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)

Measuring Regularity of Individual Travel Patterns

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals' travel over multiple days as sequences of 'travel events' - discrete and repeatable behavior units explicitly defined based on the research question and the available data. We then present a metric of regularity based on entropy rate, which is sensitive to both the frequency of travel events and the order in which they occur. The methodology is demonstrated using a large sample of pseudonymised transit smart card transaction records from London, U.K. The entropy rate is estimated with a procedure based on the Burrows-Wheeler transform. The results confirm that the order of travel events is an essential component of regularity in travel behavior. They also demonstrate that the proposed measure of regularity captures both conventional patterns and atypical routine patterns that are regular but not matched to the 9-to-5 working day or working week. Unlike existing measures of regularity, our approach is agnostic to calendar definitions and makes no assumptions regarding periodicity of travel behavior. The proposed methodology is flexible and can be adapted to study other aspects of individual mobility using different data sources.Transport for London (Organization

Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.

To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on the environmental implications of these emerging systems is often limited by using aggregated travel pattern data to characterize personal mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect several key factors that determine potential environmental impacts, including charging behaviors, connection needs between different transportation modes, and car/ride sharing potentials. Therefore, to better understand these systems and inform decision making, travel patterns at the individual level need to be considered. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefits of integrating individual travel patterns into environmental assessments through three case studies (vehicle electrification, charging station siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification study indicate that individual travel patterns can impact the environmental performance of fleet electrification. When battery cost exceeds $200/kWh, vehicles with greater battery range cannot continuously improve travel electrification and can reduce electrification rate. At the current battery cost of$400/kWh, targeting subsidies to vehicles with battery range around 90 miles can achieve higher electrification rate. The public charging station siting case demonstrates that individual travel patterns can better estimate charging demand and guide charging infrastructure development. Charging stations sited according to individual travel patterns can increase electrification rate by 59% to 88% compared to existing sites. Lastly, the ride sharing case shows that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides in Beijing can reduce total travel distance and air emissions by 33% with 10-minute travel time deviation tolerance. Only minimal tolerance to travel time change (4 minutes) is needed from the riders to enable significant ride sharing (sharing 60% of the trips and saving 20% of travel distance). In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems.PhDNatural Resources and Environment and Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113510/1/caih_1.pd

Ageing and Mobility in Germany: Are Women Taking the Fast Lane?

Results from travel demand research in many countries show that - on average - women are less mobile and have different mobility patterns than men. Recent longitudinal studies of gender specific travel demand reveal converging mobility of males and females. Moreover, in some countries results show convergence between cohort and gender specific travel demand: women and men display more and more similar travel behaviour while older individuals today have higher mobility demands than ever before. Do these developments hold also for Germany? Based on socio-economic and demographic analysis of gender specific travel behaviour using the German mobility survey data from 2002, we ask what individual travel patterns can be expected for the future in the year 2025. We place emphasis on the importance of educational attainment and labour force participation for the assessment of future personal mobility.travel demand, cohort effects, gender, households, ageing population

The Importance of Being Early

The assumption that the penalty for being early is less than that for being late was put forward by Vickrey (1963) who analyzed how commuters compare penalties in the form of schedule delay (due to peak hour congestion), against penalties in the form of reaching their destination (ahead or behind their desired time of arrival). This assumption has been tested by many researchers since then for various applications, especially in modeling congestion pricing (Arnott et al., 1990) where it is critical to understand the tradeoff between schedule delay and travel delay. Key findings are summarized in the second section of this paper. This research aims to test this hypothesis of earliness being less expensive than lateness using empirical data at different levels and across different regions. New methods to estimate the ratio of earliness to lateness for different types of datasets are developed, which could be used by agencies to implement control policies like congestion pricing or other schemes more accurately. Travel survey data from metropolitan areas provide individual travel patterns while loop detector data provide link level traffic flow data.Schedule Delay, Travel Time, Traffic, Travel Behavior.

Mapping Women\u27s Movement in Medieval England

This thesis investigates women’s geographical movement in medieval England from the perspective of mobility and freedom. It uses pilgrimage accounts from medieval miracle story collections and to gather information about individual travel patterns. The study uses GIS to analyze gendered mobility patterns, and to investigate whether there were noticeable differences in the distance which men and women traveled and the geographical area of the country they originated. It also analyzes the nearness of men’s and women’s respective origin towns to alternative pilgrimage locations, as a means of examining the factors determining gendered travel mobility. The study finds that women’s travel distances were less than men’s, especially in the later medieval period, but that they were in fact more likely than men to come from areas proximate to alternative pilgrimage sites. This suggests the existence of higher mobility capacity for women living in areas with greater contact with other travelers

Residential Self-Selection and Travel:

Most Western national governments aim to influence individual travel patterns – at least to some degree – through the spatial planning of residential areas. Nevertheless, the extent to which the characteristics of the built environment influence travel behaviour remains the subject of debate among travel behaviour researchers. This work addresses the role of residential-self-selection, an important issue within this debate. Households may not only adjust their travel behaviour to the built environment where they live, but they may also choose a residential location that corresponds to their travel-related attitudes. The empirical analysis in this work is based on data collected through an internet survey and a GPS-based survey, both of which were conducted among homeowners in three centrally located municipalities in the Netherlands. The study showed that residential self-selection has some limited effect on the relationship between distances to activity locations and travel mode use and daily kilometres travelled. The results also indicate that the inclusion of attitudes can help to detecting residential self-selection, provided that studies comply with several preconditions, such as the inclusion of the ‘reversed’ influence of behaviour on attitudes

Evaluation of the mobility impacts of the Dutch Vinex policy

Mobility reduction and modal shift towards public transport, walking and cycling were important aims of the Dutch spatial policy from the nineties (VINEX). This policy encompassed several criteria for new housing developments, to limit the mobility these generate. This paper reports on a study into the mobility consequences of the developments that were the result of this VINEX policy. It discusses the compliance of these locations with policy criteria and analyses the (car) travel behaviour of their inhabitants. The study focusses on the spatial situation of all newly built housing from the period 1995-2003 and the travel behaviour of their occupants. Part of these are classified as VINEX developments or dwellings, others are not developed as part of the VINEX policy and are referred to as non-VINEX. Results were obtained from detailled analyses of spatial characteristics and regression analyses of individual travel patterns. Differences between sections of the population and their specific characteristics are controlled for. The results show that the situation on the VINEX developments is largely in accordance with policy intentions, both with regard to proximity and accessibility. Many houses have been built within the existing urban area and the location of green field developments in relation to urban centres is favourable. Public transport facilities are on average better for VINEX dwellings, than elsewhere. Policy implementation was less succesful with regard to mixing land uses and the distances to daily amenities. The mobility generated proves to vary strongly between different types of locations. New developments, in general, generate more motorised mobility than average. Locations that were developed as part of the VINEX policy do better than non-VINEX. Especially the innercity VINEX-developments is characterised by low car use, despite the fact that they are inhabited by a relatively mobile section of the population. However, the results also show that innercity developments are most useful when located in the older parts of cities. Car use was high on Vinex-greenfield locations, but that is mainly caused by the composition of the population. The spatial criteria for VINEX developments, proximity and accessibility, have in fact played an important role in the more favourable mobility pattern. Particularly the location near urban centres and the accessibility by public transport have contributed to the lower car use in VINEX developments in comparison to non-VINEX.