From Sensors Data to Urban Traffic Flow Analysis

Po, Laura; Rollo, Federica; Bachechi, Chiara; Corni, Alberto

doi:10.1109/isc246665.2019.9071639

Cited by 24 publications

(20 citation statements)

References 14 publications

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“…z ijt =1) there won't be any sensor at node i. Constraint (11) states that in each period only one sensor can move to node j and (12) declares that the sensor located in node i can only move to one of the other nodes in each period. Constraint (13) Where, F it represents the amount of energy stored in node i at the beginning of the period t and U ijt represents the energy needed to move the sensor from node i to node j at time t. The energy is stored by solar panels and used to move sensors to the traffic congestion areas. When a sensor is selected to move, it must have enough energy needed for movement.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Fakhouri

Soltani

2021

IEEE Access

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Section: Mathematical Modelmentioning

confidence: 99%

“… Infrastructure collapse: which occurs when a road collapses or node failure happens [12].  Traffic flow control: average speed of passing cars is used in travel time estimation [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Fakhouri

Soltani

2021

IEEE Access

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“…The sensors placed in Modena count the vehicles passing through them every minute and evaluate their average speed. We collect sensors data in real-time in a local Post-greSQL database, and the model interacts with it directly [6]. In our SUMO simulated map, we placed a "calibrator" near each traffic sensor.…”

Section: The Modelmentioning

confidence: 99%

Using Real Sensors Data to Calibrate a Traffic Model for the City of Modena

Bachechi

Rollo

Desimoni

et al. 2020

Advances in Intelligent Systems and Computing

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In Italy, road vehicles are the preferred mean of transport. Over the last years, in almost all the EU Member States, the passenger car fleet increased. The high number of vehicles complicates urban planning and often results in traffic congestion and areas of increased air pollution. Overall, efficient traffic control is profitable in individual, societal, financial, and environmental terms. Traffic management solutions typically require the use of simulators able to capture in detail all the characteristics and dependencies associated with real-life traffic. Therefore, the realization of a traffic model can help to discover and control traffic bottlenecks in the urban context. In this paper, we analyze how to better simulate vehicle flows measured by traffic sensors in the streets. A dynamic traffic model was set up starting from traffic sensors data collected every minute in about 300 locations in the city of Modena. The reliability of the model is discussed and proved with a comparison between simulated values and real values from traffic sensors. This analysis pointed out some critical issues. Therefore, to better understand the origin of fake jams and incoherence with real data, we approached different configurations of the model as possible solutions.

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“…The output of the traffic model is used by a customized version of the open source model Vehicular Emissions Inventory (VEIN) [18] to calculate the emission generated in each road of the city for each hour of the day. Finally, using the weather 48 hour forecast produced daily by ARPAE 7 , the GRAL air pollution model is executed to create predictions for the next 48 hours. A similar approach was followed in Santiago de Compostela, using meteorological forecast provided by Meteo-Galicia 8 .…”

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

confidence: 99%