Navigability assessment of large-scale redesigns in nine public transport networks: Open timetable data approach
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
Redesigns of public transport networks are complex undertakings requiring a careful balancing of several conflicting perspectives (e.g., user requirements vs system performance) and aims (e.g., increasing spatial coverage, increasing frequency). Current assessment tools omit an explicit focus on navigability, often identified as a key aspect of the user perspective. For understanding the multidimensional perspective of navigability, this research introduces an assessment framework with both system and journey-level measures. The system-level measures provide an overview of redesigns based on static network representations. The journey-level measures are based on journey trajectories generated with a customized routing algorithm, assessing the distributive effects of the redesign. The framework is applied to public transport networks from nine cities with recently implemented redesigns, namely Amsterdam, Auckland, Austin, Baltimore, Columbus, Helsinki, Houston, Indianapolis, and Wellington. Results indicate that the redesigns have improved navigability both from a system-level and user perspective in general. However, in some cases, improvements in navigability come at the cost of increased travel time and number of transfers. Furthermore, the results suggest that the redesigns have differing emphasis within the regions, for different times of day, and for different aspects of network structure. The results are discussed both from the perspective of the case findings and for drawing more general planning and policy recommendations. Finally, this research provides a basis for further transdisciplinary approaches, encouraging connections between transport modeling and complex networks approaches.
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