Lixin Miao

To validate the long-term performance of self-developed limestone calcined clay cement (LC3), this study evaluated the durability performance of LC3 produced using calcined excavated spoil. Results showed that LC3 exhibited a faster chloride adsorption rate than OPC, achieving peak binding capacity within 14 days, although its total chloride-binding capacity was slightly lower. The chloride diffusion coefficient of LC3 was approximately one order of magnitude lower than that of OPC, enhancing chloride resistance. However, LC3 demonstrated weaker carbonation resistance due to complete decomposition of portlandite (Ca(OH)₂) and ettringite (AFt), alongside partial degradation of calcium silicate hydrate (C-S-H) gels, resulting in pore structure coarsening. Compared to LC3 made with commercial metakaolin (K0), the self-developed LC3 using K1 and K2 clays from excavated spoil showed comparable chloride-binding capacity but slightly weaker chloride penetration resistance. Its carbonation resistance surpassed K0-based LC3. Overall, the self-developed LC3 matched commercial metakaolin-based LC3 in durability, validating the use of locally sourced clays. Producing LC3 from calcined excavated spoil addresses environmental challenges associated with spoil disposal while delivering satisfactory durability.

In this paper, we consider a novel reliability-based network equilibrium problem for mixed traffic flows of human-driven vehicles (HVs) and connected autonomous vehicles (CAVs) with endogenous CAV penetration rates and stochastic link capacity degradations. Travelers' perception errors on travel time and their risk-aversive behaviors on mode choice and path choice are incorporated in the model with a hierarchical choice structure. Due to the differences between HVs and CAVs, the perception errors and the safety margin reserved by risk-averse travelers are assumed to be related to the vehicle type. The path travel time distribution is derived by using the moment-matching method based on the assumption that link capacity follows lognormal distribution and link travel times are correlated. Then, the underlying problem is formulated as an equivalent variational inequality problem. A path-based algorithm embedded with the Monte Carlo simulation-based method is proposed to solve the model. Numerical experiments are conducted to illustrate the features of the model and the computational performance of the solution algorithm.

In this study, the tour planning problem for electric vehicles is investigated. We aim to derive the optimal route and thus, to maximize profitability and minimize range anxiety within the time horizon. To solve this problem, a bi-objective mixed integer model is proposed. Specifically, we first introduced the reliability of route planning and quantified it as a cost with specific functions. The nonlinear model was then converted into a bi-objective mixed integer linear program, and an interactive branch and bound algorithm was adopted. Numerical experiments conducted on different networks have shown that the model that considers range anxiety offers more effective solutions. This means that our model is able to plan the routes with high reliability and low risk of profit loss and accidents.

In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

China has been one of the most aggressive countries in electric vehicle (EV) promotion. However, private EV sales fail to achieve the government’s target. In particular, cutting purchase subsidies poses great uncertainty in relation to EV diffusion. In this research, a system dynamics model aims to investigate the influence of government policies, infrastructure development plans, the duration of policies, and the phase out strategy of policies. Parameters relating to consumers’ preferences are drawn from a questionnaire survey, which is conducted in Shenzhen, the pioneer city in China’s EV promotion. The result of a scenario analysis shows that purchase subsidies, purchase restrictions and driving restrictions are the most effective policies for EV promotion. Driving restrictions are more effective but less easy to enforce than purchase restrictions. The number and location of charging piles are much more important than large charging stations. Moreover, EV diffusion can be self-sufficient after current policies have been maintained for 11 years. We find that the gradual removal of subsidies will cause a four-year delay in EV sales entering rapid growth in Shenzhen. However, cutting subsidies in cities without purchase restrictions will cause the failure of EV promotion.

China promoted the large-scale adoption of Electric Vehicles (EVs) in its 13th five-year plan; however, this target faces many obstacles. This paper analyzes the main barriers to widespread adoption of EVs through a survey in Shenzhen, which has the biggest EVs market share out of China’s major cities. Based on previous research, this paper conducted a new study using 406 approved questionnaires among 500 participants. Our study proposed five hypotheses to examine the main barriers to widespread adoption of EVs. The analysis was conducted using statistical method that included two-way frequency tables, chi-square test, and factor analysis. The results indicated that perception of advantages of EVs and access to recharging EVs remained the main barriers in large-scale penetration. Furthermore, our study revealed that a drop in financial incentives would not cause a significant decline in the future adoption of EVs. The study provides suggestions to car manufacturers and government policy advisors based on our analysis and discussion.

China planned to promote the large-scale adoption of Electric Vehicles (EVs) in the thirteenth five-year plan, however, this target faces many obstacles. This paper was trying to analyze the main barriers to widespread adoption of EVs through a survey in Shenzhen, which has the biggest EVs market share in China major cities. Based on previous scholarly findings, this paper conducted a new study which collected 406 approved questionnaires among 500 participants. The study proposed five hypotheses to examine the main barriers to widespread adoption of EVs. The analysis conducted by statistical methods: two-way frequency tables, chi-square test, and factor analysis. The results noted that perception of EVs advantages and recharging access remained the main barriers to EVs large-scale penetration. Besides, financial incentives drop would not cause a significant decline of EVs adoption in future. The study proposed suggestions to carmaker and government policy administrator on the analysis and discussion.