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Exploring the effect of the built environment, weather condition and departure time of travel on mode choice decision for different travel purposes: Evidence from Isfahan, Iran

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Abstract

A growing number of researchers have investigated the role of key factors that affect transport mode choice. Scant studies, however, have tried to incorporate the built environment factors at origin and destination, weather condition, departure time, and different trip purposes into mode choice models. To address these shortcomings, we developed four multinomial logit (MNL) models to analyze travel mode choice decision for different purposes in the context of a developing country, Iran. Travel data drawn from household travel survey conducted by Isfahan Municipality in 2015 and weather parameters were retrieved from five stations located inside the city. The results of models reveal some important insights. While entropy index and average block size strongly influence transport mode decisions, other built environment factors have weak associations with transport modes. Further, low temperature and low relative humidity decrease the probability of transit, motorcycle and bicycle usage over automobile. The impact of weather condition on discretionary trips is stronger than that of work trips. Apart from mentioned variables, socio-demographic characteristics and departure time of travel are other important variables. Findings of this paper indicate that nonphysical strategies in tandem with land use policies should be considered based on local condition.

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The effects of neighbourhood-level land use characteristics on urban travel behaviour of Iranian cities are under-researched. The present paper examines such influences in a microscopic scale. In this study the role of socio-economic factors is also studies and compared to that of urban form. Two case-study neighbourhoods in west of Tehran are selected and considered, first of which is a centralized and compact neighbourhood and the other is a sprawled and centreless one. A Multinomial Logit Regression model is developed to consider the effects of socio-economic and land use factors on urban travel pattern. In addition, to consider the effective factors, cross-sectional comparison between the influences of local accessibility and attractiveness of the neighbourhoodcentres of the two case-study areas are undertaken. Also the causality relationships are considered according to the findings of the survey. The findings indicate significant effects of age and household income as socio-economic factors on transportation mode choice in neighbourhoods with central structure. One the other hand, no meaningful association between socio-economic or land use variables are resulted by the model for the sprawled case. The most effective land use concept in micro-scale is considered to be satisfaction of entertainment facilities of the neighbourhood. Also the descriptive findings show that the centralized neighbourhood that gives more local accessibility to shops and retail generates less shopping trips. In considering the causal relations, the study shows that providing neighbourhood infrastructures that increase or ease the accessibility to neighbourhood amenities can lead to higher shares of sustainable transportation modes like walking, biking, or public transportation use.
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In this paper, using longitudinal data on route level monthly average weekday ridership in the entire Chicago Transit Authority (CTA) bus system from January 2002 through December 2010, we evaluate the ridership effects of the CTA real-time bus information system. This bus information system is called CTA Bus Tracker and was incrementally implemented on different CTA bus routes from August 2006 to May 2009. To take account of other factors that might affect bus ridership, we also include data on unemployment levels, gas prices, local weather conditions, transit service attributes, and socioeconomic characteristics during the study period. This combined longitudinal data source enables us to implement a quasi-experimental design with statistical controls to examine changes in monthly average weekday ridership, before and after the Bus Tracker system was implemented, on each bus route. Based on a linear mixed model, we found that the provision of Bus Tracker service does increase CTA bus ridership, although the average increase is modest. Further, the study findings suggest that there are temporal variations of the ridership effects among the routes, with the “winning” routes more likely to have the technology implemented in the later phases of the overall “roll-out” period. However, the results are less conclusive regarding geographical variations in the effects of Bus Tracker.
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This research aims to inform the compact city discussion with a case study of Beijing, where urban planning has emphasised clustered suburban development in the past half-century. It uses three decades of census data to describe Beijing’s spatial development trajectory and a household survey to assess its transport impacts. The research reveals an overconcentration of urban activities as a result of the featureless expansion of the central built-up area and the absorption of the suburban clusters; and, a lengthened commuting time stemming from the observed spatial development pattern. Beijing’s experience adds to the existing literature by informing the search for good city forms in urban areas of high density. It is essential to differentiate compact development from overconcentration when combating sprawling development. Developing and maintaining suburban nodal characteristics around public transit can reduce travel in high-density urban areas.
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Weather conditions have been recognized as important factors affecting school commute mode choices. This paper aims to explore the modal shift of middle school commutes with respect to the variation in weather-related variables, with empirical emphases on the situation in Beijing, China. Data from the latest Beijing School Commute Survey (2014–2015) were adopted, and multinomial probit (MNP) and multinomial logit (MNL) models were developed. The modeling results are in favor of the MNP model because it has better statistical performance. Weather-related variables, including sky condition, wind speed, highest temperature, humidity, air quality index (AQI), and some interaction terms, were found to have a significant impact on students' commute mode choices. Based on these models, an empirical sensitivity measure was defined as the expected percentage change in the probability of choosing each mode with respect to an order of magnitude change in the influential factors. Most of the results are in line with those of previous studies, and some unique results reflect features of Beijing. For example, on days with extremely poor air quality, students are more likely to turn to public transport rather than use a car from active transportation modes. This is probably due to the special urban traffic regulations that restrict household car ownership and car travel in Beijing. These findings could have implications for promoting active transportation for students and serve as references for policymakers and planners.
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In the context of sustainable urban transport in developing countries, individuals’ travel behavior faces multiple factors which influence their mobility patterns. Recognizing these factors could be a favorable method to organize more regular and sustainable trip patterns. This study aims to identify the less well-known lifestyle along with more popular built environment as the main factors which shape travel behaviors. Employing data from 900 respondents of 22 urban areas in city of Shiraz, Iran, this paper explores travel behaviors as non-working trip frequencies by different modes. Results of structural equation model indicate a strong significant effect of individual’s lifestyle patterns on their non-working trips. However, built environment impact on travel behavior is small compared to lifestyle. Besides, other variables such as travel attitudes and socio-economic factors stay crucial in the mode choice selection. These findings indicate the necessity of regarding lifestyle orientations in travel studies as well as objective factors such as land use attributes.
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Objective: Many research papers examine the relationship of the built environment on transport behaviour using only one mode of transport. Yet to inform policy makers, a broader examination of transport mode choice across different transport modes is required. Here, associations between urban design attributes and transport mode choices including transport walking, transport cycling, public transport and private motor vehicle use were explored. Methods: Secondary analysis was conducted on 16,890 participants aged 18 years or older who participated in the Victorian Integrated Survey of Travel Activity 2009-2010 (VISTA09) in metropolitan Melbourne, Australia. Adjusted multilevel logistic regression models were used to explore the relationship between urban design attributes and transport-walking, cycling, public transport and private motor vehicle use. Results: Taking transport-walking, cycling or public transport trips was positively associated with the housing diversity score and gross dwelling density. Taking private motor vehicle trips was negatively associated with street connectivity, land use mix, local living score, housing diversity score, gross dwelling density and proximity to supermarkets. Conclusion: The study found that environments that neighbourhoods with gross residential densities exceeding 20 dwellings per hectare, a well-connected street network, access to 9 or more local living destinations and short distances to public transport services (i.e., ≤ 400 m for bus and ≤ 800 m for train) encourage walking, cycling and public transport use, while discouraging driving. Comprehensive integrated urban planning of transport infrastructure, land use development and service provision is required to create neighbourhoods that support active and sustainable living that allow for a flexible mix of land uses and transport options.
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This study provides a baseline for understanding the variation in walking behavior with respect to seasonal conditions in a hot weather country in the Arabian Gulf region. A busy neighborhood with different land-use patterns in Qatar was observed for the different seasons during the year. Pedestrians’ demographics and walking characteristics were determined and compared across the different seasons. A spatial distribution method was used to analyze and display the distribution of the pedestrian in the neighborhood. A majority of middle-age men were observed walking in the streets of the studied neighborhood. Limited female, old, and young pedestrians were observed. The different seasons highly affected the pedestrian volumes and various walking activities. The summer season showed the lowest pedestrian volumes, and the winter showed the highest. This study highlights the need for different strategies to be adopted throughout the year to promote walking in this region, especially during the summer season. Special strategies should target the female, old, and youth population.
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The impacts of the built environment characteristics in residential neighborhoods on commuting behavior are explored in the literature. Scant evidence, however, is provided to scrutinize the role of the built environment characteristics at job locations. Studies also overlooked the potential error correlations between commuting mode and commuting distance due to the unobserved factors that influence both variables. We examined the impacts of the built environment characteristics at both residential and job locations on commuting mode and distance, by applying a discrete-continuous copula-based model on 857 workers in Shanghai. In contrast with studies of Western countries, we showed residential built environment characteristics are more influential on commute behavior than the built environment characteristics at job locations. This suggests the importance of local specificity in policymaking process. We also found the proportion of four-way intersections, road density, and population density in residential areas are negatively associated with driving probability, with elasticity amounts of -1.00, -0.23, and -0.08, respectively. Hence, dense and pedestrian- and cyclist-oriented development help to reduce travel distance and encourage walking, biking, and transit modes of travel.
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This paper investigates the influence of built environment measures on trip distance and walking decision of non-workers by segmenting the populace based on trip purpose, vehicle ownership, and the presence of school-going children in households. The built environment measures of home zone of individuals considered for the present analysis include zonal population density, zonal school enrolment, land use mix diversity index, and an indicator variable that captures if neighbourhoods have footpaths of adequate width available. Statistical analyses conducted on home-based trips indicate that an increase in the land use diversity of a zone has its strongest negative effect on distance travelled for participating in personal/household business activities. The non-vehicle owning group exhibit a higher tendency to walk than the vehicle-owning group for an increase in the land use diversity of zones. Further, the study suggests that school-enrolment in a zone also influences the travel decisions of non-workers in families with school-going children.
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This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.
Conference Paper
Within the transport sector, much of the climate change research conducted to date has focussed on mitigation. This includes for example, strategies to reduce emissions from our transport system that is heavily dependent on carbon based fuels. Adaptation research is now expanding in the transport sector with most effort addressing issues such as the vulnerability of infrastructure to higher sea levels or more frequent and severe storms. An area of adaptation that has received little attention in the literature relates to how individuals will adjust their travel behaviour in the face of changes in weather and climate. This paper examines the relationship between weather and travel behaviour with an emphasis on bicycling. Evidence from the literature is used to highlight how weather and climate affect active transport users. Examination of result from an aggregate bicycle demand model, developed using Melbourne data, confirms that around half of the variations in bicyclist volume can be explained by the changes in weather parameters. A non-linear effect on ridership of key weather parameters, specifically rainfall and temperature, is identified. This paper assesses adaptation behaviour in face of weather and climate change and it has important implications for government strategies that seek to increase the role of active transport in urban areas.
Conference Paper
In the planning point of view, it is essential to have mode choice, due to the massive amount of incurred in transportation systems. The intercity travellers in Libya have distinct features, as against travellers from other countries, which includes cultural and socioeconomic factors. Consequently, the goal of this study is to recognize the behavior of intercity travel using disaggregate models, for projecting the demand of nation-level intercity travel in Libya. Multinomial Logit Model for all the intercity trips has been formulated to examine the national-level intercity transportation in Libya. The Multinomial logit model was calibrated using nationwide revealed preferences (RP) and stated preferences (SP) survey. The model was developed for deference purpose of intercity trips (work, social and recreational). The variables of the model have been predicted based on maximum likelihood method. The data needed for model development were obtained from all major intercity corridors in Libya. The final sample size consisted of 1300 interviews. About two-thirds of these data were used for model calibration, and the remaining parts were used for model validation. This study, which is the first of its kind in Libya, investigates the intercity traveler’s mode-choice behavior. The intercity travel mode-choice model was successfully calibrated and validated. The outcomes indicate that, the overall model is effective and yields higher precision of estimation. The proposed model is beneficial, due to the fact that, it is receptive to a lot of variables, and can be employed to determine the impact of modifications in the numerous characteristics on the need for various travel modes. Estimations of the model might also be of valuable to planners, who can estimate possibilities for various modes and determine the impact of unique policy modifications on the need for intercity travel.
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The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.
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Expanding urban populations are inducing development at the edges of Indian cities, given the constraints on land use intensification within municipal boundaries. Existing peripheral towns are becoming anchors for this new growth, creating urban agglomerations. Such areas have become preferred home locations for the working poor and emergent middle class, groups that are often priced out of the urban housing market. However, many such exurban locations lack infrastructure such as durable paved roads and transit, because investments are largely clustered within municipal boundaries. This paper focuses on the Greater Mumbai Region and relies on a cross-sectional household travel survey data set. The objective is to understand how vehicle use is linked to the built environment and socio-economics. Spatial analysis shows that cars are used in urban centres while scooters and motorcycles are used in the exurbs. Estimated censored regression models show that greater household distance from the main employment centre Nariman Point, better job accessibility and improved socio-economic factors increase vehicle use, while land use diversity and density bring down vehicle use. A key econometric result is that after controlling for location, land use, infrastructure supply and socio-economics, the expectation of a motorised two-wheeler or car in a household does not translate to its use. Overall, the findings suggest that policies encouraging higher land use diversity, density and transit supply have the potential to marginally decrease vehicle use in the Indian metropolis. However, future research needs to focus on residential location to better understand how the choices of where to live and how to travel are interconnected.
Article
Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies.Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection.Methods: We computed elasticities for individual studies and pooled them to produce weighted averages.Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect of several such variables on travel could be quite large. Consistent with prior work, we find that vehicle miles traveled (VMT) is most strongly related to measures of accessibility to destinations and secondarily to street network design variables. Walking is most strongly related to measures of land use diversity, intersection density, and the number of destinations within walking distance. Bus and train use are equally related to proximity to transit and street network design variables, with land use diversity a secondary factor. Surprisingly, we find population and job densities to be only weakly associated with travel behavior once these other variables are controlled.Takeaway for practice: The elasticities we derived in this meta-analysis may be used to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment, or be used in sketch planning applications ranging from climate action plans to health impact assessments. However, because sample sizes are small, and very few studies control for residential preferences and attitudes, we cannot say that planners should generalize broadly from our results. While these elasticities are as accurate as currently possible, they should be understood to contain unknown error and have unknown confidence intervals. They provide a base, and as more built-environment/travel studies appear in the planning literature, these elasticities should be updated and refined.Research support: U.S. Environmental Protection Agency.
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A critical issue in the mitigation of transport CO2 emission and the development of low-carbon cities is the need to get a better understanding of factors that shape travel behavior, and resulting carbon emission. Using an activity diary survey and GIS-based land use data in Beijing, this research investigates how urban form characteristics at neighborhood and city scales impact individual's daily travel behavior and subsequent CO2 emission from work and non-work trips, respectively. Structural equation modeling (SEM) is adopted to examine the relationship between urban form, travel behavior, and CO2 emission, while accounting for residential self-selection and socio-demographic attributes. Results show that residents living in neighborhoods with higher job density, proximity to an employment sub-center and greater subway accessibility tend to travel shorter distance, choose low-carbon travel modes, and emit less CO2 from work related trips. People resident in neighborhoods with higher retail density or mixed land use tend to travel shorter distance and have less CO2 emission from non-work trips. The research also suggests that work related trips have larger variation than non-work trips across neighborhoods, indicating the job-housing spatial mismatch might be the main factor that drives up travel demand and transport CO2 emission in urban Beijing.
Article
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.
Article
This study presents a hybrid simulation model that combines logistic regression and cellular automata-based modelling to simulate future urban growth and development for the city of Ahmedabad in India. The model enables to visualize the consequence of development projections in combination with present zoning and development control regulations. The growth in activities’ floor space is projected at a zonal level using time series data. Then, a logistic regression model is used to calculate a probability surface of development transition, while a cellular automata-based spatial interaction model is used to simulate change in activity floor space per activity, and thus urban growth. The developed model has the capacity to simulate urban growth space and hence vertical growth. The structure of the model allows for a detailed urban growth simulation and is flexible enough to incorporate changes in development control regulations and settings for spatial interaction. Therefore, it carries scope of being used to visualize growth for other, similar, cities and help urban planners and decision makers to understand the consequences of their decisions on urban growth and development.
Article
In post-reform China, rapid motorisation causes various problems like traffic congestion, diminishing road safety and air pollution. Adequate policies necessitate an understanding of the forces behind changing mode choices, but the rapidly developing literature is not complete yet. This paper aims to help fill that gap with an analysis of mode choice for commuting and shopping-leisure trips in Nanjing. Using the Nanjing Residents Travel Survey, we find that models with the same independent variables explain mode choice in Nanjing better than in other cities in the world. Comparatively, members of ‘adult families’ use public transport and walking more often than the private car and bicycle. And inhabitants of danwei neighbourhoods walk more often than residents in commodity housing estates. These conclusions suggest that ongoing socio-spatial transformations will push mode choice in China further towards private car use.
Article
This paper analyses the influence of meteorological conditions on the number of public bus trips made for leisure, shopping and personal business in Gipuzkoa, Spain. The ridership transit data employed were obtained from the data generated by a CAD/AVL system (Computer-aided dispatch/Automatic Vehicle Location) that simultaneously manages the data coming from all o the bus operators in the region. So, the study analyses the trips actually made by the entire population without resorting to sample data or aggregate behavioural studies, confirming as an added value of smart technologies their potentialities as a source of information. To determine the reasons for travelling, only journeys made on Saturdays and Sundays were studied for all weekends in 2010 and 2011. Multiple linear regression results showed that wind and rain could result in a decrease in the number of trips, while a temperature rise caused an increase in the number of trips, in agreement with the results of previous survey-based studies. Finally, both regular and occasional travellers were found to share this behavioural pattern.
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To generalize the relationship between density and travel behavior, previous research proceeded with three approaches: metropolitan-level studies describing tendencies on an international scale, area-specific studies extrapolating their outcomes to other areas, and research syntheses pooling descriptive or quantitative outcomes of the studies. However, little research investigated the contextual effect of study areas on the density–travel relationship. Thus, this study conducts meta-analysis to investigate how the magnitude of the relationship differs between two areas that have been frequently studied: the United States and Europe. A pre-test shows that the way of measuring density and travel behavior does not affect the variation in study outcomes, whereas a post-test or sensitivity analysis indicates that the rigor of research designs and statistical techniques affects the variation. The main test finds that the density–travel relationship is significantly stronger in Europe than in the United States. The magnitude difference between the areas is maintained after controlling for confounders, including research design and technical rigor.
Article
The paper analyses the environmental correlates of cycling based on Danish transportation and urban form micro-data. The results show that established walkability factors such as density, connectivity and diversity are related to cycling, but access to retail concentrations/centres, public transportation level-of-service, as well as competition between walking and cycling depending on environmental features can be added. Attractive conditions for using public transportation or walking are related to less cycling. The findings quantify the effects of urban form on the probability of cycling and the distance cycled. A high probability of cycling generally implies short cycling distances leading to non-uniform, non-monotonous relationship between environmental indicators such as walkability and cycling.
Article
In the light of climate change, scholars from various disciplines recently addressed the role of weather conditions for travel behaviour. However, existing studies lack assessments of combinations of weather parameters and direct links to projected climate change. With this paper, we investigate potential effects of climate change on mode choice and distances travelled in the Randstad Holland. Based on approximate combinations of weather conditions projected for 2050, we select seasons from the last decade, to represent current and future climate conditions. By using data from the Dutch National Travel survey for the selected seasons, we analyse travel behaviour under 2050-climate conditions compared to travel behaviour under present climate conditions. Results show increasing usage and travelled distances for open-air transport modes in milder and wetter 2050-winters, mainly at the expense of the car, whereas in hotter summers with more extreme precipitation patterns reversed effects are observed. Year-round analyses of effects from 2050-climate conditions show a “flattening out” of seasonal differences in modal split, while for cycling mode shares and distances travelled significantly increase.
Article
This paper aims to describe the joint choice of residential location, travel mode, and departure time. First, based on random utility maximization theory, the Cross-Nested Logit model and traditional NL models are formulated respectively. House price, travel time, travel cost, and factors depicting the individual socio-economic characteristics are defined as exogenous variables, and the model choice sets are the combination of residential location subset, departure time subset, and travel mode choice subset. Second, using Beijing traffic survey data of 2005, the model parameters are estimated, and the direct and cross elasticity are calculated to analyze the change of alternatives probability brought by factors variation. Estimation results show the Cross-Nested Logit model outperforms the three kinds of NL model. It is also found by estimation results that decision makers will change first their departure times, then their travel modes, and finally their residential locations, when exogenous variables alter. Moreover, elasticity analysis results suggest that, for long-distance commuting, it is difficult to decrease car travels even if additional charges are imposed on car users. The effect on choice probability by variations in travel time of other travel mode can be considered as negligible for alternatives within 5 km commuting distance, and this effect are greatest for alternatives between 10 and 20 km commuting distance. These findings have important implications for transport demand management and residence planning.
Article
The increasing emission of transport-related pollutants has become a key issue in relation to climate change mitigation and the improvement of air quality in China's cities. This paper aims to examine the effects of changes in the built environment on transportation by examining the case of Beijing. Looking at household survey data, the analysis found that individual workers’ commuting behaviour (concerning travel destination, mode choice and travel time) is significantly related to some aspects of the built environment when socioeconomic and demographic characteristics are taken into account. There are obvious differences in the effects of the built environment on commuting across income groups, occupations and industries.
Article
Twelve months of automated hourly pedestrian counts in downtown Montpelier, Vermont (population 8,035), were analyzed along with weather data (temperature, relative humidity, precipitation, and wind) to determine the factors affecting count variability. This study is unique in that a large amount of data in a single location was collected in a locale with an extreme range of weather conditions. Results indicate consistent patterns in relative volumes by hour of the day and month of the year that show that good adjustment factors can be developed to use with time-limited counts to estimate usage and pedestrian exposure to accidents. Predictive relationships were found between weather variables, season, and pedestrian volumes (30% of the variation is accounted for). Consistent hourly patterns within a day and the consistency of day type (weekday or Saturday versus holiday or Sunday) suggest that correction factors and forecasting methods are feasible for pedestrian traffic volumes. The results indicate that weather such as cold temperature's or precipitation consistently reduces aggregate levels of walking by only a moderate amount (less than 20%). Precipitation and season are found to affect pedestrian levels even when time of day and day of week are controlled, but other, larger, unmeasured factors are at play.
Article
The authors examine the magnitude of health benefits from urban design characteristics that are associated with increased walking. Using geocoded travel diary data from Portland, Oregon, regression analyses give information on the magnitude and statistical significance of the link between urban design variables and two-day walking distances. From the coefficient point estimates, the authors link to the health literature to give information on how many persons would realize health benefits, in the form of reductions in mortality risk, from walking increases associated with urban design changes. Using a cost-benefit analysis framework, they give monetized estimates of the health benefits of various urban design changes. The article closes with suggestions about how the techniques developed can be applied to other cost-benefit analyses of the health benefits of planning projects that are intended to increase walking.
Article
While considerable attention has been paid to the public-health-related impacts of air pollution, relatively little research has been done to understand how other aspects of the built environment impact health. Americans are increasingly sedentary; erstwhile the rate of increase in obesity is alarming. New research suggests that increased auto dependence, and limited opportunities to walk for utilitarian purposes, has contributed to this emerging obesity epidemic. Within sociodemographic strata, land use patterns and transportation investments collectively shape the desire to walk, drive, or to travel via other means. Mixed use and more compact community designs show significant promise for the promotion of physical activity and the reduction of regional air pollution levels. Opportunities exist to increase physical activity and improve regional air quality through more compact development. However, increased compactness, or density, often exacerbates traffic congestion and can increase exposure of harmful emissions within central areas. Therefore, strategies to reduce localized air pollution in existing and developing centers are required to enable larger health benefits from smart growth to be realized.
Article
This article presents a study that analyzed the influence of land use on travel mode choice using survey data from Metropolitan Boston and Hong Kong. In Boston, the focus of inquiry was on whether land use would still matter for mode choice (and if so, to what extent) when mode attributes and traveler socioeconomic characteristics were taken into account. In Hong Kong, where the role of land use in mode choice is obvious due to the densely built environment, the focus was on whether land use completely explained the transit-dominated travel pattern. The empirical modeling confirmed that the role of land use in influencing travel was independent from travel time and monetary costs. Elasticity estimates show that the composite effect of land use on driving could be comparable in magnitude to that of driving cost. Yet being place specific, land use strategies are limited by the spatial extent to which they can be implemented. Land use strategies influence travel more effectively when complemented by pricing policies.