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Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

Twenty-minute neighbourhoods highlight the importance of well-connected and mixed-used neighbourhoods and communities with proximate access to employment, essential services, public transport, and open spaces. Shorter distances together with re-prioritised public spaces encourage more active transport choices, resulting in public health benefits and reduced environmental pollution. Higher liveability brought about by mixed-use developments enables people to have equitable access to local facilities, amenities, and employment opportunities, promoting vibrancy, social cohesion, and intergenerational connections. The attributes of 20-minute neighbourhoods also combine to create places, that are acknowledged as friendly for all ages, address changing needs across the life course, and provide better support for the ageing population. Furthermore, there are indications that 20-minute neighbourhoods may be more resilient against many of the negative impacts of stringent public health protocols such as those implemented in periods of lockdown during the Covid-19 pandemic. In this article, we evaluate and compare planning policies and practices aimed at establishing 20-minute neighbourhoods in Melbourne (Australia) and Scotland (the UK). Using case studies, we discuss similarities and differences involved in using place-based approaches of 20-minute neighbourhoods to address 21st-century challenges in key areas of health and wellbeing, equity, environmental sustainability, and community resilience.

How can urban policies and planning approaches help in achieving a safer mobility and carbon reduction in the transport sector? The attention of planners and policy makers towards the promotion of sustainability and reduction of environmental impacts has grown in recent years. This paper investigates the role that Urban Planning plays in the long term towards a safer and climate friendlier mobility, highlighting the need for integrated approaches gathering spatial planning and mobility management. After a review of several urban policies and planning strategies, initiatives, and approaches, mainly based on the urban scale, the paper presents an urban regeneration case study leading to an increase of pedestrian accessibility at the neighborhood level. This can be seen as a support tool to foster sustainable, safe, and climate friendly mobility in cities. The results of the performed analysis show a dependency of accessibility from two different factors: the distribution of services and the capillarity of the soft mobility network, which can contribute to creating a more walkable space.

Route game is recognized as an effective method to alleviate Braess’ paradox, which generates a new traffic congestion since numerous vehicles obey the same guidance from the selfish route guidance (such as Google Maps). The conventional route games have symmetry since vehicles’ payoffs depend only on the selected route distribution but not who chose, which leads to the precise Nash equilibrium being able to be solved by constructing a special potential function. However, with the arrival of smart cities, the real-time of route schemes is more of a concerned of engineers than the absolute optimality in real traffic. It is not an easy task to re-construct the new potential functions of the route games due to the dynamic traffic conditions. In this paper, compared with the hard-solvable potential function-based precise method, a matched Q-learning algorithm is designed to generate the approximate Nash equilibrium of the classic route game for real-time traffic. An experimental study shows that the Nash equilibrium coefficients generated by the Q-learning-based approximate solving algorithm all converge to 1.00, and still have the required convergence in the different traffic parameters.

Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.

Freeway bottleneck areas are prone to congestion and have high accident risk. A variable speed limit provides technical support for alleviating congestion and improving traffic safety in such areas. The existing variable speed limit rules in the related literature have a single focus, and most of them do not give specific quantitative speed limits. In this study, a variable speed limit system suitable for freeway bottleneck areas was constructed. Variable speed limit rules under different levels of traffic congestion and adverse weather conditions were designed, and the parameters for freeways were defined. Then, the VISSIM microscopic traffic simulation software was used to build two bottleneck scenarios of a tunnel area and a merging area for simulation tests. The research shows that in these two scenarios, reasonable speed limits can effectively reduce roadway delays and improve the operational efficiency of bottleneck areas in certain traffic flow ranges (e.g., a medium flow of around 900 pcu/h/lane). Unreasonable speed limits in low flow inhibit freeway efficiency more significantly. When congestion has already formed with high flow, different speed limits have a limited effect on efficiency improvement. The research results reported in this paper can provide a theoretical reference for the design and practical application of variable speed limit systems in freeway bottleneck areas. This provides a certain contribution to sustainable traffic development.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.