Strategic Infrastructure Planning for Autonomous Vehicles

Compared with conventional human-driven vehicles (HVs), AVs have various potential benefits, such as increasing road capacity and lowering vehicular fuel consumption and emissions. Road infrastructure management, adaptation, and upgrade plays a key role in promoting the adoption and benefit realization of AVs.This dissertation investigated several strategic infrastructure planning problems for AVs. First, it studied the potential impact of AVs on the congestion patterns of transportation networks. Second, it investigated the strategic planning problem for a new form of managed lanes for autonomous vehicles, designated as autonomous-vehicle/toll lanes, which are freely accessible to autonomous vehicles while allowing human-driven vehicles to utilize the lanes by paying a toll.This new type of managed lanes has the potential of increasing traffic capacity and fully utilizing the traffic capacity by selling redundant road capacity to HVs. Last, this dissertation studied the strategic infrastructure planning problem for an infrastructure-enabled autonomous driving system. The system combines vehicles and infrastructure in the realization of autonomous driving. Equipped with roadside sensor and control systems, a regular road can be upgraded into an automated road providing autonomous driving service to vehicles. Vehicles only need to carry minimum required on-board devices to enable their autonomous driving on an automated road. The costs of vehicles can thus be significantly reduced

15-13 Exploring Bicycle Route Choice Behavior with Space Syntax Analysis

Cycling provides an environmentally friendly alternative mode of transportation. It improves urban mobility, livability, and public health, and it also helps in reducing traffic congestion and emissions. Cycling is gaining popularity both as a recreational activity and a means of transportation. Therefore, to better serve and promote bicycle transportation, there is an acute need to understand the route choice behavior of cyclists. This project explored the applicability of using space syntax theory to model cyclists’ route choice behavior. In addition, several bicycle-related attributes were also considered as influential factors affecting cyclists’ route choice. A multiple regression model was built and calibrated with real-world data. The results demonstrated that space syntax is a promising tool for modeling bicycle route choice, and cyclists’ cognitive understanding of the network configuration significantly influences their route choice

MEIS2C and MEIS2D promote tumor progression via Wnt/β-catenin and hippo/YAP signaling in hepatocellular carcinoma

Abstract Background MEIS2 has been identified as one of the key transcription factors in the gene regulatory network in the development and pathogenesis of human cancers. Our study aims to identify the regulatory mechanisms of MEIS2 in hepatocellular carcinoma (HCC), which could be targeted to develop new therapeutic strategies. Methods The variation of MEIS2 levels were assayed in a cohort of HCC patients. The proliferation, clone-formation, migration, and invasion abilities of HCC cells were measured to analyze the effects of MEIS2C and MEIS2D (MEIS2C/D) knockdown with small hairpin RNAs in vitro and in vivo. Chromatin immunoprecipitation (ChIP) was performed to identify MEIS2 binding site. Immunoprecipitation and immunofluorescence assays were employed to detect proteins regulated by MEIS2. Results The expression of MEIS2C/D was increased in the HCC specimens when compared with the adjacent noncancerous liver (ANL) tissues. Moreover, MEIS2C/D expression negatively correlated with the prognosis of HCC patients. On the other hand, knockdown of MEIS2C/D could inhibit proliferation and diminish migration and invasion of hepatoma cells in vitro and in vivo. Mechanistically, MESI2C activated Wnt/β-catenin pathway in cooperation with Parafibromin (CDC73), while MEIS2D suppressed Hippo pathway by promoting YAP nuclear translocation via miR-1307-3p/LATS1 axis. Notably, CDC73 could directly either interact with MEIS2C/β-catenin or MEIS2D/YAP complex, depending on its tyrosine-phosphorylation status. Conclusions Our studies indicate that MEISC/D promote HCC development via Wnt/β-catenin and Hippo/YAP signaling pathways, highlighting the complex molecular network of MEIS2C/D in HCC pathogenesis. These results suggest that MEISC/D may serve as a potential novel therapeutic target for HCC.https://deepblue.lib.umich.edu/bitstream/2027.42/152244/1/13046_2019_Article_1417.pd

Robust planning of dynamic wireless charging infrastructure for battery electric buses

Battery electric buses with zero tailpipe emissions have great potential in improving environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited the popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate the drawbacks of electric buses. In this paper, we address the problem of simultaneously selecting the optimal location of the DWPT facilities and designing the optimal battery sizes of electric buses for a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of electric buses is ignored. The methodology of robust optimization (RO) is then adopted to address the uncertainty of energy consumption and travel time. The affinely adjustable robust counterpart (AARC) of the deterministic model is developed, and its equivalent tractable mathematical programming is derived. Both the deterministic model and the robust model are demonstrated with a real-world bus system. The results demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses

Integrated charging infrastructure planning and charging scheduling for battery electric bus systems

Studies on the electric bus system planning problem have typically focused exclusively on either the deployment of charging infrastructure or the scheduling of charging events; few have examined the impact of charging facility deployment on charging activities. Considering the interdependence of system design and operational strategies, this study proposes a two-phase optimization framework for charging infrastructure planning and charging scheduling for battery electric bus systems. An integrated optimization model is first developed to simultaneously optimize charger deployment, on-board battery capacity, and charging schedules. A charging scheduling model is further proposed and a rolling horizon approach is utilized in the second phase to optimize the real-time charging scheduling of electric buses. Compared to existing electric bus system planning methods, the proposed integrated model can reduce the total system cost by 19.5%. In addition, compared to uncontrolled charging, the proposed rolling horizon-based charging strategy can reduce the total charging cost by 68.3%

Joint optimization of electric bus charging infrastructure, vehicle scheduling, and charging management

High upfront costs of vehicles and charging infrastructure as well as the lack of knowledge related to infrastructure planning and electric bus system operation are major obstacles to the implementation of battery electric buses (BEBs). To tackle the obstacles and promote BEB adoption, a comprehensive optimization framework was developed to address the combined charging infrastructure planning, vehicle scheduling, and charging management problem for BEB systems, with the goal to minimize the total cost of ownership. The problem was formulated as a mixed-integer non-linear problem. A genetic algorithm-based approach was then proposed to solve the problem. Last, three alternative scenarios based on a sub-transit network in Salt Lake City, Utah, were analyzed and compared with the optimal scenario results in the numerical experiments. The comparison results demonstrate the effectiveness of the proposed model and solution algorithm in determining a cost-efficient planning strategy for BEB systems

Optimal Deployment of Wireless Charging Facilities for an Electric Bus System

Battery electric buses with zero tailpipe emissions have great potential to improve environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate drawbacks of electric buses. In this study, we address the problem of simultaneously selecting the location of the DWPT facilities and designing battery sizes of electric buses for a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of buses is neglected. The methodology of robust optimization (RO) is then adopted to address the uncertainty. Numerical studies demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses