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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.

This contribution presents the results of the "SENSOVO" project initiated by the Flanders Institute for Mobility (VIM), executed by the University of Antwerp (UAntwerp), the Flemish Institute for Technological Research (VITO) and the Belgian Road Research Centre (BRRC), and supported by several other parties. Both road users and road managers could benefit from massively, continuously, automatized collecting of information on road surface distress (potholes, cracking, subsidence,…) by a fleet of vehicles equipped with low-cost sensors. Road users could have immediate information on road conditions while road managers could get year-round insight on the general performance of the road network in addition to the data they obtain from annual inspections with specialized monitoring devices. The project's objective was to investigate possibilities of road surface distress detection using data collected by a fleet of vehicles. Two scenarios were considered: a large fleet of ordinary cars and trucks transmitting collected relevant sensor data already available on the CAN-bus in such vehicles; a vehicle, equipped with one or more Time-of-Flight cameras (ToF). Data on the CAN-bus were collected using either a CAN-logger that sends its data to a central server using GPRS where it is processed for road surface distress detection, or using an OBD scan-tool that sends its data using Bluetooth to a smartphone that already processes the data into detection events, forwarding only these to a central server. The performances were tested by UAntwerp and VITO. A simple computation developed by UAntwerp on the speed of all four wheels and on the vertical accelerations often allows indicating road distress. Either the CAN data-logger or the smartphone delivers the GPS location of the event. Several cars equipped with this technology sent their observations to a central database. Several ToF cameras were benchmarked by UAntwerp. Road data were collected at 40km/h and 40frames/s with ToF-cameras of brands Mesa and Fotonic. Several image processing algorithms dedicated to the identification of road distress on a flow of images were developed by UAntwerp. It has been demonstrated that several types of "unevenness" of the road surface (including potholes) can be detected. Some ToF-camera observations were added to the central database. The BRRC developed and implemented an algorithm for treatment and interpretation of the collected events in the central database using the ArcGIS software, simulating both sending out alerts to road users and providing daily "quality scores" for each road section in the network to the road manager. For this, the network was defined from a geographical map provided by the Flanders Geographical Information Agency (FGIA/AGIV). As soon as "enough" observations are made taking into account the frequency of observations in time, the defect is considered as real and will be reported. When the defect is no longer observed, it is considered as being repaired. A "score" for each road section was computed daily, from ...

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During the execution of the project Floating Automotive Data Collection (FADC), various traffic conditions were recorded using in-vehicle measurements. A series of reference measurements were performed on a fixed route in Belgium in real traffic circumstances, including motorway, rural and city traffic. Three vehicles were used for the reference measurements: a passenger car, a delivery van, and a delivery truck. An on-board device recorded vehicle speed and GPS position on second-basis. The resulting speed profiles were used as input in the vehicle emission simulation programme VeTESS to estimate fuel consumption and emissions of certain vehicles driving according to these speed profiles. The goal of these calculations was to quantify the effect of preventing traffic jams. From the simulations it was concluded that a reduction in average speed does not necessarily lead to higher fuel consumption. Actually when average speed is reduced from speeds above 100 km/h down to 80 or 60 km/h, fuel consumption can even be expected to decrease. Of course traffic jams are more about low average speeds. The simulations showed that when average speed drops below 30 or 40 km/h, fuel consumption increases significantly. Emissions of NO x , CO and HC also increase in this case. So concerning fuel consumption and emissions it is indeed worth the effort to prevent traffic jams and slow traffic and to improve the traffic flow. The role of traffic jam detection is very important to take measures to improve the traffic flow and this way save fuel and reduce emissions.