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Given the high levels of congestion that many commuters in the United States experience, the urban air mobility community has been exploring the potential of using a new class of electric vertical takeoff and landing (eVTOL) aircraft for commuters. To date, only a few studies have been conducted that compare how potential air taxi demand across cities varies. This study calculates a measure of air taxi demand for commuters for the 40 most populous combined statistical areas (CSAs) in the U.S. by using: (1) cell phone data to identify regular commuters in cities, (2) census data to associate household income characteristics with commuters, and (3) a mode choice model calibrated from a stated preference survey to predict the number of commuters that would use an air taxi. Air taxi commuter demand is concentrated in a handful of CSAs; the CSAs for New York City, Los Angeles, and Washington, D.C., generate 33 percent of the overall air taxi demand. Results are sensitive to location of existing vertiports, existing ground infrastructure and congestion levels on competing modes, and current commute patterns. A resultant set of online maps of potential air taxi routes allows readers to visualize how air taxi commuter routes differ across CSAs. Results will be of value both to aircraft manufacturers seeking to design air taxi vehicles to serve different cities as well as city planners for identifying where investments in port infrastructure may be needed to support a commuter air taxi service.

Passive monitoring with the Global Positioning System (GPS) is increasingly used to automatically monitor trip data. However, GPS tracking data includes measurement errors that depend on the monitoring device and network description in the model. In the case of urban pedestrian networks, such as city centers, the built environment of streets is often diverse, and this has a significant impact on the measurement. The errors cause the biased observations of route choices, and thus the parameter estimation results of route choice models are also biased. To deal with this problem of biased estimation, this study proposes a link-based route measurement model that sequentially infers links using decomposed sequences of data and estimates the link-specific variance of the GPS measurement error. We also incorporate a link-based route choice model as the prior to correct the measurement model by considering behavioral mechanism without path enumeration. Additionally, to remove the biases included in the prior information, this study proposes a structural estimation method in which the fixed point problem of behavioral parameter is solved by the iteration process. The performance of the proposed methods is examined both through a numerical example and a case study on a real pedestrian network. As the results, the methods refine the performance of the route measurement model, and the estimated parameters of a route choice model obtained by the structural estimation method are less biased and exhibit a different trend than those using the biased route choice observations. Also, the estimated variances of the GPS measurement errors are realistic.

The Discrete Split Delivery Vehicle Routing Problem with Time Windows (DSDVRPTW) consists of designing the optimal set of routes to serve, at least cost, a given set of customers while respecting constraints on vehicles' capacity and customer time windows. Each customer can be visited by more than one vehicle since each customer's demand, discretized in items, can be split in orders, i.e., feasible combinations of items. In this work, we model the DSDVRPTW assuming that all feasible orders are known in advance. Remarkably, service time at customer's location depends on the delivered combination of items, which is a modeling feature rarely found in literature. We present a flow-based mixed integer program for the DSDVRPTW, we reformulate it via Dantzig-Wolfe and we apply column generation. The proposed branch-and-price algorithm largely outperforms a commercial solver, as shown by computational experiments on Solomon-based instances. A comparison in terms of complexity between constant service time vs delivery-dependent service time is presented and potential savings are discussed. (C) 2011 Elsevier B.V. All rights reserved.


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