Abstract
Simulation-based dynamic traffic assignment models are increasingly used in urban transportation systems analysis and planning. They replicate traffic dynamics across transportation networks by capturing the complex interactions between travel demand and supply. However, their applications particularly for large-scale networks have been hindered by the challenges associated with the collection, parsing, development, and sharing of data-intensive inputs. In this paper, we develop and share an open dataset for reproduction of a dynamic multi-modal transportation network model of Melbourne, Australia. The dataset is developed consistently with the General Modeling Network Specification (GMNS), enabling software-agnostic human and machine readability. GMNS is a standard readable format for sharing routable transportation network data that is designed to be used in multimodal static and dynamic transportation operations and planning models.
Highlights
Two important extensions were made: (i) travel times may change due to varying traffic conditions; and (ii) travel times on used routes should be equal for the same departure time interval [2,3,4]
A growing number of studies have unraveled the potential benefits of big data in understanding and modeling of urban transportation networks, including use of mobile phone data [11,12] and, more generally, floating car data [13,14]
Applications of simulation-based dynamic traffic assignment (DTA) models have been growing in urban transportation systems operations and planning, but development of such models requires a large number of data inputs that often make their real-world applications a practical challenge
Summary
The emergence of dynamic traffic assignment (DTA) since the late 1970s [1] is largely due to the fact that traffic networks are generally not in a steady state, as depicted by static traffic assignment. Applications of simulation-based DTA models have been growing in urban transportation systems operations and planning, but development of such models requires a large number of data inputs that often make their real-world applications a practical challenge. These inputs generally fall into two categories of demand and supply sides [2]. The former typically include time-dependent origin-destination (TDOD) matrices and traveler behavior models’ parameters, while the latter consist of network geometry, traffic control information, traffic flow parameters, and others. We develop and share an open GMNS dataset for reproduction of a dynamic multi-modal transportation network model of Melbourne, Australia (see [19] for model deployment details)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
