Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information

Boriboonsomsin, Kanok; Barth, Matthew; Zhu, Weihua; Vu, Alexander

doi:10.1109/tits.2012.2204051

Cited by 263 publications

(163 citation statements)

References 16 publications

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“…Several authors have concluded that eco-route navigation has great potential for reducing fuel consumption [20][21][22][23]. However, this concept is somewhat complicated to implement, since the optimal speed profile for reducing fuel consumption and CO 2 emissions may produce an increase in some types of pollutants, such as CO and HC [24,25].…”

Section: Eco-routingmentioning

confidence: 99%

Green Eco-Driving Effects in Non-Congested Cities

et al. 2017

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Despite technological advances in engines and fuels, the transportation sector is still one of the largest emitters of greenhouse gas (GHG). Driving patterns, including eco-driving techniques, are a complementary measure for saving GHG emissions. Most eco-driving studies so far have been conducted in large cities suffering chronic congestion problems. The aim of this research is therefore to analyse the potential of driver behaviour for reducing emissions in a small non-congested city. Driver performance parameters such as travel speeds, number of stops, revolutions per minute, and maximum acceleration-deceleration are also studied. The methodology is designed to measure the effect of both eco-driving and eco-routing under real traffic conditions. A campaign was carried out in the city of Caceres (Spain) to collect data on various types of roads under different traffic conditions. This research concludes that eco-driving leads to CO 2 savings on all routes and road types of 17% in gasoline engines and 21% in diesel, although travel times are increased by 7.5% on average. The shortest route is also the most ecological, regardless of the traffic volume and characteristics, implying that consumption in non-congested cities depends mainly on distance travelled rather than driving patterns in terms of number of stops, speed and acceleration.

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Section: Eco-routingmentioning

confidence: 99%

Green Eco-Driving Effects in Non-Congested Cities

et al. 2017

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“…Novel methods that deal with the optimal charging of electric vehicles, in static or dynamic mode, have recently been introduced. These vary from stationary stations that are scattered across the road network in central positions [43][44][45], dynamic wireless charging methods that take advantage of the mobility of nodes [46,47] and eco-routing algorithms that run in isolation in every vehicle or in a central way for a fleet of vehicles [48,49]. The authors in [50] present a method for dynamic wireless charging of vehicles based on the concept that vehicles meet at rendezvous points, giving a social aspect to the power transfer procedure.…”

Section: Electric Vehiclesmentioning

confidence: 99%

Social Internet of Vehicles for Smart Cities

Μαγλαράς

Al-Bayatti

et al. 2016

JSAN

138

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Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

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“…On the transportation side, the eco-driving mechanism is developed to guide the driver to perform the most fuel efficient operation [1], and the eco-routing navigation systems aim to identify the most energy-efficient route for a vehicle to travel between two points in real-time [11].…”

Section: Green Freight Routingmentioning

confidence: 99%

Inventory Routing with Explicit Energy Consumption: A Mass-Flow Formulation and First Experimentation

Briand

Jozefowiez

2017

Operations Research and Enterprise Systems

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Abstract. Energy efficiency is becoming an important criteria for the inventory systems. Our aim is to explicitly integrate the energy into the existing Inventory Routing Problem (IRP). The problem is based on a multi-period single-vehicle IRP with one depot and several customers. An energy estimation model is proposed based on vehicle dynamics. A mass-flow based Mixed Integer Linear Programming (MILP) formulation is presented. Instead of minimizing the distance or inventory cost, energy minimization is taken as an objective. Benchmark instances for inventory routing are adapted for energy estimation and experiments are conducted. The results are compared with those of the distance/ inventory cost minimization.

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Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information

Cited by 263 publications

References 16 publications

Green Eco-Driving Effects in Non-Congested Cities

Green Eco-Driving Effects in Non-Congested Cities

Social Internet of Vehicles for Smart Cities

Inventory Routing with Explicit Energy Consumption: A Mass-Flow Formulation and First Experimentation

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