# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 1 Introduction  
Data explosion has been posing unprecedented opportunities and challenges to our cities. To utilize big data to better allocate urban resources for the purpose of improving life quality and city management, the concept of Smart City was introduced as an emerging topic to society and the research community. To fulfill the increasing demand in data processing, cloud computing is expected not to be able to fully support the computing services, and thus, the community has realized the need for a new form of computing, i.e., edge computing (EC). Edge computing processes sensor data closer to where the data are generated, thereby balancing the computing load and saving network resources. At the same time, edge computing has the potential for improved privacy protection by not transmitting all the raw data to the cloud datacenters.  
Transportation is a key building block of our city, and the concept of intelligent transportation systems (ITS) is a critical component of Smart City. Research studies and engineering implementations on ITS have been attracting attention in recent years. According to the Web of Science, we surveyed the number of publications with the keywords _intelligent transportation_, _intelligent vehicle_, or _smart transportation_. It can be seen from Figure 1 (the orange line) that the number of publications on ITS increased from 2011 to 2019 by almost five times. We also searched the publications with keywords _edge computing_, and the trend is similar to that of ITS (the blue line). However, when we searched the combined keywords, including _edge computing + transportation_ and _edge computing + vehicle_, the relatedpublications were in very small numbers: 21 publications in 2011 and 199 publications in 2019.  
From the numbers themselves, we might conclude that EC and ITS are two unrelated research fields. Nevertheless, with careful investigation, this is not the case at all. In several of the most highly cited survey papers on edge computing [1; 2; 3], the summarized key applications of edge computing included smart transportation, connected vehicle, wireless sensing, smart city, traffic video analytics, and so on. These are core research topics in ITS. They envisioned that, with the wide spread of mobile phones, sensors, network cameras, connected cars, etc., cloud computing would no longer be suitable for many city-wide applications. Edge computing would be able to leverage the large amount of data produced by them. On the other hand, we observed more and more studies and articles on applying edge computing to ITS (e.g., [4; 5; 6; 7; 8; 9]). Though still in a relatively small number, these studies are innovative and show great potential to branch out into new ideas and solutions.  
Therefore, the statistics in Figure 1 actually unveil a big gap and high demand in future research on the combination of EC and ITS. In this paper, we conduct a survey on recent advances in ITS, especially ITS sensing technologies; we then propose the challenges in ITS sensing and how EC may help address them, as well as future research opportunities in applying EC to the area of ITS sensing. Note that EC could benefit not only ITS sensing but also other components of ITS, such as data pre-processing, traffic pattern analysis, and control strategies; however, in this paper, we mainly focus on EC's application in ITS sensing and present a detailed survey. The structure of the content in this paper is displayed in Figure 2.  
Figure 1: The number of publications from 2011 to 2019 on the topics of (1) edge computing, (2) intelligent transportation systems, and (3) edge computing + transportation. The statistics are from the Web of Science.