A significant portion of the population of developing countries resides in rural areas. Despite the importance of rural transportation planning in such countries, little is known about the activity travel characteristics of these rural residents. The topic has been largely ignored in transportation studies. To contribute to the scant literature, this study focused on a classification of representative activity travel–based clusters (RACs) in rural areas of Iran. A place-based interview questionnaire was designed, and data about activity and travel characteristics of rural residents were collected in May 2012. On the basis of the distance between a village and the nearest city, nearby and distant villages were considered in the survey. The statistical analysis used 665 questionnaires. A classification procedure was applied to analyze the data. First, the application of factor analysis with Varimax rotation that used Kaiser criteria resulted in nine factors, extracted from 22 initial variables. The types of villages were used to compare the factors. Results indicated that descriptive factors of rural RACs were strongly affected by the distance between a village and the nearest city. Next, a cluster analysis, which resulted in nine RACs, was conducted. Each RAC consisted of a dominant activity type, activity location, and transportation mode. Through interpretation of the RACs, the rural population was categorized into nine lifestyles. Subsequently, the distribution of village type and respondent gender and age variables for each RAC was explored. Except for age categories, differences between distributions of the village type and gender in the RACs were considerable.

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Efficacious transportation, as prerequisites of sustainable development of rural areas, should receive pertinent attention. This needs more attention in developing countries as nearly half of population in these countries reside in rural areas. However, still little is known about the activity and travel characteristics of rural residents. This study focuses on activity-location (Ac-Lo) patterns of rural residents in Iran. An interview of place-based questionnaire was designed and data about activity-travel characteristics of rural residents was collected. In total, 663 respondents were used for analyzing out-of-home Ac-Lo patterns. Based on a descriptive analysis of Ac-Lo characteristics of the sample, 10 Ac-Lo patterns were determined, differentiated according to the primary and secondary activity type and the primary activity location of rural residents. To explore the relations between Ac-Lo patterns and socio-demographic characteristics of rural residents and the type of village, logit models were used. Based on specification test, two nested logit structures were identified modeling the joint decision of choosing activity and location. The first structure was nested based on activity location, inside or outside the village, while the second was nested based on primary activity type, work, education and other activities. The selected structure emphasized the role of activity location in activity-travel patterns in rural areas. The results also demonstrated the important role of socio-demographic variables and job type of rural residents on joint Ac-Lo decisions. Village type of either near to city or far from it was the only variable appeared in the higher level of the nested structure, and had a significant influence on individuals' membership of the inside the village Ac-Lo patterns.

Car use in the sprawled urban region of Noord-Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High-Tech Campus (2018) and the TU/e-campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car-dependent. Of the non-car-dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provision) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.