Big Data Processing and Storage Framework for ITS: A Case Study on Dynamic Tolling

Abstract

Urban and national road networks in many countries are severely congested, resulting in increased travel times, unexpected delays, greater travel costs, worsening air pollution and noise levels, and a greater number of traffic accidents. Expanding traffic network capacities by building more roads is both extremely costly and harmful to the environment. By far the best way to accommodate growing travel demand is to make more efficient use of existing networks. Portugal has a good but underused toll highway network that runs near to an urban/national road network that is free to use but congested. In choosing not to pay a toll, many Portuguese drivers are apparently accepting greater risk to their safety and longer travel times. As a result, the urban/national road network is used far more intensively than projections anticipated, which raises maintenance costs while increasing levels of risk and inconvenience. The main idea behind the work presented here, is to motivate a shift of traffic from the overused network to the underused network. To this end, a model for calculating variable toll fees needs to be developed. In order to support the model, there is the need to accurately predict the status of road networks for real-time, short and medium term horizons, by using machine learning algorithms. Such algorithms will be used to feed the dynamic toll pricing model, reflecting the present and future traffic situations on the network. Since traffic data quantity and quality are crucial to the prediction accuracy of road networks’ statuses, the real-time and predictive analytics methods will use a panoply of data sources. The approach presented here, is being developed under the scope of the H2020 OPTIMUM, a European R&D project on ITS.

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

“…Even so, within the context of toll systems, and apart from the work done in the OPTIMUM project (e.g. (Guerreiro, Figueiras, Silva, Costa, & Jardim-Gonçalves, 2016), (Figueiras, et al, 2016) , (Figueiras, Guerreiro, Silva, Costa, & Jardim-Gonçalves, 2018), (Petalas, Ammari, Georgakis, & Nwagboso, 2017), (Ramos, et al, 2018)), there is a limited number of works worth mentioning. In (Kumar & Machado, 2018), the authors propose a tollgate system enhanced by Internet of Things and Big Data technologies, in order to minimize traffic density, reduce stop time at toll gates and detect frauds.…”

“…Data collection and harmonization tasks performed in OPTIMUM and their outcomes are described in detail in several scientific publications and conference proceedings (Guerreiro, Figueiras, Silva, Costa, & Jardim-Gonçalves, 2016), (Figueiras, et al, 2016), (Figueiras, Guerreiro, Silva, Costa, & Jardim-Gonçalves, 2018). The OPTIMUM's Data Processing Pipeline is conceptually represented in Figure 4.…”

Clustering Activity–Travel Behavior Time Series using Topological Data Analysis

“…For example, Jandui Silva (2015) proposed to use data collected by the drivers using apps like Waze and Google Maps to improve urban mobility. Figueiras et al (2016) proposed to aggregate big data from various sources for implementing dynamic tolling to reduce traffic congestion. Other studies used big data for revealing individuals mobility patterns (Calabrese et al, 2013;Candia et al, 2008;Kwan, 2000;Huang et al, 2018).…”