Browsing by Author "Lee, Young-Jae"
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Item Innovative Methods for Delivering Fresh Foods to Underserved Populations(2019-12) Shin, Hyeon-Shic; Schonfeld, Paul; Lee, Young-Jae; Urban Mobility & Equity CenterLimited access to fresh food sources--ones within reasonable distances with reliable, affordable transportation--has become a public health concern. The negative associations between a lack of fresh food consumption and health are well known. Because certain demographic groups are disproportionately affected by the absence of stores selling healthy and affordable food, equity issues result. Many inner-city residents are left in neighborhoods devoid of such stores, and every day they are forced to trade off increased costs against healthy food consumption and health. This study aimed to develop a cost-effective last-mile fresh food delivery system to households in food deserts, which could help improve fresh food accessibility. Six alternative delivery modes--conventional trucks, e-bikes, shared-ride transit, parcel lockers, pop-up stores, and independently contracted drivers--were identified and optimized by employing Traveling Salesman Problem. Then we compared the results with the system's total costs. Sensitive analyses were conducted in terms of the time of delivery, zone size, user's value of time waiting for goods, the optimal number of lockers, costs associated with combined deliveries at lockers as well as customer addresses, and a second delivery attempt. Building on optimized modes, GIS network analyses were performed for randomly selected household locations in parts of poverty-prone West Baltimore. Numerical results showed that deliveries by trucks are the most cost-effective alternative, while the third-party deliveries ranked second. The two most expensive alternatives were shared-ride service and e-bike deliveries, based on the estimated costs of providing them.Item Optimal Automated Demand Responsive Feeder Transit Operation and Its Impact(2018-09) Lee, Young-Jae; Nickkar, Amirezza; Urban Mobility & Equity Center; University Transportation Centers ProgramAlthough demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and mostly available as a special service because of the high operating costs due to the intensive labor costs as well as advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, thanks to recent technological advances and business innovations by the transportation network companies (TNCs). So, preparing for the automated flexible feeder service is necessary to catch the rapid improvement of automated vehicle technology. Therefore, this research developed an algorithm for the optimal flexible feeder bus routing, which considers relocation of buses for multi-stations and multi-trains, using a simulated annealing (SA) algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocating the buses when the optimal bus routings were not feasible with the available buses at certain stations. Furthermore, the developed algorithm limited the maximum Degree of Circuity for each passenger while minimizing total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs. Unlike fixed route mass transit, small vehicle demand responsive service uses flexible routing, which means lower unit operating costs not only decrease total operating costs and total costs but also can affect routing and impact network characteristics. In the second part of this research, optimal flexible demand responsive feeder transit networks were generated with various unit transit operating costs using the developed routing optimization algorithm. Then network characteristics of those feeder networks were examined and compared. The results showed that when unit operating costs decline, total operating costs and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce total operating costs, total costs and total passenger travel costs as well.