Transit goals have typically focused on commuter trips but facilitating urban last mile freight logistics is a potential strategy to increase transit ridership and mitigate the demands of parcel distribution on the transportation network. Presently, most parcel lockers operate out of private businesses, but consumer surveys have found that transit users may be interested in locker facilities at transit facilities. The implementation of an unmanned, secure, common carrier parcel locker system could have benefits for non-transit users as well. This research presents a multiple-criteria approach for analyzing the potential of public transportation facilities as hosts for a common carrier locker system. Several accessibility and equity metrics, including ridership, mode of transportation, spatial distribution, and sociodemographic profiles of coverage areas are utilized. A case study utilizing real-word data from the Portland, OR region and its transit facilities is used to illustrate accessibility and equity tradeoffs. The results demonstrate that multiple facility types have the potential to host a locker system but there are complex accessibility and equity tradeoffs to be considered by stakeholders and policy makers when prioritizing locations.

The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation.

This research evaluates conflicts and delays caused by interactions between buses, bicycles, and right-turning vehicles. Two concerns caused by these overlapping bus, bicycle, and automobile facilities are analyzed; the first concern is the number of bus-bicycle conflicts (as a proxy for safety) and the second concern is bus delay. Video data was collected and analyzed to quantify conflicts, travel time, and delay. For every bus passing through the study site, the mixed-traffic scenario that the bus incurs was categorized as one of 72 different combinations of bus, bicycle, and automobile interactions. Video count data was weighted according to seasonal, weekly, and hourly bicycle volume data to estimate the number of annual bus-bicycle conflicts. A regression analysis was performed to identify potential sources of delays. The results indicate that each bicycle crossing the intersection after the bus (within 60 feet of the bus) contributes to bus delay. No statistically significant delay was found from the bicycles stopped in the bicycle box, bicycles stopped behind the bicycle box, bicycles that crossed the intersection before the bus, or the presence of right-turning vehicles.

No abstract