Application of wireless sensor networks to environmental monitoring for sustainable mobility

This paper presents a project idea for an innovative public transport system that may contribute to increase sustainable mobility and reduce the environmental impacts of transports. The proposed system is based on the use of low-emission small vehicles following flexible routes, which will be adapted in real time so to satisfy the customer requests, considering traffic congestion and availability of other transport services. The user will require the ride through a mobile application, which gives his/her location, and the system will provide the service considering all users' requests, vehicle availability, intermodal opportunities and traffic congestion. To implement this system, it is crucial to have real-time information on vehicle positions, other public transit supply, traffic and environmental conditions. Therefore, a distributed sensor network monitoring the environment is a fundamental component of the project. This paper focuses on the monitoring system that should be implemented so to obtain all real-time information necessary to guarantee a sustainable mobility in accordance with the project objectives. In particular, a Wireless Sensor Network (WSN) will be used to gather real-time traffic and environmental data to build descriptive and predictive models to plan the best routes in order to reduce road congestion and consequently urban pollution. The paper is developed in the context of the NETCHIP research project, which has been submitted to Italian PNR 2015-2020, Call n. 1735 of the 13th July 2017.

[1]  Antonella Ferrara,et al.  A Variable-Length cell road traffic model: Application to ring road speed limit optimization , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[2]  David Q. Mayne,et al.  Reachability analysis of discrete-time systems with disturbances , 2006, IEEE Transactions on Automatic Control.

[3]  C. O’Brien Statistical Learning with Sparsity: The Lasso and Generalizations , 2016 .

[4]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[5]  Franco Blanchini,et al.  Set-theoretic methods in control , 2007 .

[6]  David M. Broday,et al.  Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement—The Promise and the Current Reality , 2017, Sensors.

[7]  Yang Lu,et al.  Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts , 2017 .

[8]  G. Klunder,et al.  Improvement of Network Performance by In-Vehicle Routing Using Floating Car Data , 2017 .

[9]  Antonella Ferrara,et al.  Supervisory multi-class event-triggered control for congestion and emissions reduction in freeways , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[10]  Petros Spachos,et al.  Self-Powered Wireless Sensor Network for Environmental Monitoring , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[11]  Jean-Patrick Lebacque,et al.  Introducing Buses into First-Order Macroscopic Traffic Flow Models , 1998 .

[12]  Antonella Ferrara,et al.  Freeway Traffic Modelling and Control , 2018 .

[13]  Harilaos N. Koutsopoulos,et al.  A microscopic traffic simulator for evaluation of dynamic traffic management systems , 1996 .

[14]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL.. , 1993 .

[15]  Alfonso García-Cerezo,et al.  A Wireless Sensor Network for Urban Traffic Characterization and Trend Monitoring , 2015, Sensors.

[16]  Bruno Scarpa,et al.  Data Analysis and Data Mining: An Introduction , 2012 .

[17]  Rita Tse,et al.  A portable Wireless Sensor Network system for real-time environmental monitoring , 2016, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[18]  Luiz Antonio Nogueira Lorena,et al.  Customers' satisfaction in a dial-a-ride problem , 2009, IEEE Intelligent Transportation Systems Magazine.

[19]  E. Cascetta,et al.  A DAY-TO-DAY AND WITHIN-DAY DYNAMIC STOCHASTIC ASSIGNMENT MODEL , 1991 .

[20]  Antonio Polimeni,et al.  A Model to Simulate Multimodality in a Mesoscopic Dynamic Network Loading Framework , 2017 .

[21]  Luca D'Acierno,et al.  Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System , 2009, Eur. J. Oper. Res..

[22]  D. Rainham A wireless sensor network for urban environmental health monitoring: UrbanSense , 2016 .

[23]  Kris Braekers,et al.  Typology and literature review for dial-a-ride problems , 2017, Ann. Oper. Res..

[24]  Antonella Ferrara,et al.  Congestion and Emissions Reduction in Freeway Traffic Networks via Supervisory Event-triggered Control , 2017 .

[25]  Antonella Ferrara,et al.  Traffic control via moving bottleneck of coordinated vehicles , 2018 .

[26]  Markos Papageorgiou,et al.  Traffic Simulation with METANET , 2010 .

[27]  C. Daganzo THE CELL TRANSMISSION MODEL.. , 1994 .

[28]  Serge P. Hoogendoorn,et al.  Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors , 2017 .