A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

The Fullness of time

THE FULLNESS OF TIME is a novel about the quest for identity that transcends the limitations of moral duty, social status, and cultural conventions. Set in Tarpon Springs, Florida, in 1969, it is the story of Victor Lucas, a young Greek immigrant forced to go to war in Vietnam in another man's place in order to save the woman he loves. He must survive the war to return and reclaim his love and his rightful place in society. Based on the archetypal hero's quest as articulated by Joseph Campbell, the narrative is told from the limited third-person perspective of the main character. Though the journey is particular to Tarpon Springs in the 1960s, it echoes the human struggle and triumph in the wanderings of Ulysses. The novel's influences include Thomas Hardy's The Mayor of Casterbridge, but is also thematically related to contemporary works exploring cultural turbulence and upheaval.