Funded Research 

Selected Funded Research Projects in the Past Five Years:


Research Themes:

(1) Transportation systems analysis with focus on energy and emissions/air quality modeling

Our recent research involves (a) developing a holistic and integrated modeling approach to understanding the underlying relationship between travel demand, vehicular emissions, air pollution, and health exposure; and (b) developing real-time health exposure mobile app to inform and advise individuals daily activities. In (a), the modeling tool involves agent-based modeling of finely resolved individuals' daily activity patterns (location and duration), which are then used to estimate transportation network congestion levels, vehicle emissions, pollution, and finally the population exposure to the vehicular pollutants. Such a tool allows evaluation of potential health impact and environmental justice of transportation policies and investments at fine spatial and temporal scales. In (b), we are currently building a mobile app to monitor and inform personalized air-pollutant intake (PPI). This app combines the EPA's AirNow pollutant data with the location and activity recognition capabilities of smartphones as well as user specified physiological conditions (e.g., age, gender, health conditions). We envision that this app will become a powerful tool to facilitate citizen science and community engagement in protecting public health in the future. 

(2) City logistics and freight transportation

SusTrans Lab has been focusing on minimize operating cost, energy consumption, carbon footprint, and local pollution in the first/last mile urban logistics problems. There are two driving forces that are changing the landscape of city logistics in particular urban delivery. One is the rise of e-commerce - by 2017, online sales will account for more than 10% of the $4.5 trillion industry, according to a 2014 U.S. Census Bureau report. The other is the rapid advances in wireless communication and ubiquitous mobile computing that are enabling more efficient use of the otherwise unutilized or underutilized vehicle capacities in delivery services. More specifically, SusTrans Lab investigates green urban delivery strategies in the context of new mobility forms, such as real-time cargo-sharing, crowd-sourced, and new vehicle technology (e.g., electric commercial vehicles). Dr. Lin has been PI or co-PI of eight funded freight research projects and accumulated a total of $2,279,638 research funding since 2009, including an ongoing grant by the National Science Foundation (NSF) on Smart CROwdsourced Urban Delivery (CROUD) System ($1,000,000) awarded in August 2015. 

(3) Emerging mobility service enabled by information technology

Advanced information and vehicle technology are changing the way people and goods move around and the way mobility services are provided to people and goods. Think Uber, Deliv, Roadie, drone connected vehicles, autonomous vehicles.  As transportation service and mobility are increasingly transformed by advanced information and vehicle technology, this area of research is the future of transportation studies. Dr. Lin was a co-investigator of the UIC Computational Transportation Science (CTS) IGERT (Integrated Graduate Education and Research Traineeship) program funded by NSF ($3.097 million). Currently SusTrans Lab focuses on spatio-temporal resource – resource seeker matching that facilitates shared-use. Examples of transportation resources are parking slots, electric vehicle charging stations, ride sharing and bike sharing opportunities, and consolidated package deliveries. The notion of "shared economy" is the fundamental principle of Smart Cities empowered by the Internet of Things. That is, making a city smart means better utilization of unused or underused urban resources, through better detection, easier access, and greater sharing of resources. Thus, the underlying methodology and technology of our research can apply to a broad range of Smart City applications that involve spatial and temporal search and acquisition of resources. For example, one of the projects my research team is currently working on is a transit ride-sharing mobile app that enables travelers to share their taxi (or other transit vehicle) rides as they wish and best matches their preferences in ride-sharing. We are working on our prototype with the NYC taxi data of nearly 700 million trips over the past four years. 

(4) Large data analysis, advanced statistical and mathematical modeling

A consistent theme of SusTrans Lab research has been applications of state-of-the-art statistical/mathematical modeling tools and data mining to discover new knowledge in transportation studies that have important policy implications and practical values.  To give a few examples, in Chen et al. (2013) published on Public Transport (Springer), we devise an online iterative bus holding strategy to minimize total passenger waiting time by regulating bus headways. The strategy reads in real time bus location and passenger loading data streams from the Chicago Transit Authority (CTA) bus tracker sensors and fast computes the bus holding policy for real-time implementation.  The same real-time bus tracking data stream has been used in another study to predict urban street travel time and traffic conditions.  That idea was adopted by the Chicago Office of Emergency Management and Communication for developing a Google like traffic map cheaply for City of Chicago - Google Traffic or any other commercial vendors have limited traffic prediction coverage on urban streets.  In Lin et al. (2014) published on Networks and Spatial Economics, we investigate the cost and environmental performance (energy and emissions) of urban delivery consolidation strategies.  In this study, to circumvent the lack of detailed customer information, which is often proprietary, for detailed vehicle routing analysis, we construct continuous demand density functions that can be derived with aggregate statistics to spatially approximate customer demand at discrete location in a study region.  In Vallamsundar et al. (2017) we simulate first individuals' daily activity patterns (location and duration) with fine spatial and temporal resolutions, and subsequently the resulting transportation network congestion levels, vehicle emissions, pollution, and finally the population exposure to the vehicular pollutants.  In Lin et al. (2016), we propose a SLuggIng-Multiple-drop-offs (SLIM)-ridesharing algorithm that combines two forms of ridesharing, namely slugging and multiple-drop-off ridesharing, with a virtual demand management and matching system.  The proposed SLIM and ride matching system is simulated and evaluated with a NYC taxi dataset, which contains nearly 700 million trips over the past four years throughout the NYC metropolitan region.