Urban Mobility Demand Profiles: Time Series for Cars and Bike-Sharing Use as a Resource for Transport and Energy Modeling

Abstract

The transport sector is currently facing a significant transition, with strong drivers includingdecarbonization and digitalization trends, especially in urban passenger transport. The availability ofmonitoring data is at the basis of the development of optimization models supporting an enhancedurban mobility, with multiple benefits including lower pollutants and CO2 emissions, lower energyconsumption, better transport management and land space use. This paper presents two datasetsthat represent time series with a high temporal resolution (five-minute time step) both for vehiclesand bike sharing use in the city of Turin, located in Northern Italy. These high-resolution profileshave been obtained by the collection and elaboration of available online resources providing liveinformation on traffic monitoring and bike sharing docking stations. The data are provided for theentire year 2018, and they represent an interesting basis for the evaluation of seasonal and dailyvariability patterns in urban mobility. These data may be used for different applications, rangingfrom the chronological distribution of mobility demand, to the estimation of passenger transportflows for the development of transport models in urban contexts. Moreover, traffic profiles are at thebasis for the modeling of electric vehicles charging strategies and their interaction with the powergrid.

A visual tool for the analysis of usage trends of small and medium bicycle sharing systems

“…Shi et al cluster the stations based on similar behavior of trips [31]. Noussan et al also show information on bike sharing, but they only group per day of the week or month [32]. Cortez and Vázquez [30] also cluster the stations.…”

Scalability evaluation of forecasting methods applied to bicycle sharing systems

“…For larger systems, it makes sense to create clusters and analyze them collectively to get insights. Many studies have analyzed different forms to group stations based on metrics such as the activity score , as was performed by Froehlich et al [28] , similar behavior of trips [29] , availability levels [9] , grouping by day of the week or month [30] , among others. Cluster-level demand forecast has also been investigated by different authors.…”

The Complexity of Space Utilization and Environmental Pollution Control in the Main Corridor of Makassar City, South Sulawesi, Indonesia

“…Furthermore, the choice of using motorbikes and private cars for the purpose of traveling to health facilities is not only influenced by the efficiency of travel time and the economic costs but also by the distance to the destination location. Thus, the transportation sector is currently facing a transition, in this case urban passenger transportation [76]. Figure 7C shows the preferred mode of transport for the purpose of travel to industrial sites and Hasanuddin International Airport.…”