Residential location and daily travel distances: the influence of trip purpose

Elldér, Erik

doi:10.1016/j.jtrangeo.2013.11.008

Cited by 68 publications

(35 citation statements)

References 36 publications

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Section: Travel Distancesmentioning

confidence: 99%

“…Women are found to travel shorter distances than men (Akar et al, 2016;Elldér, 2014;Mercado & Páez, 2009). The elderly travel less frequently and when they do, they cover shorter distances (Akar et al, 2016;Elldér, 2014;Mercado & Páez, 2009;Schmöcker, Quddus, Noland, & Bell, 2005). Having a driver's license increases travel distances (Akar et al, 2016;Mercado & Páez, 2009;Morency, Páez, Roorda, Mercado, & Farber, 2011;Schmöcker et al, 2010).…”

Section: Travel Distancesmentioning

confidence: 99%

“…People with higher education levels tend to travel longer distances (Akar et al, 2009;Dieleman, Dijst, & Burghouwt, 2002). Household income and vehicle ownership increase travel distances (Akar et al, 2009;Dieleman et al, 2002;Elldér, 2014;Liu;Schmöcker et al, 2005). As built environment has been shown to influence travel distances significantly, land-use patterns around residential neighborhoods are widely examined and reviewed in existing studies (Ewing & Cervero, 2001;Ewing & Cervero, 2010).…”

Section: Travel Distancesmentioning

confidence: 99%

“…For instance, Dieleman, et al (2002) report that people living in suburbs or rural areas travel longer distances. As compared to living in the non-urban areas, Elldér (2014) shows that people who live in medium to large urban areas travel longer distances. Akar et al (2016) find that travel distances increase for those who live in the areas with higher percentage of single detached housing units and lower densities in population, employment, and intersections.…”

Section: Travel Distancesmentioning

confidence: 99%

See 3 more Smart Citations

Using trip chaining and joint travel as mediating variables to explore the relationships among travel behavior, socio-demographics and urban form

Chen

Akar

2017

JTLU

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Using the 2012 Household Travel Survey data for the Cleveland metropolitan area, this study aims to examine the connections between travel behavior by using trip chaining and joint travel as mediating variables of travel distances and controlling for sociodemographics and urban form. Trip chaining and joint travel capture the complexity of tours and intra-household interactions, respectively. Socio-demographics represent personal and household characteristics. Urban form, which is measured not only at tour origins but also at tour destinations, helps capture the effects of residential density, retail and non-retail densities, transportation connectivity, public transit accessibility, and land-use mix. Structural equation model (SEM) approaches are applied to examine the interrelationships among these variables. The model results reveal that significant effects with expected signs exist among travel behavior: Trip chaining is negatively associated with joint travel and positively related to travel distances, and joint travel has negative effects on travel distances. Consistent with existing literature, socio-demographic attributes are strong explanatory factors of travel behavior. Urban form characteristics have significant influence on travel distances at both tour origins and destinations. The findings of this study will improve the future evaluation of transportation projects and land-use policymaking.

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Section: Travel Distancesmentioning

confidence: 99%

Section: Travel Distancesmentioning

confidence: 99%

Section: Travel Distancesmentioning

confidence: 99%

Section: Travel Distancesmentioning

confidence: 99%

See 2 more Smart Citations

Using trip chaining and joint travel as mediating variables to explore the relationships among travel behavior, socio-demographics and urban form

Chen

Akar

2017

JTLU

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“…Depending on local conditions, the distance of everyday trips may vary and exceed even 55 km [79]. Average distances are also longer on weekdays than during the weekends [80]. Future research may want to explore the possibility of incorporating urban metabolism analyses, which may assess the volume of people spending time in specific parts of the city.…”

Section: Discussionmentioning

confidence: 99%

The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)

Kazak

2018

Sustainability

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Abstract:The increasing level of antropopression has a negative impact on environmental resources and has reached the level of our planetary boundaries. One limitation is land use change caused by urbanization. Global policies prove the need to undertake action in order to develop more sustainable human settlements, which would be adapted better to potential future climate change effects. Among such changes are the increase of average temperatures and extreme events like heat waves. Those changes are more severe in urban areas due to land use development, and result in the urban heat island effect (UHI), which has a negative impact on the thermal comfort of citizens. The paper presents a decision support system that can be used for the assessment of areas to the potential exposure to the UHI effect. The system integrates scenario analysis, land use modelling in cellular automata (Metronamica), and an indicator-based assessment in a geographic information system (ArcGIS). The applicability of the model is illustrated through developing scenarios for the future land use allocation of the Wrocław Larger Urban Zone (Poland). The results of the calculations show which scenario is the least vulnerable to UHI effects. Moreover, for each scenario, cores of urban areas were identified, in which certain urban design patterns accounting for adaptation to climate change could be implemented. The study provides a guideline for local authorities on where to focus actions in order to create more sustainable urban structures and to better adapt to climate change and environmental extremes.

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Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks

Yao

Yang

et al. 2019

Machine Learning and Knowledge Discovery in Databases

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Residential location and daily travel distances: the influence of trip purpose

Cited by 68 publications

References 36 publications

Using trip chaining and joint travel as mediating variables to explore the relationships among travel behavior, socio-demographics and urban form

Using trip chaining and joint travel as mediating variables to explore the relationships among travel behavior, socio-demographics and urban form

The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)

Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks

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