Investigating the mobility habits of electric bike owners through GPS data

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

A Preliminary Analysis Over the Factors Related with the Possession of an Electric Bike

AbstractIn recent decades different studies focused on how to incentivize a shift from car to bicycle. In this context the electric bike is gaining more and more popularity. Because of its higher speed and longer reach, the e-bike could be an attractive alternative to the car. Through an online survey (together with a GPS tracking campaign and a weekly travel diary) conducted in the city of Ghent (Belgium) we define the profile of the e-bike users (age, income, ownership, etc…) and analyze their mobility habits (distance travelled, purpose of the trip, etc…). The initial results obtained from a travel diary survey show how the e-bike is highly used for commuting trips while for more occasional trips (at most once per week) the car is the preferred alternative. Moreover, the analysis of the changes in the mobility habits after the acquisition of the e-bike shows how the e-bike has mainly incorporated the trips performed by bike while also causing an increase of the frequency for some trips. Summarizing, in this paper we propose a preliminary analysis over the factors correlated with the ownership of an e-bike and an overview about how people changed their mobility habits after the acquisition of the e-bike

Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

Dynamic assessment of exposure to air pollution using mobile phone data

Background: Exposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments. Methods: In this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO2 using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach). Results: The mean exposure to NO2 increases with 1.27 mu g/m(3) (4.3 %) during the week and with 0.12 mu g/m(3) (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO2 exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO2 concentrations. Conclusions: It is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones)

A preliminary analysis over the factors related with the possession of an electric bike

In recent decades different studies focused on how to incentivize a shift from car to bicycle. In this context the electric bike is gaining more and more popularity. Because of its higher speed and longer reach, the e-bike could be an attractive alternative to the car. Through an online survey (together with a GPS tracking campaign and a weekly travel diary) conducted in the city of Ghent (Belgium) we define the profile of the e-bike users (age,income,ownership,etc…) and analyze their mobility habits (distance travelled, purpose of the trip,etc…). The initial results obtained from a travel diary survey show how the e-bike is highly used for commuting trips while for more occasional trips (at most once per week) the car is the preferred alternative. Moreover, the analysis of the changes in the mobility habits after the acquisition of the e-bike shows how the e-bike has mainly incorporated the trips performed by bike while also causing an increase of the frequency for some trips. Summarizing, in this paper we propose a preliminary analysis over the factors correlated with the ownership of an e-bike and an overview about how people changed their mobility habits after the acquisition of the e-bike.status: publishe

Valuation of health benefits of green-blue areas for the purpose of ecosystem accounting: a pilot in Flanders, Belgium

In recent years, a vast amount of scientific literature has highlighted the benefits of nearby green space for physical and mental health, but the large variation in scope, methods and indicators used in these studies hampers the assessment of these benefits in the context of natural capital accounting. To our knowledge, this paper is one of the first studies to quantify and value these benefits in the context of natural capital accounting. A method was developed and applied to the Flemish Region in Belgium for 2013 and 2016.The physical supply and use accounts for health are based on a set of selected dose-effect relationships that quantify the impact of the availability of greenspace on seven specific indicators for physical and mental health, including mortality, cardio-vascular diseases, diabetes and depression. The indicator for green-blue areas is the percentage of green-blue areas in total land use, calculated for 0.5, 1 and 3 km radius from the residence, based on detailed land-use maps (10 m x 10 m) for Flanders, Belgium. The base-line data for mortality and illness are average data for the Flemish Region. These health impacts are weighted using Daly's (disability-adjusted life years) and aggregated. The total health benefits due to the availability of green-blue areas for the total Flemish population was estimated at almost 85,000 DALYs. This is 27% of the estimated total burden of disease in Flanders in 2016 for the seven selected diseases.The monetary accounts are based on a detailed assessment for mortality and morbidity of three different cost components, i.e. avoided medical costs (e.g. hospitalisation) and avoided absenteeism and welfare loss due to suffering and reduced life expectancy. Productivity gains from avoided absenteeism is valued, based on statistics on absenteeism for specific diseases for and labour market data from Belgium and account for 52% of the total monetary value of green spaces. Cost of illness is valued, based on market data and illness specific studies for Belgium or Europe and account for 36% of total values. Welfare gains from increased life expectancy are valued on the basis of European studies for the VOLY (value of a life year lost), based on the simulated exchange value for the willingness-to-pay for increased life expectancy. This accounts for 12% of the total monetary value of green space. The total monetary benefits amount to 464 Euro per inhabitant per year or 3 billion Euro per year for Flanders. This corresponds to 1.3% of the GDP, which reflects the importance of these benefits.The methodology is incomplete as not all health indicators are covered, mainly due to a lack of dose-effect relationships. The research priority for potential users of the accounts is a better indicator for contact with green space that does differentiate between ecosystems, their quality, accessibility or their use. This requires more systematic health impact studies that take these elements into account, as well as more systematic data on the daily use of green space by citizens. In the meantime, an additional set of condition accounts on these elements can be used, especially to follow changes in quality and use of green-blue areas over time

Valuation of health benefits of green-blue areas for the purpose of ecosystem accounting: a pilot in Flanders, Belgium

In recent years, a vast amount of scientific literature has highlighted the benefits of nearby greenspace for physical and mental health, but the large variation in scope, methods and indicators used in these studies hampers the assessment of these benefits in the context of natural capital accounting. To our knowledge, this paper is one of the first studies to quantify and value these benefits in the context of natural capital accounting. A method was developed and applied to the Flemish region in Belgium for 2013 and 2016. The physical supply and use accounts for health are based on a set of selected dose-effect relations that quantify the impact of the availability of greenspace on 7 specific indicators for physical and mental health, including mortality, cardio-vascular diseases, diabetes and depression. The indicator for green-blue areas is the % of green-blue areas in total land use, calculated for 0.5, 1 and 3 km radius from the residence, based on detailed land-use maps (10m x 10m) for Flanders, Belgium. The base-line data for mortality and illness are average data for the Flemish region. These health impacts are weighted using Daly's (disability-adjusted life years) and aggregated. The total health benefits due to the availability of green-blue areas for the total Flemish population was estimated almost 85.000 DALY’s. This is 27% of the estimated total burden of disease in 2016 for the 7 selected diseases. The monetary accounts include assessments of avoided costs of illness (e.g. hospitalisation costs) and avoided absenteeism and productivity loss. In addition, we valued the years of life lost due to cardio vascular mortality based on simulated exchange values. The total monetary benefits amount to 464 Euro per inhabitant per year or 3 billion Euro per year for Flanders. This corresponds to 1.3% of the GDP, which reflects the importance of these benefits. Productivity gains and avoided costs of illness account for respectively 52 % and 36% of these costs. The methodology is incomplete as not all health indicators are covered, mainly due to a lack of dose-effect relations. The research priority for potential users of the accounts is a better indicator for contact with greenspace that does differentiate between ecosystems, their quality, accessibility or their use. This requires more systematic health impact studies that take these elements into account, as well as more systematic data on the daily use of greenspace by citizens. In the meantime,  an additional set of condition accounts on these elements can be used, especially to follow changes in quality and use of green blue areas over time.

Using an activity-based framework to determine effects of a policy measure on population exposure to nitrogen dioxide

Few studies have modeled the effects of policy measures on population exposure. This work assessed for the first time the impact of a policy measure on population exposure to nitrogen dioxide (NO 2) by using the activity-based model ALBATROSS. Activity-based models can be of great value in evaluating the effect of integrated policies and measures that have no obvious relation with transport or air quality. The scenario considered in this study involved changing the hours during which shops could be open to allow shopping earlier in the morning and later in the evening. Both emissions and population distribution of this policy measure could be derived from the activity travel behavior predicted by the activity-based model. It was found that extending the opening hours changed the activity pattern of the adult population in the Netherlands. Approximately 6% more nondaily and 0.5% more daily shopping hours were predicted. The change in activity pattern resulted in more transport (+0.5% more vehicle kilometers driven). As a consequence of this, emissions and air pollutant concentrations were also altered. When the concentration maps were matched with the dynamic population, an increase in population exposure to NO 2 was observed. Absolute differences were small (up to 0.40 μg/m 3). On an average weekday, NO 2 exposure increased by 0.15 μg/m 3. The relative change in exposure on an average weekday was 0.4%. In certain neighborhoods and at certain hours a more substantial increase could be observed

An integrated activity-based modelling framework to assess vehicle emissions: approach and application

Owing to the richer set of concepts which are involved in activity-based transportation models, the potential advantages of an activity-based approach for air quality purposes have been recognized for a long time. However, models that have been developed along these lines are still scarce. In this research the activity-based model ALBATROSS was used in combination with the emission model MIMOSA to assess the travelled distances and the mobile source emissions produced by passenger cars in the Netherlands. The fact that this approach is based on hourly travel and emission values, rather than on aggregated results or peak hour values, a common practice within other traditional models, is an important added value. The predicted values seem to correspond well with the reported values from the Dutch Scientific Statistical Agency. Predictions for travelled distances overestimated the reported values by approximately 8%. Predictions for emissions of nitrogen oxide, carbon dioxide, volatile organic compounds, and particular matter differed by 16%, 11%, 9%, and 3%, respectively, from the officially reported values. This paper is novel in the sense that it both reports on the applied methodology and presents the practical results from a case study of the activity-based emission modelling approach.

Associations between time spent in green areas and physical activity among late middle-aged adults

Physical activity is an important facilitator for health and wellbeing, especially for late middle-aged adults, who are more susceptible to cardiovascular diseases. Physical activity performed in green areas is supposed to be particularly beneficial, so we studied whether late middle- aged adults are more active in green areas than in non-green areas and how this is influenced by individual characteristics and the level of neighbourhood greenness. We tracked 180 late middle-aged (58 to 65 years) adults using global positioning system and accelerometer data to know whether and where they were sedentary or active. These data were combined with information on land use to obtain information on the greenness of sedentary and active hotspots. We found that late middle-aged adults are more physically active when spending more time in green areas than in non-green areas. Spending more time at home and in non-green areas was found to be associated with more sedentary behaviour. Time spent in non-green areas was found to be related to more moderate-to-vigorous physical activity (MVPA) for males and to less MVPA for females. The positive association between time spent in green areas and MVPA was the strongest for highly educated people and for those living in a green neighbourhood. This study shows that the combined use of global positioning system and accelerometer data facilitates understanding of where people are sedentary or physically active, which can help policy makers encourage activity in this age cohort