Detailed spatial and temporal data creates an opportunity to measure and relate geometric design attributes of the bus system with performance. Emerging data sources provide network-wide, continuous bus performance assessment based on real-time data. Likewise, detailed design elements such as bus lane existence, stop placement and even curb cuts can be quantified.
This PhD project will analyse archived General Transit Feed Specification (GTFS) data using Artemis high-performance computing, which records up-to-minute past bus performance information, including travel time and on-time running statistics. Geometric design data will be identified from Transport for NSW’s (TfNSW) open data and satellite imagery data, and the relationship between design and performance will be modelled.
The outcome will identify significant inefficient road designs and exemplary designs for optimising performance, which aims to benefit optimising the bus network performance.
With the comprehensive bus performance assessment, this study will potentially benefit in introducing autonomous buses by improving priority lane designs, reducing conflict between human drivers and autonomous vehicles.
With bus infrastructure design case studies and simulations integrated with bus performance assessment, this PhD project aims to provide general policy and design recommendations that aim to enhance public transport users experience globally.
For decades, public transit organisations have been under pressure to meet efficiency objectives (Strathman and Hopper 1993). Historically, efficiency indicators were more focused on reflecting the overall cost efficiency, labour utilisation and vehicle utilisation (Fielding, Babitsky, and Brenner 1985).
More recently, effectiveness indicators are popular as they reflect transit operations’ ability to meet certain goals, including service utilisation, service quality, and accessibility of service. Traditionally, bus performance was measured based on surveys, which has limited coverage of bus routes and operation times.
In 2006, Google created the General Transit Feed Specification (GTFS), which is now industry standard. GTFS feed contains transit service data, including stops, routes, trips, and other schedule datasets as well as real-time updates and vehicle positions (Google Developers 2020). With real-time updates and large time and area coverage, the bus micro delays can be analysed for an extended period and area.
With the development of TfNSW’s GTFS-real-time feeds API, the GTFS data is available for buses with GPS installed in the Greater Sydney Area since late 2016. This study will use archived GTFS data to assess the bus performance by comparing the bus performance information, including travel time and on-time running statistics to infrastructure design.
The Greater Sydney Area bus performance assessment aims to provide comprehensive analysis of the bus infrastructure elements that contribute to on-time performance. Casual observation indicates that the road design can significantly impact bus performance, including bus travel speed and reliability.
This study will quantify the design elements such as bus lanes and stop locations that affect bus performance using TfNSW’s clearway and satellite imagery data. This study will relate the bus performance data with the quantified design elements data on each bus trip to find the relationship between performance and link attributes. The relationship found will be used to improve bus performance as well as provide bus infrastructure design recommendations for the Greater Sydney Area and across Australia.
Fielding, Gordon J., Timlynn T. Babitsky, and Mary E. Brenner. 1985. Performance Evaluation for Bus Transit. Transportation Research Part A: General 19 (1): 73–82.
Google Developers. 2020. GTFS Static Overview | Static Transit. Google Developers. November 2, 2020.
Strathman, James G., and Janet R. Hopper. 1993. Empirical Analysis of Bus Transit On-Time Performance. Transportation Research Part A: Policy and Practice 27 (2): 93–100.
- Measure stop-to-stop level bus performance including travel time and reliability metrics.
- Quantify the design of the bus infrastructure at a fine spatial and, where relevant, temporal resolution.
- Describe relationships between performance and the links attributes in order to recommend design alternatives.
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