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Freight demand (FD) is closely related to industrial structure (IS), and the determination of this relationship is conducive to the development of the transport industry. This research proposed an input–output structural decomposition analysis (IO-SDA) model to explain the growth of FD with the changes in economic aggregate, structural factor, transport demand intensity, and industry connection. This method enables us to quantify and compare the contribution of industrial development to FD growth of five freight modes and determine the role of the structural factor in the growth of FD. An empirical study was conducted based on China’s 2002 to 2017 input–output tables. The results showed that the construction industry was the most important sector leading to the growth of FD, and the role of the service industry in promoting the growth of FD became more evident. There was a clear trend of concentration to the road freight industry, which may undermine the coordination of the transport industry. The structural factor became an important factor in reducing FD in 2007 to 2017. Based on the findings, it is suggested that China should promote the upgrading of IS and emphasize the importance of green road transport and multimodal transport.

Geothermal energy piles are an environmentally friendly energy source and an innovative approach to melt snow on the bridge surface and minimize or eliminate the use of deicing salt. However, the application potential of energy piles for bridge deicing or snow melting has not been fully explored for different climates. In this study, the feasibility of using energy piles for bridge deicing in eight cities of the United States was investigated. Temperature response function (G function) method was validated and used to estimate the extracted heat from energy piles installed in the soils with different thermal properties, which was used to heat the bridge deck during snowing. The results of numerical simulation and statistical analysis confirm that the performance of the geothermal deicing system depends on the weather conditions during snowing and thermal properties of soils. The coverage rate of the geothermal system (percentage of snowing time that the geothermal deicing system can keep the bridge surface above 0°C) increases with the increasing air temperature and thermal diffusivities of soils, and decreases with the increasing precipitation rate and wind speed. This deicing method is promising in cities with higher average air temperature and low precipitation rate during snowing.

The rapid development of metro transit systems brings very significant energy consumption, and the high service frequency of metro trains increases the peak power requirement, which affects the operation of systems. Train scheduling optimization is an effective method to reduce energy consumption and substation peak power by adjusting timetable parameters. This paper proposes a train timetable optimization model to coordinate the operation of trains. The overlap time between accelerating and braking phases is maximized to improve the utilization of regenerative braking energy (RBE). Meanwhile, the overlap time between accelerating phases is minimized to reduce the substation peak power. In addition, the timetable optimization model is rebuilt into a mixed integer linear programming model by introducing logical and auxiliary variables, which can be solved by related solvers effectively. Case studies based on one of Guangzhou Metro Lines indicate that, for all-day operation, the utilization of RBE would likely be improved on the order of 23%, the substation energy consumption would likely be reduced on the order of 14%, and the duration of substation peak power would likely be reduced on the order of 66%.

Under worldwide environmental stress, zero-emission vehicles (ZEV) are rapidly coming to market. However, it is not clear how such vehicles reduce vehicular emissions at a spatially explicit level, which is crucial for developing specific policies. This study proposed a quantitative approach to estimate the effectiveness of ZEVs in reducing emissions to support investment decisions promoting the use of ZEVs. The approach uses existing statewide travel demand and mobile emission models in an integrated framework. Scenarios are designed to measure the emissions reduction effects of ZEVs at different spatial scales (statewide, county, and roadway) and characteristics (densely and sparsely populated counties) and with various levels of market penetration and driving range limits. Results show significant spatial differentiation of the impact of ZEV deployment from county to roadway levels. Offering greater spatial detail and new insights on decision-making processes, this study described an integrated tool for identifying effective strategies for ZEV implementation.

Algorithms for current automatic train operation (ATO) focus mainly on reducing the mechanical energy of motion for a single train within an existing timetable. However, the reuse of regenerative energy is another factor that contributes to energy consumption and conservation in multitrain networks. To improve regenerative energy receptivity and energy savings in a bidirectional metro transit network, this study formulated a coordinated train control algorithm that was based on genetic algorithm techniques. The energy saving potential of different station departure time intervals between two opposing trains (synchronization time) was tested. Simulation on the Visual C++ platform demonstrated that the algorithm could provide an optimal train speed profile with better energy performance while also satisfying operational constraints. Different synchronization times have different optimization ratios. This research was another step to facilitate the development of an ATO control algorithm that considers overall energy consumption. Increased knowledge of the influence of synchronization time at stations on energy consumption in regenerative multitrain networks will also aid in the design of more energy-efficient timetables.

To unveil the influencing factors of drivers’ curbside charging choice behavior and provide valuable insights for the deployment of curbside charging infrastructure, a stated preference survey involving 402 respondents was conducted in Chongqing, China. A correlated mixed logit model was then employed to fit the survey data. The proposed model of drivers’ curbside charging choice behavior focuses on three factors: personal attributes, vehicle characteristics, and charging facility attributes. Findings suggest that individuals with higher monthly incomes, a positive attitude, and older age are more inclined to choose curbside chargers, while public institution employees and younger individuals are less likely to do so. In addition, factors such as lower remaining range, longer parking durations, proximity to residential areas, and the availability of fast chargers positively influence the likelihood of selecting a curbside charger. Moreover, unobserved heterogeneity factors, such as monthly income, parking duration, and pricing, along with their interactions, further underscore the complexity of the factors influencing curbside charger choice behavior.

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

Intelligent Battery Swapping Services (BSSs) present an innovative solution to the challenges of charging, safety hazards, and disorganized battery management encountered by electric micromobility vehicles (EMVs). Although BSSs have gained traction in the business-to-business domain, their acceptance in the business-to-customer sector remains uncertain. This study leverages the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) combined with a structural equation modeling framework to discern the adoption intentions toward BSS among individual drivers. Drawing from a survey of 434 EMV users in Jiangsu Province, China, we analyzed the relationship between latent variables, while also evaluating the moderating effects of sociodemographic and transport-related factors via multi-group analysis. The findings revealed a model with impressive explanatory power, accounting for approximately 54.8% of the variance in intention. Notably, social influence emerged as the most potent influencer on intention, trailed by effortlessness expectancy, price sensitivity, and performance expectancy. Intriguingly, both price sensitivity and technology anxiety exhibited a negative correlation with intention. Furthermore, variables such as gender, age, income, riding purpose, and riding frequency were found to significantly shape users’ intentions to embrace BSS. This research offers valuable insights for policy makers aiming to promote BSS adoption among EMV users and encourage the EMV market’s growth and sustainability.