Bicycle Mode Share Research Articles

Bicycle Use in Germany: Explaining Differences between Municipalities with Social Network Effects

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.

Macroscopic Model of Greenhouse Gas Emissions for Municipalities

In the challenge to reduce greenhouse gas (GHG) emissions from the transportation sector, urban municipalities hold significant responsibilities. The Municipal Transportation and Greenhouse Gas (MUNTAG) model was developed to help municipalities estimate their current transportation emissions, set future targets, and run forecasting scenarios and responses to policies. A set of seven criteria was adopted for the development of the model including low input, ease of use, and feasibility (i.e., the model includes only variables that are controlled by municipalities). The model contains five strategies: land use intensification, public transport, active transport, financial policies, and vehicle technology. Each use is addressed separately and then integrated into one working model. Motorized passenger kilometers traveled (PKT) per capita is first estimated with the gross domestic product per capita and population density. With transit infrastructure indicators, PKT per capita is calculated for each transit mode. Bicycle infrastructure is included to calculate the bicycle mode share. Response to several financial policies (e.g., parking price, area pricing) can be modeled by using elasticity statistics gathered from the literature. Finally, changes in vehicle technology (e.g., hybrid electric vehicles) can be modeled by adjusting the various emission factors. One advantage of the model is that all parameters can be adapted fairly easily to account for municipal specificities. Overall, it is a macroscopic, aggregate, and static model suited for medium-sized and large municipalities that can be useful as a screening tool.

Analyzing the Effect of Bicycle Facilities on Commute Mode Share over Time

This study employs United States census data to analyze changes in bicycle commuting between 1990 and 2000 in the Minneapolis-St. Paul, Minn. area. A variety of perspectives are used to understand the impact of newly created facilities. The evidence suggests that bicycle facilities significantly impact levels of bicycle commuting, although the results are not totally free of uncertainty. For example, areas near new bicycle facilities showed considerably more of an increase in bicycle mode share than areas farther away. Observing increased cycling due to these physical interventions provides a starting point to which future research could add detail that would be needed to guide infrastructure investment.

Modeling Bicycle Facility Operation

Current concerns surrounding regional air pollution, climate change, rising gasoline prices, and urban congestion could presage a substantial increase in the bicycle mode share. However, state-of-the-art methods for the safe and efficient design of bicycle facilities are based on difficult-to-collect data and potentially dubious assumptions regarding cyclist behavior. Simulation models offer a way forward, but existing bicycling models in the academic literature have not been validated with actual data. These shortcomings are addressed by obtaining real-world bicycle data and implementing a multilane, inhomogeneous cellular automaton simulation model that can reproduce observations. The existing literature is reviewed to inform the data collection and model development. It is found that the model emulates field conditions while possibly underpredicting bike path capacity. Since the simulation model can “observe” individual cyclists, it is ideally suited to determine level of service based on difficult-to-observe cycling events such as passing. Future work on data collection and model development is suggested.

Estimation of the determinants of bicycle mode share for the journey to work using census data

A model is presented that relates the proportion of bicycle journeys to work for English and Welsh electoral wards to relevant socio-economic, transport and physical variables. A number of previous studies have exploited existing disaggregate data sets. This study uses UK 2001 census data, is based on a logistic regression model and provides complementary evidence based on aggregate data for the determinants of cycle choice. It suggests a saturation level for bicycle use of 43%. Smaller proportions cycle in wards with more females and higher car ownership. The physical condition of the highway, rainfall and temperature each have an effect on the proportion that cycles to work, but the most significant physical variable is hilliness. The proportion of bicycle route that is off-road is shown to be significant, although it displays a low elasticity (+0.049) and this contrasts with more significant changes usually forecast by models constructed from stated preference based data. Forecasting shows the trend in car ownership has a significant effect on cycle use and offsets the positive effect of the provision of off-road routes for cycle traffic but only in districts that are moderately hilly or hilly. The provision of infrastructure alone appears insufficient to engender higher levels of cycling.

Bicycle-associated head injuries and deaths in the United States from 1984 through 1988, how many are preventable? : J.J. Sacks, P. Holmgreen, S.M. Smith, D.M. Sosin, Journal of the American Medical Association, 266(21), 3016–3018.

Today, North American governments are more willing to consider compact neighborhoods with increased use of sustainable transportation modes. Bicycling, one of the most effective modes for short trips with distances less than 5 km is being encouraged. However, as vulnerable road users (VRUs), cyclists are more likely to be injured when involved in collisions. In order to create a safe road environment for them, evaluating cyclists’ road safety at a macro level in a proactive way is necessary. In this paper, different generalized linear regression methods for collision prediction model (CPM) development are reviewed and previous studies on micro-level and macro-level bicycle-related CPMs are summarized. On the basis of insights gained in the exploration stage, this paper also reports on efforts to develop negative binomial models for bicycle–auto collisions at a community-based, macro-level. Data came from the Central Okanagan Regional District (CORD), of British Columbia, Canada. The model results revealed two types of statistical associations between collisions and each explanatory variable: (1) An increase in bicycle–auto collisions is associated with an increase in total lane kilometers (TLKM), bicycle lane kilometers (BLKM), bus stops (BS), traffic signals (SIG), intersection density (INTD), and arterial–local intersection percentage (IALP). (2) A decrease in bicycle collisions was found to be associated with an increase in the number of drive commuters (DRIVE), and in the percentage of drive commuters (DRP). These results support our hypothesis that in North America, with its current low levels of bicycle use (<4%), we can initially expect to see an increase in bicycle collisions as cycle mode share increases. However, as bicycle mode share increases beyond some unknown ‘critical’ level, our hypothesis also predicts a net safety improvement. To test this hypothesis and to further explore the statistical relationships between bicycle mode split and overall road safety, future research needs to pursue further development and application of community-based, macro-level CPMs.