Smart City Gnosys

Smart city article details

Title	A Dynamic Urban Mobility Index From Clustering Of Vehicle Speeds In A Tourist-Heavy City
ID_Doc	1612
Authors	Liponhay M.; Valenzuela J.F.; Dorosan M.; Dailisan D.; Monterola C.
Year	2023
Published	Applied Sciences (Switzerland), 13, 23
DOI	http://dx.doi.org/10.3390/app132312763
Abstract	The rapid urbanization of cities often brings about complex mobility issues, such as traffic congestion that, when unplanned, results in decreased productivity and quality of life. While many cities have adopted smart city initiatives to capture and monitor mobility, applying these in a developing country context remains a challenge when infrastructure and high-resolution spatial and temporal data are lacking. In this work, we use GPS data obtained from probe vehicles (a mix of public and private transport vehicles) within the city of Baguio, The Philippines, to develop and propose the Zone-based Speed Index (ZSI), a mobility index based on the speed clusters observed in this city. The ZSI dynamically infers monthly speed thresholds to classify zones as fast or slow and successfully captures the decrease in vehicle mobility associated with the impact of typhoons and holidays. Thus, it can be used to characterize urban vehicle mobility with high (hourly) resolution. Insights from the use of our dynamic mobility index are useful in the development and optimization of transportation systems, in monitoring the ease of vehicle mobility, and in the performance assessment of smart city initiatives, which are much needed in tourism hotspots. © 2023 by the authors.
Author Keywords	Baguio city; mobility index; smart city; tourist hotspot; transport

Similar Articles

Id	Similarity	Authors	Title	Published
25494	0.868	Almeida A.; Brás S.; Sargento S.; Oliveira I.	Exploring Bus Tracking Data To Characterize Urban Traffic Congestion	Journal of Urban Mobility, 4 (2023)
13158	0.863	Meegahapola L.; Kandappu T.; Jayarajah K.; Akoglu L.; Xiang S.; Misra A.	Buscope: Fusing Individual & Aggregated Mobility Behavior For “Live” Smart City Services	MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (2019)
9563	0.861	Leal D.; Albuquerque V.; Dias M.S.; Ferreira J.C.	Analyzing Urban Mobility Based On Smartphone Data: The Lisbon Case Study	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 486 LNICST (2023)
60232	0.861	Mehta V.; Chana I.	Urban Traffic State Estimation Techniques Using Probe Vehicles: A Review	Lecture Notes in Networks and Systems, 12 (2017)
58524	0.859	Bachechi C.; Po L.	Traffic Analysis In A Smart City	Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion (2019)
27311	0.858	Castro P.S.; Zhang D.; Chen C.; Li S.; Pan G.	From Taxi Gps Traces To Social And Community Dynamics: A Survey	ACM Computing Surveys, 46, 2 (2014)
46504	0.858	Ghosh N.; Sarkar U.; Nagesh P.	Review On Application Of Call Details Records (Cdrs) Data To Understand Urban Mobility Scenarios For Future Smart Cities	Springer Geography (2023)
23054	0.857	Chen C.; Zhang D.; Wang Y.; Huang H.	Enabling Smart Urban Services With Gps Trajectory Data	Enabling Smart Urban Services with GPS Trajectory Data (2021)
14531	0.857	Bolaños-Martinez D.; Bermudez-Edo M.; Garrido J.L.	Clustering Pipeline For Vehicle Behavior In Smart Villages	Information Fusion, 104 (2024)
52995	0.856	Perazzini S.; Metulini R.; Carpita M.	Statistical Indicators Based On Mobile Phone And Street Maps Data For Risk Management In Small Urban Areas	Statistical Methods and Applications, 33, 4 (2024)