Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits

Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version

We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties of the proposed techniques and offer evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for Complete Weight Annotation of Road Networks," which is accepted for publication in IEEE TKD

A GIS based 3D-Routing-Model to estimate and reduce CO2-emissions of distribution transports

Schröder, M., & Cabral, P. (2019). Eco-friendly 3D-Routing: A GIS based 3D-Routing-Model to estimate and reduce CO2-emissions of distribution transports. Computers, Environment and Urban Systems, 73, 40-55. DOI: 10.1016/j.compenvurbsys.2018.08.002Road freight transportation accounts for a significant share of the worldwide CO2-Emissions, indicating that respective operations are not sustainable. Regarding the forecasted increase in CO2-Emissions from this sector, undertaking responsibilities for its environmental impact are needed. Although technical and strategic solutions to reduce emissions have been introduced, or are in development, these rarely yield instant emission reduction potentials. A strategic approach to reducing them instantly, based on the given infrastructure and existing vehicle fleet, may be achieved through route optimization. Route optimization is a well-researched topic in the transportation domain. However, it is mainly used to reduce transportation times and expenses. Rising expectations towards sustainability by authorities and consumers led to an increased interest in route optimization in which environmental externalities, such as fuel consumption and CO2-Emissions are minimized. This paper introduces a Geographic Information System (GIS) based 3D-Routing-Model, which incorporates models to estimate vehicle fuel consumption while taking effects, such as road inclination and varying velocities into account. The proposed model utilizes a Digital Elevation Model (DEM) to enrich a road network with elevation data. The 3D-Routing-Model is applied in different distribution scenarios within the framework of an artificial company in the Lisbon Metropolitan Area, Portugal to evaluate the effects of road inclination on vehicles fuel consumption and its proportional CO2-Emissions. Results indicate that eco-friendly routes can yield significant fuel and emission saving potentials of up to 20% in the tested scenarios. However, eco-friendly routes are characterized by longer distances as well as operation times, which leads to increased expenses. The question remains if companies within the transportation sector are more interested in maximizing their profits, or investing in a sustainable future.authorsversionpublishe

Vehicle telematics for safer, cleaner and more sustainable urban transport:a review

Urban transport contributes more than a quarter of the global greenhouse gas emissionns that drive climate change; it also produces significant air pollution emissions. Furthermore, vehicle collisions kill and seriously injure 1.35 and 60 million people worldwide, respectively, each year. This paper reviews how vehicle telematics can contribute towards safer, cleaner and more sustainable urban transport. Collection methods are reviewed with a focus on technical challenges, including data processing, storage and privacy concerns. We review how vehicle telematics can be used to estimate transport variables, such as traffic flow speed, driving characteristics, fuel consumption and exhaustive and non-exhaustive emissions. The roles of telematics in the development of intelligent transportation systems (ITSs), optimised routing services, safer road networks and fairer insurance premia estimation are highlighted. Finally, we outline the potential for telematics to facilitate new-to-market urban mobility technologies, signalised intersections, vehicle-to-vehicle (V2V) communication networks and other internet-of-things (IoT) and internet-of-vehicles (IoV) technologies

Eco-friendly 3d-routing : a GIS based 3d-routing-model to estimate and reduce co2-emissions of distribution transports

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesRoad Freight Transportation accounts for a significant share of the worldwide CO2-Emissions, indicating that respective operations are not sustainable. Regarding the forecasted increase in CO2-Emissions from Road Freight Transportation, this sector needs to undertake responsibilities for its environmental impact. Although technical and strategic solutions to reduce emissions have been introduced or are in development, such solutions rarely yield instant emission reduction potentials. A strategic approach to reduce them instantly, based on the given infrastructure and existing vehicle fleet, is represented through route optimization. Route optimization is a well-researched topic in the transportation domain. However, it is mainly used to reduce transportation times and expenses. Rising expectation towards sustainability through stakeholders such as authorities and consumers, let to an increased interest in route optimization where environmental externalities as fuel consumption and CO2-Emissions are minimized. This paper introduces a Geographic Information System (GIS) based 3D-Routing-Model, which incorporates models to estimate vehicle fuel consumption while taking effects as road inclination and varying velocities into account. The proposed model utilizes a Digital Elevation Model to enrich a Road Network with elevation data – An approach which is applicable to any area where respective data is available. To evaluate the effects of road inclination on a vehicles fuel consumption and its proportional CO2-Emissions, the 3D-Routing-Model is applied in different distribution scenarios within the framework of an artificial company in the Lisbon Metropolitan Area. The obtained results indicate that eco-friendly routes can yield significant fuel and emission saving potentials of up to 20 % in the tested scenarios. However, the results also indicate that eco-friendly routes are characterized through longer distances as well as operation times, which eventually leads to increased expenses. It remains the question if companies within the transportation sector are more interested in maximizing their profits, or to invest in a sustainable future