# A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City
## 1 Introduction  
Globalization and population growth have rapidly accelerated urban development, often without adequate planning, leading to unintended negative impacts on the environment, economy, and society [1, 2, 3]. Among the various consequences, issues related to mobility, such as traffic congestion and costs of driving, stand as a significant obstacle to the economic growth of developing nations, resulting in far-reaching social and economic ramifications [4, 5, 6, 7, 8]. For instance, one study in the United States revealed that the average driving costs per driver encompass both direct (such as purchase, maintenance, and fuel) and indirect costs (including wasted time, fuel, and carbon emissions from congestion). Surprisingly, the indirect costs were found to constitute a substantial portion, amounting to approximately one-third of the total cost of car ownership in the country [9]. Similarly, a survey study conducted in a developing city in Bangladesh [10] emphasized the socioeconomic impacts of traffic congestion in ports and industrial zones, highlighting its direct effects on stress levels. The study estimated a staggering daily economic loss of USD 2.01M, considering delayed costs, fuel loss, pollution, and the loss of vehicle operators. Furthermore, the study's stress assessment, with an overall score of \(3.23\pm 0.71\), indicated a significant impact on respondents across various sociodemographic groups. Recognizing the challenges in transport stemming from rapid urbanization, many cities have embraced smart city initiatives, which have gained significant momentum since 2012 and peaked in 2018 [11]. The primary function of strategic smart city planning is to assist urban areas in achieving sustainable development objectives, addressing a variety of present and future difficulties and challenges in domains such as transportation, water management, waste disposal, energy, living conditions, and governance [11]. For this purpose, assessing the impact of smart city initiatives on quality of life across various levels and dimensions is crucial. Despite the lack of a universal definition for a smart city [12; 13; 14], six domains or subindices are generally considered to be a smart city index's basic components. These are economy, environment, governance, living, mobility, and people [11; 15; 16]. Each domain or subindex entails a number of indicators to enable us to assess the performance of smart city initiatives [17; 18], whose results are then used as a basis for the introduction of policy interventions, urban planning, and the better management of smart cities. To address issues on mobility, including traffic congestion, several approaches have been taken, such as synergy of the transport system [19], traffic signal control [20], and predictive analytics [21], among many others. Additionally, extensive measures of a transport or mobility index have been proposed for different purposes under different perspectives, such as urban planning, environmental impact, traffic system performance monitoring, urban development, human mobility patterns in relation to work, and transport sustainability [22; 23; 24]. We note, however, that calculations of the mobility index require the availability of high-resolution and up-to-date spatial and temporal data from various indicators, such as travel cost, travel time, and road network, among others, which are not readily available in developing countries. Moreover, the growing digital divide, as noted by the United Nations, between highly urbanized and developing countries [25] is deemed to have contributed to the widening gap of applications of smart city initiatives--now primarily focusing on more developed regions. Consequently, the emphasis on smart city development has shifted from infrastructure build-up to the provision of smart city services, assuming the pre-existence of supporting infrastructure [26].