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Grant: Discovery Grants Program - Individual

École de technologie supérieure

$36,000.00

Intelligent Transportation Planning

EF 2024-25

Résumé
The road-transportation sector represents nowadays a major contributing factor to the count of Green-House Gas emissions. This contribution is mostly due to the large ratio of vehicles powered by traditional fossil fuels, and by the constant rise of the number of vehicles on the roads. The transportation sector has, however, the perspective of undergoing an authentic technological revolution, labelled as Transportation 4.0 (Tr4), whose main elements are: 1) the transition towards the electrification of the transportation system, 2) the adoption of self-driving and Autonomous Vehicles (AVs), 3) the advances in Information Technology, 4) the widespread collection and storage of transportation data, and 5) the instantaneous and distributed access to computing resources. While the technological advances implicit in the Tr4 set up the conditions for more sustainable and efficient transportation systems, their management becomes dramatically more challenging. When surveying the scientific literature, one finds that there is a considerable lack of methodologies to address the challenges presented by the Tr4, both in the context of transit systems, as well as in freight distribution. Therefore, the main objective of this proposal is to develop decision-support tools to enable the transportation industry to fully profit from the opportunities given by the Tr4. The proposed methods are rooted in the fields of operations research and artificial intelligence. This research program is organized around two themes. The first theme considers transit systems and focuses on planning problems arising in the integration of on-demand services with traditional transit. This is done by adopting a modeling framework named Semi-flexible Transit System (STS). STSs aim to provide demand-responsive service within the framework of regularly scheduled transit. Combining characteristics of traditional and on-demand systems, STSs require an extremely complex planning activity. More specifically, this proposal aims to develop the methodology to incorporate a fleet of AVs in the operations planning of STSs, and build a simulation framework to analyze the performance of a system integrating STSs with traditional transit, and its impact on the mobility at a city-scale. The second theme considers freight transportation and focuses on stochastic and dynamic routing problems arising in the context of the Tr4. More specifically, this second theme aims to develop decision-support tools for real-time routing of vehicles under stochastic and dynamic traffic conditions, as well as dynamic policies for routing and charging operations when operating a fleet of battery-equipped electric vehicles. By developing tools to enable the Tr4 revolution, the anticipated impact of this research program is to provide more sustainable and more equitable transportation services, both in terms of public transit, as well as freight distribution.
Programme
Discovery Grants Program - Individual
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2022-03916
Name nom
Errico, Fausto
Department département
Construction
Organization id
53
Institution établissement
École de technologie supérieure
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
36000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1509
Committee name en
Civil, Industrial and Systems Engineering
Committee name fr
Génie civil, industriel et des systèmes
Area of application code
700
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Transportation systems and services
Area of application fr
Services et systèmes de transport
Research subject code
1601
Research subject group en
Industrial engineering
Research subject group fr
Génie industriel
Research subject en
Operations research and management science
Research subject fr
Recherche opérationnelle et science de la gestion
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Intelligent Transportation Planning
Keywords
semi-flexible transit systems; autonomous vehicles; electric vehcles; routing problems; stochastic programming; benders decomposition; column generation; approximate dynamic programming; reinforcement learning; q-learning
Application summary
The road-transportation sector represents nowadays a major contributing factor to the count of Green-House Gas emissions. This contribution is mostly due to the large ratio of vehicles powered by traditional fossil fuels, and by the constant rise of the number of vehicles on the roads. The transportation sector has, however, the perspective of undergoing an authentic technological revolution, labelled as Transportation 4.0 (Tr4), whose main elements are: 1) the transition towards the electrification of the transportation system, 2) the adoption of self-driving and Autonomous Vehicles (AVs), 3) the advances in Information Technology, 4) the widespread collection and storage of transportation data, and 5) the instantaneous and distributed access to computing resources. While the technological advances implicit in the Tr4 set up the conditions for more sustainable and efficient transportation systems, their management becomes dramatically more challenging. When surveying the scientific literature, one finds that there is a considerable lack of methodologies to address the challenges presented by the Tr4, both in the context of transit systems, as well as in freight distribution. Therefore, the main objective of this proposal is to develop decision-support tools to enable the transportation industry to fully profit from the opportunities given by the Tr4. The proposed methods are rooted in the fields of operations research and artificial intelligence. This research program is organized around two themes. The first theme considers transit systems and focuses on planning problems arising in the integration of on-demand services with traditional transit. This is done by adopting a modeling framework named Semi-flexible Transit System (STS). STSs aim to provide demand-responsive service within the framework of regularly scheduled transit. Combining characteristics of traditional and on-demand systems, STSs require an extremely complex planning activity. More specifically, this proposal aims to develop the methodology to incorporate a fleet of AVs in the operations planning of STSs, and build a simulation framework to analyze the performance of a system integrating STSs with traditional transit, and its impact on the mobility at a city-scale. The second theme considers freight transportation and focuses on stochastic and dynamic routing problems arising in the context of the Tr4. More specifically, this second theme aims to develop decision-support tools for real-time routing of vehicles under stochastic and dynamic traffic conditions, as well as dynamic policies for routing and charging operations when operating a fleet of battery-equipped electric vehicles. By developing tools to enable the Tr4 revolution, the anticipated impact of this research program is to provide more sustainable and more equitable transportation services, both in terms of public transit, as well as freight distribution.