Pedestrians and cyclists mobility models
With the increasing transport demand and the raising of environmental awareness, tools for analysing travel demand are constantly being reviewed and updated. Active mobility rates, such as walking, cycling, and riding personal mobility devices (PMDs), are increasing in Singapore as in many countries...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/104139 http://hdl.handle.net/10220/47797 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With the increasing transport demand and the raising of environmental awareness, tools for analysing travel demand are constantly being reviewed and updated. Active mobility rates, such as walking, cycling, and riding personal mobility devices (PMDs), are increasing in Singapore as in many countries in the world. In Singapore, active mobility users share off-road travelling spaces as necessitated by the limited land space in the urbanised island-nation, which is not common in other countries. As previous transport research was mostly focused on motorised modes, a knowledge gap regarding pedestrians’ and cyclists’ travel quality needs and travel demand analysis has become apparent. Concepts from several fields have been utilised to explain travel-related theories. However, not many of the theories specifically address decisions related to walking and cycling. In addition, many are based on specific local context and thus are not transferable to the unique urban layout and transport characteristics of Singapore.
In this research, to understand pedestrians’ and cyclists’ quality needs in the local context, three studies were developed (due to the different nature of the factors being studied) utilising: [1] intercept qualitative surveys (202 participants); [2] mixed-method – qualitative and quantitative questions (277 participants); and [3] qualitative interviews (46 participants). Overall, walking was described as a safe and convenient “mode of transport” but the distance limitations reduce its attractiveness. Cycling was perceived as an enjoyable “activity” and its limitations could be addressed with enhancement of infrastructure and/or provision of cycling-centric policies. Off-road delineated paths were found to be the preferred mobility facility by cyclist and pedestrian respondents, as these spaces can be dynamically used, shifting between pedestrian and cycling paths in response to traffic or perceived obstructions. In addition, the provision of bicycle-sharing scheme, in combination with infrastructural improvements, was found to be associated with a rapid increase in cycling demand in the recent years with most of the trips being used for transport purposes.
The findings from the three studies, together with knowledge from a comprehensive review of 27 theories that are applicable to travel-behaviour, were used to develop a holistic and dynamic “process and determinants of mobility decisions (PDMD)” framework. The PDMD framework considers how the different aspects vary according to different locations and people, and thus how these can be applied into different contexts. This was found to be missing in most travel behaviour theories. Then, utilising the PDMD framework, a refined household travel survey questionnaire was developed, which was then utilised to collect data for analysing travel demand, with strong emphasis on active mobility movements.
For the demand analysis, modifications to the traditional Four-Step Method (4SM) were introduced by shifting the order of the steps and a computer intelligent neural network model (using Bayesian regularisation as training function) was developed for mode choice modelling. The order of the steps has previously been modified for demand analysis, but few studies have mainly focussed on active modes trips, and usage of the off-road (formal) transport network. Neural networks were selected for this study in view of the complex travel behaviour as this method allows for multiparametric modification of factors to estimate changes in travel demand. Data from 1,119 households (and corresponding 9,316 trip-stages) in 4 residential towns in Singapore was utilised for modelling.
The thesis provides an overview of the many capabilities of neural network models regarding scenarios modifications to estimate changes in demand. Besides current scenario (encapsulated in the survey data), three modified scenarios were considered and the changes in travel demand were estimated. A good fit of the neural network model was found (R>0.96, and normally-distributed errors between -2 and 2). It was found that policies and programmes (e.g. reducing vehicle ownership, increasing access to bicycles and PMDs, etc.) have stronger influence in increasing active mobility demand as compared to modifications to the built-environment (e.g. reduced distance to public transport, enhanced cycling paths, etc.) while a combination of policies/programmes and infrastructure improvements yields the best outcomes regarding active mobility. |
|---|