Understanding the differences in multi-modal travel demand can help transport planners to improve the sustainability of a transport system. Thus, this study aims to develop a multi-step methodological framework to identify gaps in demand between different modes and apply on a realistic large-scale network. The framework includes three methods. Method 1 is carried at a coarser level of spatial resolution, while method 2 and 3 are carried at one level finer resolution than that of method 1. The proposed framework is demonstrated using car and transit OD matrices developed from observed Bluetooth and smart card data, respectively for the Brisbane City Council region. The gaps in transit service usage are estimated between different sections of the network by identifying OD pairs that have low transit usage but high car demand. The findings from this study show that there are significant number of OD pairs that might require further investigation in order to improve overall transit patronage for Brisbane city. For instance, Method-1 showed that SA4 (coarser level) OD pair of Brisbane North- Brisbane East needed the most attention for transit improvement, and method-2 further identifies the SA2 (finer level) zones within Brisbane North- Brisbane East (for example, Eagle Farm – Pinkenba) that needed to be further investigated. Although the techniques are only applied to car and transit matrices, the proposed methods are generic in nature, and therefore can be applied to compare other modal combinations.
[1]
Ernesto Cipriani,et al.
Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation
,
2016
.
[2]
Jiwon Kim,et al.
Urban Trajectory Analytics: Day-of-Week Movement Pattern Mining Using Tensor Factorization
,
2019,
IEEE Transactions on Intelligent Transportation Systems.
[3]
Reid Ewing,et al.
Transit-Oriented Development in the Sun Belt
,
1996
.
[4]
Ahmad Tavassoli,et al.
How close the models are to the reality? Comparison of transit origin-destination estimates with automatic fare collection data
,
2016
.
[5]
Linda Steg,et al.
SUSTAINABLE TRANSPORTATION * - A Psychological Perspective -
,
2007
.
[6]
Ashish Bhaskar,et al.
Fundamental understanding on the use of Bluetooth scanner as a complementary transport data
,
2013
.