Abstract
Modeling sustainable mobility is a challenging task among the researchers, planners and evaluators. Indicator-based approaches are, however, meritorious, owing to the communicative benefits among the diverse interdisciplinary group of people such as researchers, planners, evaluators, and stakeholders. The main aim of the paper is to develop a sustainable mobility model using a combination of statistical and machine learning tools. A set of sustainable mobility indicators, categorized into three dimensions of sustainability i.e. Environmental, Social, and Economic are developed. Then, the indicators are normalized, weighted and aggregated by Factor Analysis. The sub-indices corresponding to the three dimensions are then aggregated using Fuzzy Logic. This model was applied to 16 Indian states and 1 UT, which were shortlisted based on the data availability. Based on the computed Fuzzy Sustainable Mobility Index (IFSM), these states and UT were ranked. Tamil Nadu, Telangana, and Andhra Pradesh were the best performing states with computed IFSM of 0.727, 0.726, and 0.726 respectively. Whereas, Odisha, Uttarakhand, and Haryana with computed IFSM of 0.578, 0.555, and 0.513 respectively were the least performing states.
| Original language | English |
|---|---|
| Title of host publication | Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies |
| Publisher | Springer Singapore |
| Pages | 107–114 |
| Volume | 643 |
| Edition | 1 |
| ISBN (Electronic) | 978-981-15-3125-5 |
| DOIs | |
| Publication status | Published - 29 Apr 2020 |