iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

Prendinger, Helmut; Oliveira, João Fradinho; Catarino, João Ricardo; Madruga, Marconi; Prada, Rui

doi:10.1109/mic.2014.21

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…Nunzio adopted graph discretizing approach to identify the ecological spatial-temporal trajectories of vehicles [30]. Madruga applied the multi-agent system to eco-driving training [31]. Some others focused on improving the fuel efficiency of HEV, PHEV or EV at the signalized intersections [8,32].…”

Section: Introductionmentioning

confidence: 99%

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Jiang¹,

An²,

Jian³

et al. 2018

Sustainability

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Abstract:This research proposed an eco-approach and departure system for left-turn vehicles at a fixed-time signalized intersection. This system gives higher priority to enhancing traffic safety than improving mobility and fuel efficiency, and optimizes the entire traffic consisted of connected and automated vehicles (CAVs) and conventional human-driven vehicles by providing ecological speed trajectories for left-turn CAVs. All the ecological speed trajectories are offline optimized before the implementation of system. The speed trajectory optimization is constructed in Pontryagin's Minimum Principle structure. The before and after evaluation of the proposed system shows the percentage of vehicles that drive pass the intersection at safe speed increases by 2.14% to 45.65%, fuel consumption benefits range 0.53% to 18.44%, emission benefits range from 0.57% to 15.69%, no significant throughput benefits is observed. The proposed system significantly enhances the traffic safety and improves the fuel efficiency and emission reduction of left-turn vehicles with no adverse effect on mobility, and has a good robustness against the randomness of traffic. The investigation also indicates that the computation time of proposed system is greatly reduced compared to previous eco-driving system with online speed optimization. The computation time is up to 0.01 s. The proposed system is ready for real-time application.

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Section: Introductionmentioning

confidence: 99%

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Jiang¹,

An²,

Jian³

et al. 2018

Sustainability

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“…As a result, traffic situations occur naturally in the virtual environment, which can be utilized to investigate eco-driving policies [38] or traffic congestion [39]. iCO 2 's predecessor version [13] contained two game modes, (1) "Free drive" and (2) "Campaign", where players were paid for completing a quest or task. By contrast, the current version of iCO 2 offers a full-fledged quest system, FIGURE 3: Screenshot of the iCO 2 game, which displays a player driving around the replica of Tokyo.…”

Section: The Ico2 Data Collection Platformmentioning

confidence: 99%

“…Section II provides background research on eco-driving games, training applications, crowdsourcing and games with a purpose. Section III describes the iCO 2 simulation platform that extends our previous version of iCO 2 [13] with a quest system and upgrade functionality. In Section IV we explain the campaign and present usage statistics of iCO 2 during the campaign.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Analytics of the Distributed Eco-Driving Simulation iCO₂

Hollerit¹,

Prendinger

Jain³

et al. 2021

IEEE Access

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We describe iCO 2 , a simulation platform for collecting driving behavior data. It is designed as the first massively multiplayer online game for mobile devices to practice eco-friendly driving. It facilitates the collection of large-scale data on driving behavior to better understand compliance and incentive mechanisms for eco-driving and users' preferences. We present the results of a campaign with iCO 2 that used a game promoter to attract 2455 users. The results are described from three angles: (1) types of drivers are identified by clustering driving behavior; (2) types of players are identified by relating players' interaction with game elements and their driving behavior; (3) by looking at longer sessions, we demonstrate that players who show eco-unfriendly behavior at the beginning of the session improve their eco-driving behavior throughout their playtime.

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“…Eco-driving has potential in fuel saving [54]. The International Energy Agency [5] and previous studies [2, [55][56][57] have calculated various amounts of fuel consumption savings from eco-driving and reported the short-term and medium-term impacts of eco-driving training. Just after eco-driving training, the average fuel efficiency will be improved by 15% [5, 54,58], while, in the long-term, 5% to10% improvement will be expected [59,60].…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

Cited by 13 publications

References 11 publications

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Implementation and Analytics of the Distributed Eco-Driving Simulation iCO₂

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

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iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

Cited by 13 publications

References 11 publications

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Implementation and Analytics of the Distributed Eco-Driving Simulation iCO2

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

Contact Info

Product

Resources

About

Implementation and Analytics of the Distributed Eco-Driving Simulation iCO₂