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This paper investigates the mobility habits of electric bike owners as well as their preferred routes. Through a GPS tracking campaign conducted in the city of Ghent (Belgium) we analyze the mobility habits (travel distance, time spent, speed) during the week of some e-bike users. Moreover, we propose the results of our map matching, based on the Hausdorff criterion, and preliminary results on the route choice of our sample. We strongly believe that investigating the behavior of electric bikes’ owners can help us in better understanding how to incentivize the use of this mode of transport. First results show that the trips with a higher travel distance are performed during the working days. It could be easily correlated with the daily commuting trips (home-work). Moreover, the results of our map-matching highlight how 61% of the trips are performed using the shortest path.

Transport was responsible for 20% of the total greenhouse gas emissions in Europe during 2011 (European Environmental Agency 2013) with road transport being the key contributor. To tackle this, targets have been established in Europe and worldwide to curb transport emissions. This poses a significant challenge on Local Government and transport operators who need to identify a set of effective measures to reduce the environmental impact of road transport and at the same time keep the traffic smooth. Of the road transport pollutants, this paper considers NOx, CO2 and black carbon (BC). A particular focus is put on black carbon, which is formed through incomplete combustion of carboneous materials, as it has a significant impact on the Earth’s climate system. It absorbs solar radiation, influences cloud processes, and alters the melting of snow and ice cover (Bond et al. 2013). BC also causes serious health concerns: black carbon is associated with asthma and other respiratory problems, heart attacks and lung cancer (Sharma 2010; United States Environmental Protection Agency 2012). Since BC emissions are mainly produced during the decelerating and accelerating phases (Zhang et al. 2009), ITS actions able to reduce stop&go phases have the potential to reduce BC emissions. This paper investigates the effectiveness of combined ITS actions in urban context in reducing CO2 and BC emissions and improving traffic conditions.

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses all these model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC.

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses these off-line model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC.

Purpose: There are different reasons to assume that not every vehicle kilometre yields the same environmental impact. For instance, some vehicle kilometres are driven at high speeds on highways, while others are driven at low speeds in urban environments. This will have an impact on the resulting exhaust missions. Furthermore, the timing of a trip determines its impact on the resulting concentration levels. Pollutants emitted during trips at night will have a larger impact on the ground-level concentrations than emissions exhausted during the day, due to the greater atmospheric stability. And, concerning the impact on exposure, emissions produced in cities will affect a larger number of people than emissions exhausted in sparsely populated areas. These aspects are examined in this paper using an integrated model chain involving an activity based traffc demand model (to assess people's travel behavior), an emission model (to convert the trips into emissions) and a pollutant concentration module(to stimulate detailed concentration maps and assess the impact of each trip on the resulting concentration level). By combining the model output with detailed population information, the marginal impact of a trip on the population exposure to NO2 was assessed. Results demonstrate that not every vehicle kilometre and not every trip motive yields the same impact on the population exposure.

Long term transport policy should focus energy saving and introducing renewable energy in transport. While energy saving should receive the highest priority, for the second pillar the main options are electric mobility and biofuels. While in the next ten years current biofuels (based on agricultural crops) are still the basis in biofuels roadmaps, further growth afterwards will have to come from other feedstocks, like waste & residues, ligno­cellulose and possibly algae on the longer term. By 2030, the contribution of advanced biofuels and electric mobility will become significant. The main focus for electric vehicles will be on local traffic (delivery vans, public transport) and on the segment of passenger cars. Long distance traffic (trucks, coaches, airplanes, ships) strongly relies on a high density (liquid) fuel and it will be difficult to create a major role for electricity in these sectors. So on the long term, a balance will appear between electricity and liquid (or gaseous) fuels, both fossil and biomass based. This creates an important role for biofuels in certain sectors, which is also anticipated in the worldwide scenarios of the International Energy Agency (IEA)., Proceedings of the 19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany, pp. 2749-2754

In this study VITO was asked to perform a plausibility check of The Dutch Platform for Biobased Raw Materials (PGG) 60% biofuels target for 2030. The analysis of the evolution of the transport sector lead to insights that with policy supporting energy saving and electrification of transport, by 2030 energy demand in transport could be reduced by one third compared to the reference scenario. Nevertheless it is striking that, even in a pro-active scenario, about 90% of total energy in transport in 2030 will be consumed in more or less traditional technologies. Within these technologies, biofuels can play an important role. The local biomass potential in the Netherlands can only supply part of the demand, the accessability, the presence of the international harbour of Rotterdam, and ample expertise in the Netherlands on biomass conversion are important foundations to build local biofuel activities. It was concluded that it would be ambituous, but possible to produce an amount of biofuels in the Netherlands, equivalent to 60% of its in-land fuel consumption in 2030. However most of this production will be for export to other European countries. The ambition for biofuel consumption in the dutch transport system will need to be geared to the policies in neighbour countries, although some higher target will be possible, e.g. through the support of high blends. It was suggested that a 25% target for in-land biofuel consumption in 2030 would be very ambitious, but still plausible if thoroughly supported by policy and RD&D in different fields., Proceedings of the 18th European Biomass Conference and Exhibition, 3-7 May 2010, Lyon, France, pp. 1971-1974

Policymakers are increasingly recognizing the importance of source-related measures, instead of technological actions, to tackle the problem of traffic air pollution. Unfortunately, traditional trip-based models fail to make accurate predictions for activityrelated policy questions. Due to the richer set of concepts which are involved in activitybased transportation models, the use of these models should be encouraged to contribute to this part. In this research the activity-based model ALBATROSS was used to assess trips and emissions produced by passenger cars in the Netherlands. The results were segregated according to trip motive to gain more insights into the contribution of different trips towards the total amount kilometres and air pollution. The predicted values correspond well with the reported values from the Dutch Scientific Statistical Agency. Predictions for total travelled distance, carbon dioxide, carbon monoxide, nitrogen oxide, sulphur dioxide and particulate matter (PM) differed not more than 8% from the officially reported values. Concerning the classification into trip motive, the commuter trips produced almost half of the PM emissions. Further, trips with a social purpose caused 17% of the PM emissions, and shopping and leisure trips each accounted for 10% of the total PM emissions. This paper is novel in the sense that it reports on the applied methodology and presents the practical results from a case study of the activity-based modelling approach as well.

This paper presents the first results of a research project that applies an activity-based travel demand model to the evaluation of vehicle exhaust emissions. More specifically, the activity-based model ALBATROSS, a fully operational computational process model, was used to predict activity schedules and trip matrices for personal vehicle travel in the Netherlands. By assigning the predicted matrices to a road network and converting the resulting traffic flows into emissions, good estimates were made of the vehicle emissions caused by personal vehicle travel. Considering the detailed level of activity and trip information present in the activity-based approach, these results are very promising for future research on the environmental impact of transportation control measures.

This study explores the influence of gear changing behaviour on vehicular exhaust emissions and fuel consumption using real drive cycles as an input. 235 different drive cycles, recorded from people participating in a survey, were imported in an emission simulation tool called VeTESS (Vehicle Transient Emissions Simulation Software). Emissions and fuel consumption were calculated with VeTESS using two different gear change assumptions (normal and aggressive). This paper reports on the differences in vehicle exhaust emissions between trips made with those two different settings.

The presented traffic control system (CARBOTRAF) combines real-time monitoring of traffic and air pollution with simulation models for traffic, emission and local air quality predictions to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimising the traffic flows. A chain of models combines microscopic traffic simulations, emission models and air quality simulations for a range of traffic demand levels and intelligent transport system (ITS) actions. These ITS scenarios simulate combinations of traffic signal optimisation plans and variable messaging systems. The real-time decision support system uses these simulations to select the best traffic management in terms of traffic and air quality. In this paper the modelled effects of ITS measures on air quality are analysed with a focus on BC for urban areas in two European cities, Graz and Glasgow.