Abstract
Comparative analysis of multimodal travel demand can help transport planners to improve the sustainability of a transport system. This research proposes a framework to qualitatively compare and analyze multimodal travel demand (of constrained and free transport modes) to identify opportunities and prioritize areas for improvement of constrained mode usage. The framework includes two methods. First method, CLAN is a Coarser Level ANalysis, comparing travel patterns and demand ratios of the two modes. Second method, FLAN is a Finer Level ANalysis based on density distributions of zones and OD pairs. The proposed framework is applied to the car (relatively free mode) and transit (constrained mode) OD matrices developed from observed Bluetooth and smart card data, respectively, for the Brisbane City Council region, Australia. The gaps in transit service usage are identified at different sections of the network using both methods. The findings from this study reveal that CLAN can be effectively used to prioritize OD pairs for transit improvement at a coarser level. The OD pairs with the highest and least priority from CLAN are further tested using FLAN. The results from FLAN further confirmed the findings from CLAN. The study showed that both methods have their own advantages and disadvantages if applied independently. However, when applied together, the two methods can help prioritize zones and OD pairs for wider benefits of transport systems such as improvement in transit patronage. The methodological framework proposed in this study is generic and can be applied to compare other multimodal combinations.
Original language | English |
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Journal | IEEE Transactions on Intelligent Transportation Systems |
DOIs | |
Publication status | Accepted/In press - 2021 |
Keywords
- Australia
- Automobiles
- Bluetooth
- Bluetooth data
- car demand
- Intelligent transportation systems
- multimodal OD comparison
- smart card data
- Smart cards
- Sociology
- Systematics
- transit demand
- transit service improvement.
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications