Energy-Saving Methods for Urban Travel and Public Transportation in Smart Cities

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

Urban Travel Behavior and Socio-Spatial Issues in the Mena Region: What Do We Know?

Unlike literature and studies coming from high-income or Western countries, the existing conducted on the Middle East and North Africa fail to draw a nearly complete image of the characteristics of passenger travel behaviors in the urban areas of the region. This gap necessitates a holistic review of the previous studies and comparing their results of those of the international findings. This paper summarizes the status of urban travel behavior studies on the MENA region under eight categories of socioeconomics, land use, perceptions and attitudes, urban sprawl, neighborhood design, public transportation use, active mobility, and new technologies and concepts. Descriptive literature review and desk research depicts both lack of research results or data and differences between the behaviors in the MENA region and the Western countries. Moreover, based on the background review, this paper provides a list of recommendations for having more sustainable mobility in the MENA region.

Smart cities need environmental consciousness and more social responsibilities as an outcome of COVID-19 – reflections from urban road commuters

Purpose A lesson has been learned from the pandemic experience that less damages to the environment and realizing more social responsibilities would be the direction of the post-pandemic period globally. The purpose of this study is to focus on identifying the appropriate determinants of the proposed urban travel behavior model to develop Smart Mobility in Smart Cities to protect the environment. Potential to realize Smart Cities with infrastructure development has been explored in this study if road users are keen to combat climatic change which is clear from the challenges of flattening the infection rate through the enforcement of rules and regulations by the various government. Design/methodology/approach The proposed urban travel behavior model includes sub-drivers for each of the main drivers in the theory of interpersonal behavior (TIB). These sub-drivers emphasize in forming intentions to perform the behavioral changes while driving on urban roads during COVID-19 and post-pandemic periods. A primary online survey was conducted among road commuters in the most crowded place in Malaysia, the Greater Kuala Lumpur. A total of 383 respondents who frequently drive on road during the past one year were surveyed for this study. This data analysis of this quantitative study applied a partial least squares-structural equation modeling approach to determine the significant findings and results. Findings The significant findings of the study reveal that environmental consciousness and timely deviation in driving during traffic congestion are positively and significantly influencing the travel behavior performance (TBP) of commuters on urban roads. On the other hand, wet conditions due to weather, narrow road infrastructure and habits of road commuters are negatively influencing TBP. Social responsibility is positively and significantly influencing TBP through the mediating effect of the intention of road commuters’ behavior. Research limitations/implications The current environmental concerns and societal adherence efforts in breaking the chain of the infectious COVID-19 among people can be manifested to develop Smart Cities with less air and noise pollution in the future. In this context, the present study proposes an urban travel behavior model and tests for its suitability of a greener and cleaner environment for the benefit of future generations. The limitation of the present study is that travel hazards are not included in the framework, as it is a topic of its own volume. Originality/value It is timely to implement Smart Mobility on road business models for Smart Cities as the consequences of the pandemic make us to realize the importance of environmental concerns and the social responsibilities of everyone. TIB considers four drivers, namely, attitude, subjective norm, affect and habit which induce intention to perform behavioral decisions. The novelty of the present study is the development of sub-drivers for these four drivers in the context of the urban travel behavior model.

Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective

Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.