Abstract
In order to select the correct mobility strategy, individuals’ choice behavior must be studied, and their reactions in response to strategies must be predicted. Upon exploring aspects subject to constraints, our results included walking distance, parking rates, search time and parking availability. We have identified scenarios in which simulation outputs of environmental parking mobility strategies differ significantly when using the Constrained Multi-Nominal Logit (CMNL) when compared with the widely used Multi-Nominal Logit (MNL). With the CMNL model growing in popularity, it is worth considering environmental mobility strategy repercussions in this context with the incorporation of relevant constraints.
1. Introduction
Managing parking demand is a crucial issue for any transportation administration seeking to balance the use of urban areas. The response of drivers to parking demand management measures depends mainly on the drivers ‘preferences; therefore (dis)incentives are used to achieve specific goals regarding traffic conditions and air quality.
In the literature, road transportation is often blamed for a significant portion of air pollution in the urban environment (O’Mahony et al., 2000). It is common for vehicular fleets within cities to increase at a speed unmatched by road infrastructure growth. This often leads to tariff differentiation and a shortage of parking spaces.
6. Conclusions
In order to simplify the analysis and come to a conclusion, we assumed that this fictional city’s parking supply consists solely of parking garages; on-street/off-street parking differentiation was not considered.
From a macro viewpoint, the number of arrivals in all scenarios is the same; hence, we studied the resulting effect of using one model (MNL) or over another (CMNL). In our tests, a closer view of what is happening within each parking facility indicates that search times are higher when walking distance and fare restrictions are included in the model. Facilities that satisfy most of the restrictions set by drivers (i.e.: walking convenience or charge) contend with more demand, therefore higher occupation that increases search time.
The model also confirms that parking user aid technology (e.g.: VMS, parking guidance systems, real-time information on websites, Smartphone applications), incorporated as an active occupancy restriction, plays a significant role in correcting the inefficiencies of choice making. Hence, modern cities, or ‘‘smart” cities, have the advantage of technology.
