Smart City Energy Efficient Multi-Modal Transportation Modeling and Route Planning

dc.contributor.authorGhanem, Ahmed Mohamed Abdelaleemen
dc.contributor.committeechairRakha, Hesham A.en
dc.contributor.committeememberBansal, Manishen
dc.contributor.committeememberMidkiff, Scott F.en
dc.contributor.committeememberAbbott, A. Lynnen
dc.contributor.committeememberClancy, Thomas Charlesen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2021-12-18T07:00:06Zen
dc.date.available2021-12-18T07:00:06Zen
dc.date.issued2020-06-25en
dc.description.abstractAs concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction in toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times for use in bike share systems (BSSs) using random forest (RF), least square boosting (LSBoost), and artificial neural network (ANN) techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.en
dc.description.abstractgeneralAs concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction of toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times in bike share systems (BSSs) using machine learning techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:26519en
dc.identifier.urihttp://hdl.handle.net/10919/107110en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBike Travel Time Modelingen
dc.subjectCycling Behavior Modelingen
dc.subjectRailway Simulationen
dc.subjectRidesharingen
dc.subjectMulti-Modal Trip Planningen
dc.titleSmart City Energy Efficient Multi-Modal Transportation Modeling and Route Planningen
dc.typeDissertationen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen
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