摘要: |
Previous research using the Heuristic On-Line Web Linked Arrival Time Estimator (HOWLATE) methodology showed that the user benefits associated with pre-trip route choice and trip timing are highly concentrated for congested PM peak trips. One implication of this finding is that pre-trip traveler information benefits increase with increasing congestion. This report explores how much benefit pre-trip traveler information provides on some of the worst commuting days, seen over a year, in Washington, DC. It analyzes the impacts on a commuter who does not utilize traveler information services, and examines what would have happened to his commute if he had made use of a notification-based pre-trip traveler information service on those days. The worst days were determined as those that had high travel times, travel disutility cost, travel-expenditure, late and early schedule delays, and poor on-time reliability and just-in-time reliability. When possible, contributing factors that made the days the worst with respect to a particular measure were identified from data on incidents, weather and high-demand. The study showed that the worst days varied by the measure of effectiveness chosen to rank them. High-demand was the main contributing factor for high travel time and travel expenditure, while incidents played a major role in increased travel disutility cost and late schedule delays, and poor on-time reliability and just-in-time reliability. The impacts on the worst days were significant for a commuter who did not rely on traveler information; typically late trips doubled and travel disutility cost jumped by 30%. The benefits of pre-trip traveler information were high on the worst days; lateness risk was cut by more than half and travel disutility cost was reduced by more than 20%. With respect to just-in-time reliability and early schedule delay, a commuter who made use of pre-trip traveler information fared better on the worst days than a commuter who did not use traveler information service on a typical day of the year. On the worst days, users who made use of pre-trip traveler information changed routes or trip start time or both on more than 60% of the trips. |