Using the 2011 Swedish national travel survey data, this paper explores the influence of weather ... more Using the 2011 Swedish national travel survey data, this paper explores the influence of weather characteristics on individuals' home-based trip chaining complexity. A series of panel mixed ordered Probit models are estimated to examine the influence of individual/household social demographics, land use characteristics, and weather characteristics on individuals' home-based trip chaining complexity. A thermal index, the universal thermal climate index (UTCI), is used in this study instead of using directly measured weather variables in order to better approximate the effects of the thermal environment. The effects of UTCI are segmented into different seasons to account for the seasonal difference of UTCI effects. Moreover, a spatial expansion method is applied to allow the impacts of UTCI to vary across geographical locations, as individuals in different regions have different weather/climate adaptions. The effects of weather are examined in subsistence, routine, and discretionary trip chains. The results reveal that the 'ground covered with snow' condition is the most influential factor on the number of trips chained per trip chain among all other weather factors. The variation of UTCI significantly influences trip chaining complexity in autumn but not in spring and winter. The routine trip chains are found to be most elastic towards the variation of UTCI. The marginal effects of UTCI on the expected number of trips per routine trip chain have considerable spatial variations, while these spatial trends of UTCI effects are found to be not consistent over seasons.
Understanding mechanisms underlie the individual's daily time allocations is very important to un... more Understanding mechanisms underlie the individual's daily time allocations is very important to understand the variability of individual's time-space constraints and to forecast his/her daily activity participation. At most of previous studies, activity time allocation was viewed as allocating a continuous quantity (daily time budget) into multiple discrete alternatives (i.e. various activities and trips to engage with). However, few researches considered the influence of travel time that needs to be spent on reaching the activity location. Moreover, travel time itself is influenced by individuals' mode choice. This can lead to an over-or underestimation of particular activity time location. In order to explicitly include the individual's travel time and mode choice considerations in activity time allocation modelling, in this study, a nested multivariate Tobit model is proposed. This proposed model can handle: 1. Corner solution problem (i.e. the present of substantial amount of zero observations); 2. Time allocation trade-offs among different types of activities (which tends to be ignored in previous studies); 3. Travel is treated as a derived demand of activity participation (i.e. travel time and mode share are automatically censored, and are not estimated, if corresponding activity duration is censored). The model is applied on a combined dataset of Swedish national travel survey (NTS) and SMHI (Swedish Meteorological and Hydrological Institute) weather record. Individuals' work and non-work activity durations, travel time and mode shares are jointly modelled as dependent variables. The influences of time-location characteristics, individual and household socio demographics and weather characteristics on each dependent variable are examined. The estimation results show a strong work and non-work activity time trade-offs due to the individual's time-space constraints. Evidences on a potential positive utility of travel time added on non-work activity time allocation in the Swedish case, are also found. Meanwhile, the results also show a consistent mode choice preference for a given individual. The estimated nested multivariate Tobit model provides a superior prediction, in terms of the deviation of the predicted value against the actual value conditional on the correct prediction regarding censored and non-censored, compared to mutually independent Tobit models. However, the nested multivariate Tobit model does not necessarily have a better prediction for model components regarding non-work related activities.
By jointly modelling the routine and leisure activity–travel engagements of non-commuters in diff... more By jointly modelling the routine and leisure activity–travel engagements of non-commuters in different regions of Sweden, this paper explores the interactions between time allocation, travel demand and mode choice under different weather conditions. Combined weather and travel survey datasets that span a period of over 13 years were analysed. Simultaneous Tobit models were applied to explore the interactions among these activity–travel indicators, whilst municipalities' unique conditions and heterogeneities between different time-points were taken into account. The model results reveal the trade-offs between routine and leisure activities in terms of activity duration, number of trips and travel time. Positive mutual endogeneity was found between slow-mode share in routine and leisure trips. The results also highlight the trade-offs between routine and leisure activities under abnormal weather conditions. Regional differences between weather effects are substantial due to differences in direct, indirect and total marginal effects. Between-municipality variability constitutes a considerable part of the variability in activity duration and travel time. Between-municipality variability in leisure activity duration and leisure travel time is larger in northern Sweden, while that of routine activity duration and routine travel time is larger in central Sweden, after weather and social demographics have been controlled.
Understanding travel behaviour change under various weather conditions can help analysts and poli... more Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.
This paper investigates the influence of weather on the Swedish people's mode choice decision in ... more This paper investigates the influence of weather on the Swedish people's mode choice decision in different seasons and regions using a long term series of the Swedish National Transport Survey datasets (NTS) and weather data from the Swedish Meteorological and Hydrological Institute (SMHI). The weather data includes mean of daily temperature, amount of rain precipitation and road surface condition. The daily mean temperature is normalised based on each region and season and classified into five categories as 'very cold', 'cold', 'normal', 'warm', and 'very warm'. This normalisation approach enables us to investigate the impact of individual's heterogeneity in perceiving regional and seasonal variability of temperature. The impacts of these weather indicators' variability on individual's mode choice is investigated with multinomial logit models. The results show that the impacts of weather differ in different seasons and different regions. Pedestrians' perception of variation of temperature differs between those in the northern Sweden and those in the central and southern Sweden. Such perception also differs in summer and in spring and autumn. Similarly, northern Sweden cyclists are more aware of temperature variation than cyclists in the central and southern Sweden in spring and autumn when temperature changes significantly. The influence of temperature variation on motorised modes also varies among seasons and regions. However, the trend is less straightforward than that on non-motorised modes. The findings highlight the importance to incorporate individual and regional unique anticipation and adaptations behaviours within our policy design and infrastructure management.
Using the 2011 Swedish national travel survey data, this paper explores the influence of weather ... more Using the 2011 Swedish national travel survey data, this paper explores the influence of weather characteristics on individuals' home-based trip chaining complexity. A series of panel mixed ordered Probit models are estimated to examine the influence of individual/household social demographics, land use characteristics, and weather characteristics on individuals' home-based trip chaining complexity. A thermal index, the universal thermal climate index (UTCI), is used in this study instead of using directly measured weather variables in order to better approximate the effects of the thermal environment. The effects of UTCI are segmented into different seasons to account for the seasonal difference of UTCI effects. Moreover, a spatial expansion method is applied to allow the impacts of UTCI to vary across geographical locations, as individuals in different regions have different weather/climate adaptions. The effects of weather are examined in subsistence, routine, and discretionary trip chains. The results reveal that the 'ground covered with snow' condition is the most influential factor on the number of trips chained per trip chain among all other weather factors. The variation of UTCI significantly influences trip chaining complexity in autumn but not in spring and winter. The routine trip chains are found to be most elastic towards the variation of UTCI. The marginal effects of UTCI on the expected number of trips per routine trip chain have considerable spatial variations, while these spatial trends of UTCI effects are found to be not consistent over seasons.
Understanding mechanisms underlie the individual's daily time allocations is very important to un... more Understanding mechanisms underlie the individual's daily time allocations is very important to understand the variability of individual's time-space constraints and to forecast his/her daily activity participation. At most of previous studies, activity time allocation was viewed as allocating a continuous quantity (daily time budget) into multiple discrete alternatives (i.e. various activities and trips to engage with). However, few researches considered the influence of travel time that needs to be spent on reaching the activity location. Moreover, travel time itself is influenced by individuals' mode choice. This can lead to an over-or underestimation of particular activity time location. In order to explicitly include the individual's travel time and mode choice considerations in activity time allocation modelling, in this study, a nested multivariate Tobit model is proposed. This proposed model can handle: 1. Corner solution problem (i.e. the present of substantial amount of zero observations); 2. Time allocation trade-offs among different types of activities (which tends to be ignored in previous studies); 3. Travel is treated as a derived demand of activity participation (i.e. travel time and mode share are automatically censored, and are not estimated, if corresponding activity duration is censored). The model is applied on a combined dataset of Swedish national travel survey (NTS) and SMHI (Swedish Meteorological and Hydrological Institute) weather record. Individuals' work and non-work activity durations, travel time and mode shares are jointly modelled as dependent variables. The influences of time-location characteristics, individual and household socio demographics and weather characteristics on each dependent variable are examined. The estimation results show a strong work and non-work activity time trade-offs due to the individual's time-space constraints. Evidences on a potential positive utility of travel time added on non-work activity time allocation in the Swedish case, are also found. Meanwhile, the results also show a consistent mode choice preference for a given individual. The estimated nested multivariate Tobit model provides a superior prediction, in terms of the deviation of the predicted value against the actual value conditional on the correct prediction regarding censored and non-censored, compared to mutually independent Tobit models. However, the nested multivariate Tobit model does not necessarily have a better prediction for model components regarding non-work related activities.
By jointly modelling the routine and leisure activity–travel engagements of non-commuters in diff... more By jointly modelling the routine and leisure activity–travel engagements of non-commuters in different regions of Sweden, this paper explores the interactions between time allocation, travel demand and mode choice under different weather conditions. Combined weather and travel survey datasets that span a period of over 13 years were analysed. Simultaneous Tobit models were applied to explore the interactions among these activity–travel indicators, whilst municipalities' unique conditions and heterogeneities between different time-points were taken into account. The model results reveal the trade-offs between routine and leisure activities in terms of activity duration, number of trips and travel time. Positive mutual endogeneity was found between slow-mode share in routine and leisure trips. The results also highlight the trade-offs between routine and leisure activities under abnormal weather conditions. Regional differences between weather effects are substantial due to differences in direct, indirect and total marginal effects. Between-municipality variability constitutes a considerable part of the variability in activity duration and travel time. Between-municipality variability in leisure activity duration and leisure travel time is larger in northern Sweden, while that of routine activity duration and routine travel time is larger in central Sweden, after weather and social demographics have been controlled.
Understanding travel behaviour change under various weather conditions can help analysts and poli... more Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.
This paper investigates the influence of weather on the Swedish people's mode choice decision in ... more This paper investigates the influence of weather on the Swedish people's mode choice decision in different seasons and regions using a long term series of the Swedish National Transport Survey datasets (NTS) and weather data from the Swedish Meteorological and Hydrological Institute (SMHI). The weather data includes mean of daily temperature, amount of rain precipitation and road surface condition. The daily mean temperature is normalised based on each region and season and classified into five categories as 'very cold', 'cold', 'normal', 'warm', and 'very warm'. This normalisation approach enables us to investigate the impact of individual's heterogeneity in perceiving regional and seasonal variability of temperature. The impacts of these weather indicators' variability on individual's mode choice is investigated with multinomial logit models. The results show that the impacts of weather differ in different seasons and different regions. Pedestrians' perception of variation of temperature differs between those in the northern Sweden and those in the central and southern Sweden. Such perception also differs in summer and in spring and autumn. Similarly, northern Sweden cyclists are more aware of temperature variation than cyclists in the central and southern Sweden in spring and autumn when temperature changes significantly. The influence of temperature variation on motorised modes also varies among seasons and regions. However, the trend is less straightforward than that on non-motorised modes. The findings highlight the importance to incorporate individual and regional unique anticipation and adaptations behaviours within our policy design and infrastructure management.
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