Dwell time is defined as the time that delivery workers spend performing out-of-vehicle activities while their vehicle is parked. Restricting vehicle dwell time is widely used to manage commercial vehicle parking behavior. However, there is insufficient data to help assess the effectiveness of these restrictions. This makes it difficult for policymakers to account for the complexity of commercial vehicle parking behavior.
This dissertation aims to provide insights and data-driven approaches to support freight plans in various cities around the globe with a focus on urban freight deliveries. To accomplish this goal, this dissertation first proposes to discover the current delivery process at the final 50 feet by creating value stream maps that summarize the flow of delivery activities and times, time variations between activities. The map will be based on the data collected from five freight-attracting buildings in downtown Seattle. Secondly, this research explores contributing factors associated with dwell time for commercial vehicles by building regression models. Dwell time, in this study, is defined as the time that delivery workers spend performing out-of-vehicle activities while their vehicle is parked. Finally, this dissertation predicts total time spent at the final 50 feet of delivery, including dwell times and parking-related times through discrete event simulations for various “what if” delivery scenarios.
Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed.
Seattle now ranks as the nation’s sixth-fastest growing city and is among the nation’s densest. As the city grows, so do truck volumes—volumes tied to economic growth for Seattle and the region as a whole. But many streets are already at capacity during peak hours and bottleneck conditions are worsening. This project is designed to deliver critical granular baseline data on commercial vehicle movement in two key areas of the
As e-commerce and urban deliveries spike, cities grapple with managing urban freight more actively. To manage urban deliveries effectively, city planners and policy makers need to better understand driver behaviors and the challenges they experience in making deliveries. In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers.
Although road infrastructure has been designed to accommodate human drivers’ physiology and psychology for over a century, human error has always been the main cause of traffic accidents. Consequently, Advanced Driver Assistance Systems (ADAS) have been developed to mitigate human shortcomings. These automated functions are becoming more sophisticated allowing for Automated Driving Systems (ADS) to drive under an increasing number of road conditions.
Urban deliveries are traditionally carried out with vans or trucks. These vehicles tend to face parking difficulties in dense urban areas, leading to traffic congestion. Smaller and nimbler vehicles by design, such as cargo-cycles, struggle to compete in distance range and carrying capacity. However, a system of cargo-cycles complemented with strategically located cargo-storing hubs can overcome some limitations of the cargo-cycles.
Despite significant advances in freight transport modeling in recent years, there is still lack of available tools for evaluating novel logistics solutions. We introduce the framework of SimMobility Freight, which is part of SimMobility, a multi-scale agent-based urban transportation simulation platform. SimMobility Freight is capable of simulating commodity contracts, logistics and vehicle operation planning and parking decisions in a fully-disaggregate manner.
This report presents research to improve the understanding of curb space and delivery needs in urban areas. Observations of delivery operations to determine vehicle type, loading actions, door locations, and accessories used were conducted. Once common practices had been identified, then simulated loading activities were measured to quantify different types of loading space requirements around commercial vehicles.
Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affect such behavior.