Evaluating the effectiveness of eco-driving courses based on car-GPS tracking data in the itinerary tracking device to reduce fuel consumption of vehicles in urban areas (original) (raw)
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EcoSmart: An application for smartphones for monitoring driving economy
Advanced Materials Research, 2014
Instantaneous consumption models are designed to provide an accurate description of fuel consumption by relating the instantaneous rate of consumption with the vehicle acceleration, speed and the engine dynamics. In principle, instantaneous consumption models allow users to calculate the fuel consumption for any profile of vehicle operation. Some kind of models that take into account only driving dynamics can be used to describe the variability of consumption. The procedure used by this type of models is to establish reference matrices (related to specific time intervals) that for each combination of speed and acceleration determine the rate of fuel consumption. This paper Describes the design of a mobile application for the estimation of consumption, and user behavior, that applies models of instantaneous consumption. A preliminary experimental survey has been the carried out to obtain a large data base related to the acquisition of cinematic and engine parameters to allow the development of the mobile application. The experimental acquisition of parameters was accomplished in dynamic driving, connecting the OBDII port to different models of vehicles. With this procedure, reference matrices of fuel consumption for each specific class of emission (as required by ECE 15.03 -Directive 78/665/EEC and ECE 15.04 -Directive 83/351/EEC) have been obtained. The mobile phone application, after receiving as input the vehicle specifications provided by the user, can associate the specified matrix of consumption to the cinematic parameters (instant speed and acceleration obtained by GPS measures) and by using each pair of speed and acceleration can give an estimate of consumption values. At the end of the trip the user can take view of all device information on consumption also by geo-displaying the information on a map (showing the path and the related point by point consumptions). The output of the system, that can also provide information related to the dynamic behaviour and energy management in the user guide style, have been confronted with real consumption data. The advantage of the application is to estimate fuel consumption and driving style, from just GPS data, without connecting the phone to OBDII port.
Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission
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.
Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data
The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles' mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles' activities in road networks.
Predicting The Mode Of Transportation Using GPS Data, For Vehicular Carbon Footprint Determination
Greenhouse gas emissions by vehicles is damaging the environment. In order to take remedial measures at individual level, one must first know the full scale of damage being done. This study suggests that using GPS data from smartphones, the travel mode used by individuals can be classified into motorized and non-motorized. It can assist in correctly estimating the vehicular carbon footprint by each individual. In this study, simplest features (travel duration, travel distance and average velocity), derived from GPS data, were used to train and test two popular algorithms i.e. Support Vector Machine and Random Forest. Results show that Random Forest provides a prediction accuracy of 90%, outperforming Support Vector Machine.
Long-term effect of eco-driving education on fuel consumption using an on-board logging device
Urban Transport XIV, 2008
This paper describes the measured long-term effects on fuel consumption of ecodriving education. The results are part of the long-term survey within the Flemish research program "An activity-based approach for surveying and modelling travel behaviour". During several months, the travel and driving behaviour of 28 respondents was monitored. The methodology consists of using an on-board vehicle device and a web-application. The on-board device is equipped with a GPRS-modem, a WiFi connection, a GPS system and a CANinterface. The GPS system allows the monitoring of travel behaviour. Driving behaviour is studied by logging various CAN-parameters (e.g. revs per minute, chosen gear etc). Data is transmitted to a central server through the GPRSnetwork. Alternatively, data can be transmitted using a WiFi connection when present. Respondents can access the data on a web-application and provide additional information. The gathered information is used on the one hand to develop a regional activity-based travel model (not discussed in this paper). On the other hand, the data is used to assess the long-term effect of an eco-driving course by analyzing the change in driving behaviour and monitoring fuel consumption, and using these inputs to simulate the emissions before and after such training. The data might also be used as feedback to the driver, to visualize his driving behaviour, and to help him understand what he can do to further improve his driving style. This paper discusses the long-term effect of an ecodriving course on fuel economy and driving style for eight participants.
Transportation Research Part D-transport and Environment, 2010
This paper describes the development of a global positioning system, enhanced data collection tool for the assessment of vehicle exhaust emissions. This involves the collection of activity and travel data on a personal digital assistant with built-in global positioning system receiver. By converting the second-by-second global positioning system based travel data into emissions, estimates are made of the exhausts produced by individual vehicle trips. Differences in travel behaviour and vehicle emissions were examined by gender and trip purpose.
The Effect of Ecodrive Program on Driving Behavior and Fuel Economy of Passenger Cars in Tokyo
Journal of the Eastern Asia Society for Transportation Studies, 2011
Ecodrive or Ecodriving is a driving technique designed to improve fuel economy, reduce CO2 and lessen the adverse impacts of road transport on the environment. This study investigates the effects of the Ecodrive program on candidate drivers by looking into their driving behavior (psychological and non-psychological factors) as well as taking into consideration the vehicle parameters used during Ecodrive training. There are twenty-seven drivers with their vehicles equipped with on-board data-logging equipment that serve as subjects of this study. The drivers were given sets of questionnaires for them to fill-in before and after training. Questionnaire data and actual driving data were analyzed using Cronbach‟s Alpha and Regression. The study has successfully recorded and analyzed the effects of Ecodrive program on driver behavior with respect to fuel efficiency. The study also showed fuel efficiency, improvement of the program, improvement during idling stop and improvement during dr...
Influence of driver characteristics on emissions and fuel consumption
Transportation Research Procedia, 2017
Fuel consumption and atmospheric pollution emissions of vehicles depend on driving conditions, the characteristics of the driver and the car. The influence of driving style on the environmental aspects of a car journey has been investigated. Driver characteristics were determined by a Driver Behaviour Questionnaire and observed acceleration and deceleration behaviour. That results in four types of drivers with similar characteristics within a type group. We measured 56 trajectories of 28 drivers using GPS devices. The measurements were done on a route of 8.4 km in an urban environment in Chengdu (PR China). From the trajectories, the emissions and fuel consumption were determined with the Comprehensive Modal Emissions Model. The results were related to the traffic control along the journey resulting in fuel consumption and emissions per stop and per second idling. There are significant differences in saturation flow, emissions and fuel consumption between different driver types. Cautious, novice drivers have the lowest emission and fuel consumption and give the lowest saturation flow and have the lowest cruise speed; experienced smooth driving drivers give a high saturation flow while keeping fuel consumption and emissions also low. Aggressive experienced drivers have a high saturation flow and fuel consumption / emissions. Therefore, microscopic traffic models that simulate emissions and fuel consumption should take the differences between driver types into account.
Evaluation of Eco-driving using Smart Mobile Devices
The methods of measuring driving behaviour and the quality of drive in road transport are important factors in data acquisition and subsequent analysis of driving. The prevalence of smart terminal devices and cost effectiveness of On-Board Diagnostics (OBD) sensor devices provide great potential and the availability of the aforementioned technologies. This study shows the possibility of using information and communication technologies (ICT) and sensor devices for measuring the effectiveness of eco-driving. The ease of implementation of ICT elements, the accuracy of collected data and their storage for later data analysis offer a number of possibilities to use. This study shows the technical solution of the system and analysis of collected data on actual driving samples. By comparing normal and eco-driving modes, the advantage of using eco-driving modes is demonstrated through reduced fuel consumption and CO2 emissions amounting to almost 23% and 31%, respectively.
Developing Eco-Driving Strategies considering City Characteristics
Journal of Advanced Transportation, 2020
CO2 emissions reduction is a top element of transport policy agenda. Among other mitigation policy measures, eco-driving techniques have proven to be effective in reducing fuel consumption and CO2 emissions. The aim of this paper is to compare the impacts of adopting eco-driving in different cities, road segments, traffic, and driver features. It intends to gain an insight into how city size and driving characteristics can reduce fuel consumption and CO2 emissions in order to develop specific eco-driving strategies. Field trials were conducted in two Spanish cities (Madrid and Caceres). 24 drivers, with different driving experiences, drove two different vehicles (petrol and diesel) along roads with different characteristics. The experiment was divided into two periods of 2 weeks; after the first one, drivers received an eco-driving training course. The impacts of eco-driving were measured comparing before and after results. They showed that eco-driving is highly effective in reducin...