Real-Time Traffic Estimation of Unmonitored Roads

Bellini, Pierfrancesco; Bilotta, Stefano; Nesi, Paolo; Paolucci, Michela; Soderi, Mirco

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.000-6

Cited by 14 publications

(15 citation statements)

References 11 publications

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“…). Figure 8 illustrates the daily commuting proneness distribution (i.e., the morning and the afternoon weight vectors), used in our experiments, extracted from the DISIT traffic model [13], when the size of time intervals is equal to one hour. For instance, states that time interval compared to interval , has more , and compared to interval , has less commuting proneness .…”

Section: Analysis Methodsmentioning

confidence: 99%

“…Then, the number of people who are picked-up (dropped-off ) at the stop in the morning time interval is equal to the sum of the number of people that begin (end) their trip and those who start their next trip stage (finish their current trip stage) at which can be calculated as in (10) ((11)). Likewise, the number of people who are picked-up and dropped-off at the stop in the afternoon interval can be calculated as in (12) and (13). Table 2 presents the model input setup.…”

Section: Figure 8: Examples Of the Morning And The Afternoon Weight Vmentioning

confidence: 99%

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Analyzing Public Transportation Offer wrt Mobility Demand

Arman

Bellini

Nesi

et al. 2019

Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Commu

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An important problem in improving mobility services consists in analyzing the transportation offer with respect to the demand of mobility. The purpose is always the assessment of the service for its improvements. This activity can be approached having all the historical data, while in most cases is not realistic due to the expensive process of data collection and lack of details about the movements of travelers at the bus stops in terms of pickup and drop-off for each bus line. To deal with these issues, in this paper, a model is provided to support mobility analysis in public transport networks. Our model operates first by analyzing the service offer, provided by mobility operators, and the service demands. Then, the model allows to evaluate the number of people who are picked-up and dropped-off at a stop. The performance of the model has been validated by comparing the observed values obtained from a field observation. The research and tool have been developed in the context of MOSAiC research project partially funded by Tuscany Region, with DISIT lab, ALSTOM, Municipia/Engineering, TAGES and CNIT research centers.

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Section: Analysis Methodsmentioning

confidence: 99%

Section: Figure 8: Examples Of the Morning And The Afternoon Weight Vmentioning

confidence: 99%

Analyzing Public Transportation Offer wrt Mobility Demand

Arman

Bellini

Nesi

et al. 2019

Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Commu

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides, the Air Pollution Spatial Interpolation and the Traffic Model demands largescale data processing technologies based on the map-reduce paradigm. The different cities make use of HPC technologies by using different solutions: [] In Florence, Pisa, Livorno and Santiago, a real time traffic flow reconstruction has been realized with the Disit Lab traffic Model by exploiting the DISIT HPC resources [5].…”

Section: Software and Hpc Architecturesmentioning

confidence: 99%

“…In Modena, a traffic flow model has been implemented based on traffic sensors real data [6]- [8]. We used the Simulation of Urban MObility (SUMO) tool 5 that is a microscopic, collisionfree, space-continuous and time-discrete simulator. Every step of the simulation corresponds to one second in reality.…”

Section: Traffic Modelmentioning

confidence: 99%

“…Figure 4 shows a snapshot of 1 hour prediction for air pollution in Santiago (on the left) and Zaragoza (on the right). In Firenze, Pisa, Livorno, a realtime visual self-adaptive solution for the vehicular traffic flow reconstruction developed by the DISIT Lab, has been used [5]. This traffic model allows to visualize in a map the real-time state of traffic and produces the output files containing the reconstructed traffic flow data for the road-segments considered.…”

Section: Air Quality Model In Italy and In Spain Local Regional Enmentioning

confidence: 99%

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