaz (2016), A utility–based travel impedance measure for public transit network accessibility. Transportation Research Part A: Policy and Practice, 88, pp. 26–39. Download Full Text

• Nassir, Neema; Hickman, Mark; Malekzadeh, Ali; Irannezhad, Elnaz (2015), Modeling transit passenger choices of access stop. Transportation Research Record, 2493(2493), pp.70–77. Download Full Text

This research was also presented by Neema in an international conference as:

• Nassir, Neema, Hickman, Mark, Malekzadeh, Ali, and Irannezhad, Elnaz (2015); Modeling transit access stop choices. In: The Transportation Research Board 94^th Annual Meeting. Transportation Research Board Annual Meeting; 11–16 January 2015; Washington D.C. USA.