Day-to-day evolution of network flows under departure time dynamics in commuter decisions

Day-to-day dynamics in an urban traffic network induced by departure time dynamics in commuter decisions are investigated. This investigation relaxes some key restrictions about fixed departure time and equilibrium assumptions to analyze the stability and performance of urban traffic networks over a multiple day planning horizon. A simulation-based framework is developed to analyze day-to-day dynamics by integrating an empirically calibrated model of departure time decisions with a dynamic network assignment model. Computational experiments are used to investigate the effect of the following experimental factors: recurrent network congestion level, time-dependent loading profile, and users' sensitivity to commute experience and trip-time volatility on network performance and reliability. The findings provide evidence of considerable day-to-day variations and stochasticity in network flows and performance, even under the assumption of fixed routes and in the absence of information. The results indicate that (a) the network performance under departure time dynamics can deviate significantly from equilibrium; (b) the departure time adjustment process is remarkably stable and reaches stationarity, although the departure time choices do not appear to be at equilibrium; (c) departure time. dynamics introduce significant volatility in trip times from day to day; and (d). increasing the sensitivity of users to commute and network performance attributes (schedule delay, trip-time variability) can lead to more stable system behavior and reliabitity. These results have important implications for estimation of time-dependent origin-destination matrices, dynamic network analysis, and effective congestion management strategies.