Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better.

Using ultrasound activating contrast agents to induce sonoporation is a potential strategy for effective lesion-targeted gene delivery. Previous reports have proven that submicron nanodroplets have a better advantage than microbubbles in that they can pass through tumor vasculature endothelial gaps by passive targeting; however, they cannot achieve an adequate dose in tumors to facilitate ultrasound-enhanced gene delivery. Additionally, a few studies focused on delivering macromolecular genetic materials (i.e. overexpression plasmid and CRISPR plasmid) have presented more unique advantages than small-molecular genetic materials (i.e. miRNA mimics, siRNA and shRNA etc.), such as enhancing the expression of target genes with long-term effectiveness. Thereby, we constructed novel plasmid-loadable magnetic/ultrasound-responsive nanodroplets, where superparamagnetic iron oxide nanoparticle dispersed perfluoropentane was encapsulated with lipids to which plasmids could be adhered, and branched polyethylenimine was used to protect the plasmids from enzymolysis. Furthermore, in vitro and in vivo studies were performed to verify the magnetic tumor-targeting ability of the plasmid-loadable magnetic/ultrasound-responsive nanodroplets and focused ultrasound enhanced intracellular plasmid delivery. The plasmid-loadable magnetic/ultrasound-responsive nanodroplets, carrying 16–19 plasmids per droplet, had desirable diameters less than 300 nm, and integrated the merits of excellent magnetic targeting capabilities and phase transition sensitivity to focused ultrasound. Under programmable focused ultrasound exposure, the plasmid-loadable magnetic/ultrasound-responsive nanodroplets underwent a phase-transition into echogenic microbubbles and the subsequent inertial cavitation of the microbubbles achieved an ∼40% in vitro plasmid delivery efficiency. Following intravenous administration, T2-weighted magnet resonance imaging, scanning electron microscopy and inductively coupled plasma optical emission spectrometry of the tumors showed significantly enhanced intratumoral accumulation of the plasmid-loadable magnetic/ultrasound-responsive nanodroplets under an external magnetic field. And a GFP ELISA assay and immunofluorescence staining indicated that focused ultrasound-induced inertial cavitation of the plasmid-loadable magnetic/ultrasound-responsive nanodroplets significantly enhanced the intracellular delivery of plasmids within the tumor after magnet-assisted accumulation, while only lower GFP levels were observed in the tumors on applying focused ultrasound or an external magnet alone. Taken together, utilizing the excellent plasmid-loadable magnetic/ultrasound-responsive nanodroplets combined with magnetism and ultrasound could efficiently deliver plasmids to cancer cells, which could be a potential strategy for macromolecular genetic material delivery in the clinic to treat cancer.

This paper formulates a dynamic approach for real-time bus control in uncertain demand. This dynamic approach aims to save the total cost for passengers and operators, while improving transit service reliability. An unfixed rolling horizon was implemented to choose the best dynamic approach. Real-time control predicts two discrete variables (arrival time and bus position) and determines the space-time point of buses. Furthermore, controlled actions include stop skipping and bus holding. The holding time starts when a bus serves a station and depends on previous intervals of passenger boarding and alighting at the station. The stop skipping action allows a bus to skip not only stations with a short-turning exception, but also stations with low demand for boarding that have been alighted in the short-turning segment. Stop skipping and bus holding actions for short-turning service both decrease the travel time of served passengers and the running time of buses, thus improving transit service reliability. A genetic algorithm was applied to solve the problem and the validity of the proposed dynamic approach was tested with four different scenarios. The result of these tests shows that a dynamic short-term bus control can significantly reduce total cost and improve transit service reliability.

In a traditional transit system, passenger arrival time and bus running time are typically random and uncoordinated. This randomness gives the appearance of unbalanced passenger demand and unreliable transit services. Therefore, this paper proposes a real-time control method for bus routes. In our method, buses skip some stations and turn back at appropriate stations, in order to balance passenger demand along the bus route and improve the overall transit service. Our real-time control method considers the typical changes in passenger demand and the stochastic travel time of buses. In this paper, the number of controlled vehicles at any given time is determined, and the bus holding time at the turn-back station is adopted. When implemented correctly, the optimal scheme indicates which stations should be skipped, where it is suitable for buses to turn back, and how long the holding time should be at turn-back stations, which in turn will minimize the total cost of a transit system. This paper formulates such an integrated strategy, presents the solution method of the formulation, and proves the validity of the real-time control method.

As a major choice for daily travel, public transit plays an important role in transporting passengers, thus relieving congestion on urban transit routes. In high-demand bus networks, urban transit demand presents imbalance of use of urban bus corridors. The demand patterns in both directions are asymmetric. In this paper, we develop a model which calculates the network and transportation costs in terms of wait time, in-vehicle travel time, and operator costs. We propose an integrated strategy, with an integrated limited-stop and short-turn line, by adjusting a variety of frequencies to meet the unbalanced and asymmetric demand. To minimize these costs, a model with a genetic algorithm can determine frequencies and the proper stations which can be skipped, as well as where turning back can occur, given an origin-destination trip matrix. Numerical examples are optimized to test the availability of an integrated service by minimizing the objective function, and the results are analyzed. Our results show that integrated service patterns can be adjusted to meet the demand under different conditions. In addition, the optimized schemes of an integrated service and the frequencies derived from the model can significantly reduce total cost.

From the perspective of urbanization, commuters’ long-distance and diversified trips are suitable for the connection between city and suburbs. The existing park-and-ride (P&R) facilities can influence user travel decisions, promoting the usage of public transportation and reducing the environmental pollution, but the P&R lots are usually located near train stations that the construction of lots at these locations is costly and complex. It is important to analyze the need for P&R and its capacity before its construction. Therefore, this paper proposes a bi-level model for determination of optimal location and capacity of a remote P&R (RPR) facility that allows a user to park a vehicle in a suburban area at lower cost and then take a bus to the nearby train station. A combined modal split and traffic assignment model with capacity constraints is developed as a lower-level model, and a nested-logit model is adopted to manage the mode similarity. The upper-level optimization is conducted based on the generalized cost of the transportation system according to the budget and environmental limits. The performance of the proposed model is experimentally verified that the optimal RPR scheme shifts commuters from the automobile mode to the transit and RPR modes, thus improving the social benefit dramatically. The model and analysis provide insights that may help operators determine how RPR facilities can be established to meet social cost improvement goals.