published journal article
published journal article
This study aims to evaluate and quantify the impact of demand forecast spatial resolution on the operational performance of a shared-use automated vehicle (AV) mobility service (SAMS) fleet. To perform the evaluation, this study employs an agent-based modeling framework that includes user requests, AVs, and an SAMS fleet controller. In the simulation, an SAMS fleet controller dynamically assigns AVs to on-demand user requests and repositions empty AVs throughout the service region to serve expected future demand requests. The fleet controller uses an offline demand forecast model and an online optimization model that jointly assigns AVs to users and repositioning trips. Results indicate that despite demand forecast quality decreasing at higher spatial resolutions, the operational efficiency of the SAMS fleet increases with higher spatial resolution forecasts (i.e. smaller subareas). Results also indicate that there is a significant operational value associated with improving short-term demand forecasts at high spatial resolutions.
published journal article
conference paper
published journal article
conference paper
Accurate evaluations of general truck management strategies (GTMS) are required in order to identify positive benefits that can be expected to result from implementation. Most GTMS have been studied with primary or even exclusive consideration of the public sector standpoint. Traffic agencies have primarily focused on addressing operational and safety aspects of GTMS. These are important but insufficient because they do not accurately describe the full effects of GTMS. In our research, multi-criteria performance measures are used to reflect and evaluate overall benefits, including the public and private sector standpoints. This study was performed on an approximately 10-mile section of the I-710 corridor in Southern California. This corridor has one of the highest truck volumes in the US. We considered a truck lane restriction strategy because it is regarded as the most appropriate option for this facility. Three types of scenarios were developed to examine the proper number of restricted lanes. The micro traffic simulation models TransCAD and PARAMICS were the primary analytical tools employed in this research. We concluded that scenario 3, in which two lanes out of four to six are restricted, would provide the maximum positive benefits to the public and private sectors. This study also demonstrated the number of restricted lanes is an important factor in the success of the implementation of this strategy. Our simulation model is fairly general and can be used to evaluate other possible GTMS such as weigh in motion and the addition or conversion of one or more lanes to allow trucks only.
published journal article
This paper provides a simple analysis of the effects of network structure on the scheduling, traffic, and aircraft size choices of a monopoly airline. The analysis shows that switching to a hub-and-spoke network leads to increases in both flight frequency and aircraft size, while stimulating local traffic in and out of the hub. In addition, HS networks are shown to be preferred by the airline when travel demand is low, when flights are expensive to operate, and when passengers place a high value on flight frequency but are not excessively inconvenienced by the extra travel time required for a connecting trip. The welfare analysis shows that the flight frequency, traffic volumes, and aircraft size chosen by the monopolist are all inefficiently low under both network types. Moreover, in the most plausible case, the monopolist’s network choice exhibits an inefficient bias toward the HS network, apparently reflecting an excessive desire to economize on the number of flights.
published journal article
published journal article
Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.
published journal article