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Dynamic wireless charging (DWC) technology has the potential to promote the uptake of electric freight vehicles (EFVs) by allowing charging on the move. This paper presented a joint DWC lane deployment and operation model based on the trajectory data of actual EFVs. The model was formulated as a multi-objective mixed-integer programming model, with objectives to maximize saved charging time, minimize charging cost, and minimize negative impact on traffic. The model considered various constraints related to investment, facility construction, and charging decisions of EFV drivers. It was first tested on a small network and was further applied into a large-scale scenario in Beijing. The results suggested that DWC could reduce charging time by approximately 0.08–0.11 h per EFV per day within an investment limit of 67 million dollars. The deployment of DWC can be long-term profitable, resulting in a net value of 140.83 million dollars over a 25-year period. [ABSTRACT FROM AUTHOR]

Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China's dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas. [ABSTRACT FROM AUTHOR]

• Trips by dockless bike-sharing show the highest travel satisfaction. • The longer assess time is related to lower satisfaction with dockless bike-sharing. • Travelers have higher satisfaction with dockless bike-sharing as the primary mode. • Pro-public transit attitude relates to higher satisfaction of dockless bike-sharing. • Residential road network has no effect on satisfaction with dockless bike-sharinga. Using survey data collected among residents in Beijing, this paper presents an investigation of the difference in travel satisfaction between dockless bike-sharing and other travel modes. The effects of individual, spatial and trip attributes on travel satisfaction with dockless bike-sharing are also identified. Our analysis adds to the empirical support for higher satisfaction with trips by dockless bike-sharing than trips by private bicycles. The assess time for shared bicycles is negatively associated with travel satisfaction with dockless bike-sharing. In addition, travelers have higher satisfaction with dockless bike-sharing when used as the primary mode than as the first-mile/last-mile solution. Travel-related attitudes tend to play a more significant role in travel satisfaction with dockless bike-sharing than residential built environment. Travelers with a preference for bicycles and public transport and those who value the health or environmental influence of travel tend to evaluate dockless bike-sharing travel as more satisfying. [ABSTRACT FROM AUTHOR]

• Analyze impacts of shared mobility on property value in the vicinity of subway stops; • Didi's ride service steepens subway house price gradient by 1.26 percentage points; • Bikesharing service flattens the house price gradient by 2.14 percentage points; • Didi is more of a substitute, while bikesharing is more of a complement to subway; • Effects of Didi and bikesharing are more salient in areas with poor commuting conditions; With the rapid development of smart phones and mobile internets, shared mobility services have become increasingly popular among travellers, and may influence housing values through their effects on people's demand on subway services. This paper makes one of the first attempts to investigate the aggregate impact of different shared mobility services on housing values near subway stations, by conducting an empirical analysis on a dataset of apartment resale records in Beijing. We find that, the on-demand ride service offered by transportation network companies (like Didi) is more of a substitute than a complement to subway, leading to a reduction in house price in the vicinity of subway stations. In contrast, dockless bikesharing service brings about a greater complementary effect than substitution effect on subway usage, resulting in house price appreciation in the proximity of subway station. The effects are found to be heterogenous at different temporal and spatial scales. [ABSTRACT FROM AUTHOR]

• Rail accessibility is measured by the market access approach. • Non-linear association between travel satisfaction and accessibility is observed. • Residential self-selection and social group strata differentiation play a moderating role. Rail stations located in residential environments provide significant amenities to the travel-related satisfaction of residents. Using an individual survey conducted in Beijing in 2013, this paper applies multilevel models to explore the association between travel satisfaction and rail accessibility, controlling for residential self-selection, socio-demographics, and neighborhood characteristics. We differentiate between station adjacent and non-adjacent residential locations to reveal non-linear association over space. We consider the effect of rail accessibility changes on travel satisfaction. The results suggest that rail accessibility improvements generate significant impacts on residents' satisfaction with commuting convenience. The findings suggest the important role of the spatial placement of rail transit infrastructure on people's subjective wellbeing with travel. [ABSTRACT FROM AUTHOR]