How can we develop road space allocation solutions for smart cities using emerging information technologies? A review using text mining

Abstract

We propose a systematic literature review using text mining. The methodology has two main directions: (i) analyzing the evolution of concepts and topics explored throughout time by using word frequency counts in abstracts, and (ii) Detecting the main topics expressed in literature by using topic modeling and bigrams in full papers. Additionally, using text mining in reviews can help summarize, detect patterns, and better explore papers. The proposed methodology (and code) is reproducible and can be applied to different research fields. As a case study, we systematically review smart road space allocation literature and the role of big data and Information and Communication Technology (ICT) in promoting emerging urban solutions. Even though, recently there has been a significant rise in studies related to smart cities and big data, few papers consider these approaches in road space allocation. On the one hand, temporary solutions are mainly held in local access streets to achieve place-making and reclaim vacant public spaces. On the other hand, intermittent and dynamic solutions are mainly studied in arterials and highways and focused on traffic performance. Ultimately, new road space allocation proposals are not sufficiently studied where there is a high dispute and limited space to allocate space. Big data and smart technologies have the potential to collect real-time data and adapt the infrastructure according to fluctuations of demand and also in case of an unpredictable event.

Keywords

• Smart cities
• Road space planning
• Urban development
• Big data
• Artificial intelligence
• Literature review