Publications

Summary:
While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between supply and demand, preventing the current resources from meeting the future needs of urban freight activities. This study aimed to discover the future delivery rates that would overflow the current delivery systems and find the optimal number of resources.

Summary:
Fundamental diagrams describe the relationship between speed, flow, and density for some roadway (or set of roadway) configuration(s). These diagrams typically do not reflect, however, information on how speed-flow relationships change as a function of exogenous variables such as curb configuration, weather or other exogenous, contextual information.

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.

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:
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:
Freight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates environmental (e.g. pollution), economic (e.g. delays in deliveries), and freight and social (e.g. traffic) negative externalities.

Summary:
Although trucks move the largest volume and value of goods in urban areas, relatively little is known about their travel patterns and how the roadway network performs for trucks.

Summary:
Bike facilities like bike lanes, bike trails, and neighborhood greenways have been the backbone of Seattle’s bike planning policy with the goal of promoting active transportation, reducing car dependence, improving social equity, and eliminating bike accidents.

Summary:
Currently, knowledge of actual freight flows in the US is insufficient at a level of geographic resolution that permits corridor-level freight transportation analysis and planning. Commodity specific origins, destinations, and routes are typically estimated from four-step models or commodity flow models. At a sub-regional level, both of these families of models are built on important assumptions driven by the limited availability of data.

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:
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:
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:
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:
Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle.