published journal article
published journal article
Taxi fleets serve a significant and important subset of travel demand in major cities around the world. This paper characterizes the Chicago taxi fleet operational network using complex network metrics and analyzes the operational efficiency of individual taxis over the past four years using an extensive taxi-trip dataset. The dataset, recently released by the city of Chicago, includes the pickup and drop-off census tracts and time stamps for over 100 million taxi trips. The paper explores year-over-year changes in the spatial distribution of Chicago taxi travel demand. The taxi pickup and drop-off census tract locations are modeled as nodes, and links are generated between unique pickup and drop-off node pairs. The analysis shows that high-demand pickup and drop-off location pairs in 2013 generated similar trip volumes in 2016; however, the low-demand pairs in 2013 generated significantly fewer trips in 2016. Additionally, this paper presents temporal efficiency and spatial efficiency metrics. The temporal efficiency metric determines the percentage of in-service time taxis are productive (i.e., transporting travelers), rather than empty. The spatial efficiency metric measures the percentage of taxi miles that are productive (i.e., loaded), rather than empty. The efficiency analysis of the Chicago taxi fleet shows that, for most taxis, around 50% of their in-service time and travel distance are unproductive. This inefficiency negatively affects the profitability of individual drivers and the fleet, traffic congestion, vehicle emissions, the service quality provided to customers, and the ability of taxi services to compete with emerging mobility services.
published journal article
published journal article
book/book chapter
conference paper
conference paper
Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app’s network traffic-including ISPs and access points-to infer the app’s QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.
conference paper
Light-duty vehicles are responsible for over 16% greenhouse gas (GHG) emissions in the United States. Human driving behavior has a significant impact on vehicle efficiency, the emission of GHG and primary pollutants, and safety. With environmental health in mind, both academia and industry have the opportunity to develop advanced sensor and complementary control technologies to manage the human role. To explore this hypothesis, the research reported herein began with a comprehensive study of demonstration projects and academic publications which test and evaluate modern technologies to mitigate threats associated with safety and efficiency. The research identified the environmental signals to detect, the corresponding sensors to detect these signals, and the sensor technologies to study in greater depth. Of all the sensor technologies, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications technologies emerged as the most promising. A major requirement identified is a planning tool designed to assess advanced vehicle sensor technologies on traffic flow, fuel economy, and emissions. In response, a major focus of the research was then directed to (1) developing the Fuel Economy and Traffic of Connected Hybrids (FETCH) planning tool, and (2)Â?evaluating the utility of FETCH for a simple V2V-enabled automatic re-routing control on a custom roadway. The major outcomes of this research work are (1) the FETCH tool, (2) a research plan for utilizing FETCH to explore the variety of scenarios evolving for the advanced control of hybrid vehicles, and (3) an example research result of FETCH.
conference paper
conference paper