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. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles with global positioning system devices.

The results showed that, on average, urban CVs spent 80% of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with less than 5% of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times. We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and competition with other commercial drivers. The results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

In recent years, cities have changed their approach toward managing urban freight vehicles. Passive regulations, such as limiting delivery vehicles’ road and curb use to given time windows or areas (1), have been replaced by active management through designing policies for deploying more commercial vehicle (CV) load zones, pay-per-use load zone pricing, curb reservations, and parking information systems.

The goal is to reduce the negative externalities produced by urban freight vehicles, such as noise and emissions, traffic congestion, and unauthorized parking while guaranteeing goods flow in dense urban areas. To accomplish this goal, planners need to have an understanding of the fundamental parking decision-making process and behaviors of CV drivers.

Two main difficulties are encountered when CV driver behaviors are analyzed. First, freight movement in urban areas is a very heterogeneous phenomenon. Drivers face numerous challenges and have to adopt different travel and parking behaviors to navigate the complex urban network and perform deliveries and pick-ups. Therefore, researchers and policymakers find it harder to identify common behaviors and responses to policy actions for freight vehicles than for passenger vehicles. Second, there is a lack of available data. Most data on CV movements are collected by private carriers, who use them to make business decisions and therefore rarely release them to the public (2). Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions (3).

The urban freight literature discusses research that has employed various data collection techniques to study CV driver behaviors. Cherrett et al. reviewed 30 UK surveys on urban delivery activity and performed empirical analyses on delivery rates, time-of-day choice, types of vehicles used to perform deliveries, and dwell time distribution, among others. The surveys reviewed were mostly establishment-based, capturing driver behaviors at specific locations and times of the day. Allen et al. (5) performed a more comprehensive investigation, reviewing different survey techniques used to study urban freight activities, including driver surveys, field observations, vehicle trip diaries, and global positioning system (GPS) traces.

Driver surveys collect data on driver activities and are usually performed through in-person interviews with drivers outside their working hours or at roadside at specific locations. In-person interviews provide valuable insights into driver choices and decisions but are often limited by the locations at which the interviews occur or might not reflect actual choices because they are done outside the driver work context. Vehicle trip diaries involve drivers recording their daily activities while field observations entail observing driver activities at specific locations and establishments; neither collects insights into the challenges that drivers face during their trips and how they make certain decisions.

The same limitations hold true for data collected through GPS traces. Allen et al. (5) mentioned the collection of travel diaries by surveyors traveling in vehicles with drivers performing deliveries and pick-ups as another data collection technique that could provide useful insights into how deliveries/pick-ups are performed. However, they acknowledged that collecting this type of data is cumbersome because of the difficulty of obtaining permission from carriers and the large effort needed to coordinate data collection.

This study aims to fill that gap by collecting data on driver decision-making behaviors through observations made while riding along with CV drivers. A systematic approach was taken to observe and collect data on last-mile deliveries, combining both qualitative observations and quantitative data from GPS traces. The ridealongs were performed with various delivery companies in Seattle, Washington, covering a range of vehicle types (cars, vans, and trucks), goods types (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail).

The data collected will not only add to the existing literature by providing estimates of trip times, parking choice types, time and distance spent cruising for parking, and parking dwell times but will also provide insights into why those decisions are made and the factors affecting driver choices. The objectives of this study are to provide a better understanding of CV driver behaviors and to identify common and unique challenges they experience in performing the last mile. These findings will help city planners, policy makers, and delivery companies work together better to address those challenges and improve urban delivery efficiency.

The next section of this paper describes the relevant literature on empirical urban freight behavior studies. The following section then introduces the ridealongs performed and the data collection methods employed. Next, analysis of the data and qualitative observations from the ridealongs are described, and the results are discussed in five overarching categories: the time spent in and out of the vehicle, parking location choice, the reasons behind those choices, parking cruising time, and factors affecting dwell time.