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• Station-based one-way carsharing benefits in a developing country. • The results in Shanghai are compared with the relevant research results in different cities in the world. • Operation data and questionnaire survey are combined to evaluate the carsharing's impact on vehicle ownership. • Characteristics of users affected by carsharing are analyzed and the key factors are identified. • Shanghai carsharing mainly suppresses vehicle growth by reducing individual willingness to buy a car. Carsharing is considered an important alternative to the private car. Based on the studies in Europe and North America, it is agreed that carsharing can reduce private car ownership. However, due to the differences in the development stages and characteristics of cities and transportation, it is unknown whether carsharing can also decrease car ownership in China, which results in a gap in the evaluation of carsharing in China. This paper takes the carsharing project EVCARD in Shanghai as an example. Based on survey data on vehicle ownership from users and actual operations data, the impacts of carsharing on selling cars, giving up and delaying purchasing a car in Shanghai are quantitatively analyzed. The results in Shanghai are compared with the relevant research results in different cities around the world. The results show that: (1) the carsharing project in Shanghai also has the benefit of reducing private car ownership. Each carsharing vehicle can replace 4.56 private cars. However, unlike in European and North American cities, Shanghai carsharing mainly suppresses vehicle growth by reducing individual willingness to buy a car, because the proportion of cars sold owing to carsharing is obviously lower than in Europe and North America. (2) For giving up car purchases, carsharing mainly suppresses the first car in the family. (3) Good public transport and carsharing services can substantially improve the substitution benefits of carsharing for private cars. If carsharing can reach the service level of "whenever and wherever carsharing is available", the proportion of users who sell, give up and delay buying cars will increase from 0.78%, 30% and 31% to 12%, 40% and 47% respectively. The findings in the study provide a quantitative basis for assessing the carsharing benefits on the reduction of vehicle ownership in China, and also provide a useful reference for evaluating carsharing benefits in other developing countries. [ABSTRACT FROM AUTHOR]

Private transport constitutes the prerequisite for the freedom of mobility and facilitates accessibility for activities of older adults. Abundant studies have been conducted on the association between car ownership and life satisfaction in Western countries where cars dominate private transport. However, as the most prevalent and conventional private transport in China, cars and e-bikes have not received adequate attention. Using 2015 and 2018 datasets from the China Health and Retirement Longitudinal Study, we apply the Propensity Score Matching–Difference in Differences to investigate how private transport influences the multi-dimensional life quality of older adults. Furthermore, we focus on the mediation effect of social participation on the above causal effect. Our findings reveal that car and e-bike ownership have a causal impact on older adults' life satisfaction, while the influence of e-bike ownership is multi-dimensional. The relationship between car ownership and life satisfaction is mediated by social participation regarding exercise and voluntary activities. However, the mediation effects of mahjong and exercise activities are statistically significant between e-bike ownership and the multi-dimensional life quality of older adults. Understanding the life quality of older adults related to private transport is essential for guiding travel mobility and the life quality of older adults in China. • The paper reveals the causal effect of private transport on multi-dimensional life quality among older adults, including life satisfaction, happiness, loneliness, and depression. • The paper explores the mediation effect of social participation on the relationship between car/e-bike ownership and multi-dimensional life quality. • Using 2015 and 2018 datasets from the CHARLS, the methods of Propensity Score Matching–Difference in Differences and Mediation Analysis are applied in the study. • The results show that both car and e-bike ownership have causal effects on the life satisfaction of older adults, while the influence of e-bike ownership is multi-dimensional. • Participation in mahjong activity mediated the relationship between e-bike ownership and the life quality of older adults, while not for car ownership. [ABSTRACT FROM AUTHOR]

Incorporating mobile phone data, known for its high spatial and temporal resolution and extensive population coverage, into Activity-Based Models (ABM) for understanding individual travel and activity behaviors is a current research hotspot. However, applying them in ABM building is not straightforward because they miss key information — activity type. In this paper, we present an activity types detection method named the Teacher–Student Cycling model based on a self-training framework. Our model can combine travel survey data with extensive prior knowledge and mobile phone data. We introduce two different resolutions of mobile phone datasets to test the model performance. Our results show that our proposed model can achieve good performance on datasets of different resolutions. Our model, one of the semi-supervised learning models that uses a mixture of labeled and unlabeled knowledge performs better than the supervised learning model that uses labeled knowledge alone. In addition, our model improves the overall detection accuracy by 7% over the second-best semi-supervised learning model and improves the detection accuracy of secondary activities by up to 17%. Our model can be valuable in generating daily activity schedules for agent-based models. • Using a semi-supervised self-learning model to explore the activity types hidden in the mobile phone. • Activity-travel survey is introduced as prior knowledge in the detection model. • The model's performance is evaluated using various resolution data. [ABSTRACT FROM AUTHOR]