Publications

Summary:
This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors.

Summary:
The Norwegian Public Roads Administration, the Norwegian Geotechnical Institute, and SINTEF conducted a field test with a unmanned aerial system (UAS) with various instruments at the research station Fonnbu in Stryn. The purpose of the test was to evaluate the use of instrumented drones for monitoring and assessing avalanche danger. The instruments tested included optical and thermal imaging, laser scanning and ground-penetrating radar.

Summary:
Capturing pedestrian exposure is important to assess the likelihood of a pedestrian-vehicle crash. In this study, we show how data collected on pedestrians using personal electronic devices can provide insights on exposure. This paper presents a framework for capturing exposure using spatial pedestrian movements based on GPS coordinates collected from accelerometers, defined as walking bouts.

Summary:
Isolated communities are particularly vulnerable to disasters caused by natural hazards. In many cases, evacuation is the only option to ensure the population’s safety. Isolated communities are becoming increasingly aware of this threat and demand solutions to this problem. However, the large body of existing research on evacuation modeling usually considers environments where populations can evacuate via private vehicles and by using an existing road infrastructure.

Summary:
Delivery vehicle drivers are experiencing increasing challenges in finding available curb space to park in urban areas, which increases instances of cruising for parking and parking in unauthorized spaces. Policies traditionally used to reduce cruising for parking for passenger vehicles, such as parking fees and congestion pricing, are not effective at changing delivery drivers’ travel and parking behaviors.

Summary:
This paper explores the value of providing parking availability data in urban environments for commercial vehicle deliveries. The research investigated how historic cruising and parking delay data can be leveraged to improve the routes of carriers in urban environments to increase cost efficiency. To do so, the research developed a methodology consisting of a travel time prediction model and a routing model to account for parking delay estimates.

Summary:
Common-carrier parcel lockers present a solution for decreasing delivery times, traffic congestion, and emissions in dense urban areas through consolidation of deliveries. Multi-story residential buildings with large numbers of residents, and thus a high volume of online package orders, are one of the best venues for installing parcel lockers. But what is the right size for a residential building locker that would suit the residents’ and building managers’ needs?

Summary:
We utilize an unsupervised learning algorithm called-modes clustering (Huang 1998), which is similar to the better-known-means method (Hartigan and Wong 1979), but with a dissimilarity measure designed for categorical variables (Cao et al. 2012), originally developed for analyzing sequential categorical data such as gene sequences (Goodall 1966), but also amenable to curb zoning types.

Summary:
This paper presents a method for extracting transit performance metrics from a General Transit Feed Specification’s Real-Time (GTFS-RT) component and aggregating them to roadway segments. A framework is then used to analyze this data in terms of consistent, predictable delays (systematic delays) and random variation on a segment-by-segment basis (stochastic delays). All methods and datasets used are generalizable to transit systems which report vehicle locations in terms of GTFS-RT parameters.

Summary:
Predicting the stay time of private cars has various applications in location-based services and traffic management. Due to the associated randomness and uncertainty, achieving the promising performance of stay time prediction is a challenge. We propose an RNN-based encoder model to solve this problem, which consists of three components, i.e., an encoder module, an exception module, and an MLP dropout.

Summary:
In this research we consider an approach for improving the efficiency and tightness of column generation (CG) methods for solving vehicle routing problems. This work builds upon recent work on Local Area (LA) routes. LA routes rely on pre-computing (prior to any call to pricing during CG) the lowest cost elementary sub-route (called an LA arc) for each tuple consisting of the following: (1) a customer to begin the LA arc, (2) a customer to end the LA arc, which...

Summary:
At the core of any flight schedule is the four dimensional (4D) trajectories which are comprised of three spatial dimensions with time added as the fourth dimension. Each trajectory contains spatial and temporal features that are associated with uncertainties that make the prediction process complex. Because of the increasing demand for air transportation, airports and airlines must have optimized schedules to best use the airports’ infrastructure potential.

Summary:
In this research we consider an approach for improving the efficiency and tightness of column generation (CG) methods for solving vehicle routing problems. This work builds upon recent work on Local Area (LA) routes. LA routes rely on pre-computing (prior to any call to pricing during CG) the lowest cost elementary sub-route (called an LA arc) for each tuple consisting of the following: (1) a customer to begin the LA arc, (2) a customer to end the LA arc, which...

Summary:
Green loading zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some US cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users, and their willing to pay.

Summary:
At the time we are writing this article, hundreds of thousands of delivery vehicles are getting ready to hit the road and travel across U.S. cities to meet the highest peak of demand for ecommerce deliveries during Thanksgiving, Black Friday, and the Christmas holiday season. This mammoth fleet will not only add vehicle miles traveled through urban centers but also increase parking congestion, battling with other vehicles for available curb space.