Impact of Variable Pricing on Temporal Distribution of Travel Demand

Abstract

Despite the potential of congestion pricing to ease the nation’s ever-increasing congestion problems, there is little quantitative evidence of its ability to spread peak travel demand more efficiently over the course of the day. The objective of the present work is to assess the impact of variable pricing on the temporal distribution of demand to investigate further the role of variable pricing as a travel demand management tool. The Variable Pricing Program of Lee County, Florida, was used as the data source for the study. Because of the limited congestion experienced at the program location, the effects of travel cost changes on the temporal distribution of demand could be isolated. It was found that program implementation had a minimal impact on the overall distribution of demand. Demand for peak-period travel remained relatively unaltered, and active peak spreading was not observed. At the more disaggregate level, however, the impact of the program was more apparent, with significant temporal shifts in the proportion of demand within individual half-hour time segments. Further analysis revealed a price elasticity relationship that was consistent with that in the existing literature. A relationship was also observed between the extent of preprogram peak spreading and the subsequent percent reduction in peak-period travel demand after program implementation. This finding suggests the potential to predict the active peak spreading that may result from congestion pricing. The fact that substantial temporal shifts in travel demand were observed in response to a discount of only 25 cents highlights the potential of variable pricing as a travel demand management tool.

The impacts of time of day pricing on car user behavior: findings from the Port Authority of New York and New Jersey’s initiative

“…The direct elasticities of the different demand segments with respect to tolls were calculated (see Table 7). In all cases, the demand was inelastic with an average elasticity of -0.143, which is consistent with previous research (Oum et al 1990;Cain et al 2001;Burris and Pendyala 2002). As a technical comment, it is important to note that, since cross-effects between the tolls at different time periods were not taken into account when computing the elasticities, it is likely that the elasticities overestimate the impact of pricing because they did not take into account how the lower toll rate in the off-peak periods, for instance, impacts the demand during the peak hours.…”

Design and evaluation of road pricing: state-of-the-art and methodological advances

“…The fixed-schedule congestion toll pricing is pre-determined, typically according to the historical traffic conditions by the time-of-day, usually seeking to shift the timing of travel demand from peak to off-peak periods [53,55,203,230,280]. Most of the current research focuses on the state-or congestion-dependent MCP, where optimal reactive congestion charges vary with the prevailing traffic conditions.…”

Exploiting Rush Hours for Energy-Efficient Contact Probing in Opportunistic Data Collection

“…It has been shown that human trajectories have a high degree of temporal and spatial regularity and their mobility follows simple reproducible patterns [17]. The temporal distribution of human mobility also shows high unevenness [16]. Hence, the contacts between a sensor node and mobile nodes tend to arrive unevenly in temporal and Rush Hours, during which contacts arrive more frequently, exist widely in the environment.…”