There is increasing interest in analysing spatial dependencies and network effects in travel behaviour. LeSage and Polasek (2005) examine commodity fl ow matrices by extending a gravity model, a tool widely used in the field of transport, to include spatial autoregression. Using a Monte Carlo simulation, Scott (2005, 2007) investigate the impact of social networks in discrete choice models.It is not entirely new to include network effects within empirical choice models. Durlauf (2001, 2002) AbstractThis article empirically tests for positive network effects in transit use by applying a spatial autoregressive logit mode choice model with 1997/98 work trip data from New York City. Positive network effects exist when people prefer to use transit together with other people as a result of social spill-over. Although these network preferences should differ for each person, because of statistical restrictions in the model, individual network preferences cannot be obtained. However, it is possible to derive econometrically a measure of aggregate network preference. This paper can be seen in the wider context of other recent work focusing on the analysis of spatial dependencies and network effects in travel.

This paper aims to account for important factors influencing bicycle use and focuses in particular on differences between 20 selected German municipalities with considerable variation in their bicycle mode share. Using data from the nation-wide survey Mobility in Germany 2002, a mode choice model for bicycling is developed. In an extension to previous research, social network or spillover effects as a measure of the city’s bicycling culture are also taken into account. These effects are modelled using an instrumental variable approach. It is shown that social network effects increase the probability of cycling for shopping and recreational trip purposes, but not for school, work or errands. Furthermore, it is found that cycling infrastructure matters only for shopping and errand trips. Finally, commuting trips by bicycle seem to be largely independent of any policy variables.

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As environmental concerns mount alongside increasing auto dependence, research has been devoted to understanding the number of automobiles households own. The 2000 US census public use micro sample is used to demonstrate the importance of preference formation in auto ownership by studying auto ownership among recent movers. Using a multinomial probit model, the paper demonstrates that residents in the US transit cities who moved from major metropolitan areas are more likely to own fewer vehicles than counterparts who moved from smaller metropolitan areas and non-metropolitan areas. It is concluded that these results are due to learned preferences for levels of car ownership. Once the self-reinforcing ‘cultural knowledge’ of living without cars is lost, it could be difficult to regain. A focus on children and young adults, familiarising them with alternatives to the car may be an important approach to developing collective preferences for fewer cars.

Using the 1997/98 New York Metropolitan Transportation Council household survey and United States Census, we estimate an instrumental variable probit model to test the innpact of contextual and endogenous social interaction effects on auto ownership and determine that the probability of car ownership is affected by both types of social interaction effects. Previous research focused only either on contextual effects, or, increasingly, on endogenous effects using contextual effects variables as instruments. Therefore we were unable to find studies looking at both social interaction effects simultaneously. Consistent with earlier results, we find that households have a higher probability of possessing a vehicle if they are surrounded by other automobile-owning households (endogenous effect). However, we find that contextual effects are correctly measured only when the endogenous effect is included. In our case, everything else being equal, households in poorer neighborhoods are more likely to own vehicles, and households in neighborhoods with higher proportions of people with graduate degrees are less likely to own vehicles. This suggests that car ownership in New York City is a status symbol for poorer households and that non-car-ownership is a status symbol for people with post baccalaureate education. The results are important in two policy contexts: as auto ownership is a precursor to trip generation and mode choice, auto ownership estimation is important to effective travel forecasting; as vehicle miles traveled (VMT) is tied to auto ownership, VMT reduction strategies, as a way to improve air quality, reduce congestion, and reduce greenhouse gas emissions, may depend on strategies to reduce auto ownership. In either case, correct modeling of auto ownership will lead to more effective policy outcomes.