Abstract

Transit oriented developments are high density mixed use developments located within short and easily walkable distance of a major transit centre. These developments are often hypothesised as a means of enticing a mode shift from the private car to sustainable transport modes such as, walking, cycling and public transport. However, it is important to gather evidence to test this hypothesis by determining the travel characteristics of transit oriented developments users. For this purpose, travel surveys were conducted for an urban transit oriented development currently under development. This chapter presents the findings from the preliminary data analysis of the travel surveys. Kelvin Grove Urban Village, a mixed use development located in Brisbane, Australia, has been selected as the case for the transit oriented developments study. Travel data for all groups of transit oriented development users ranging from students to shoppers, and residents to employees were collected. Different survey instruments were used for different transit oriented development users to optimise their response rates, and the performance of these survey instruments are stated herein. The travel characteristics of transit oriented development users are reported in this chapter by explaining mode share, trip length distribution, and time of day of trip. The results of the travel survey reveal that Kelvin Grove Urban Village users use more sustainable modes of transport as compared to other Brisbane residents.   Document type: Part of book or chapter of book

Abstract

In this paper we describe how we included travel time variability in the national Dutch transport forecasting model and what the policy impacts of this new forecasting tool are. Until now, travel time reliability improvements for road projects were included in Dutch cost-benefit analysis (CBA) by multiplying the travel time benefits from reduced congestion by a factor 1.25. This proportionality is based on the linkage between congestion reduction and reliability improvements. However, this treatment of reliability is not useful to evaluate policies that especially affect travel time variability. From the start, this method was provisional and meant to be replaced by a better method capturing travel time variability. For this, we derived an empirical relation between the standard deviation of travel time, mean delay of travel time and length of route. This has been implemented in the national Dutch model as a post processing module. The new travel time reliability forecasting model will be incorporated in the Dutch guidelines for CBA. Document type: Part of book or chapter of book