As cities approach transport network capacities, solutions to that seek a more efficient use of the transport infrastructure are useful in policy strategies. Australian cities are no exception with ever increasing levels of demand for road space, especially during morning and evening peak demand periods. One proposed solution method for tackling this problem is the implementation of peak spreading policies that seek to better manage this aspect of car demand can lead to improved transport outcomes for the wider community. This can include a more financially, environmentally and socially sustainable transport network. The concept of peak spreading thus introduces strategies and management techniques to handle peak traffic demands as it allows for the spreading of peak period traffic flow profiles in congested areas. The passive peak spreading dimension describes an increase in the duration of a peak period as travel demand tests the capacity of a facility so that the levels of peak travel activity persist for a longer period. The active peak spreading dimension relates to the behaviour of individual travellers to deliberately avoid peak periods, or transport policies that are enacted to encourage people to travel away from the peak periods. Peak spreading policies focus on the active aspect, therefore increasing the need for policy makers quantify to peak spreading phenomenon. Appropriate strategic modelling environments can meet this need. This paper describes the research involved in the development of a peak period departure timing model for car users and the additional abilities of the model to represent the influence peak spreading policy options. To begin, the paper defines the peak spreading phenomenon and provides a brief analysis of current modelling approaches. The structure and purpose of the developed model is then detailed in two main sections; the departure timing element followed by the peak spreading policy scenario modelling component. Travellers departure timing decision is modelled as a discrete choice between critical timing elements within both the morning and evening peaks. All trips between origin-destination pairs are assigned to an appropriate departure time slice depending on household, personal and journey attributes. Extensive use of household traveller survey information is made in the calibration procedures of the random parameters logit model employed here. Beyond the assignment of trips to departure times is the ability of the model to represent what-if scenarios in relation to peak spreading policies. Each trip with flexibility in the departure time is provided with an opportunity to depart earlier or later in relation to toll pricing and/or travel time saving regimes. The result is a departure time profile that incorporates the influence of peak spreading policy strategies. Finally, the paper details the application of the model to the real transport demands in Sydney, Australia and tests a range of peak spreading policy alternatives. For the covering abstract see ITRD E137145.
Abstract