Mapping Bicycle Crash Risk Patterns on the Local Scale

Approaching Bike Hazards via Crowdsourcing of Volunteered Geographic Information

Information on individual hazard perception while cycling and the associated feeling of safety are key aspects to foster sustainable urban cycling mobility. Although cyclist’s perceptions must also be critically reviewed, such crowdsourced Volunteered Geographic Information (VGI) provides wide-ranging insights on diverse hazard categories in cycling. In this case study in the city of Freiburg, Germany, hazard perceptions, information about lane types, and the underlying routes were crowdsourced via an open source smartphone application by a small group with the aim of providing cyclists with effective solutions. By dealing with levels of reliability, we show that even a small group of laypersons can generate an extensive and valuable set of VGI consisting of comprehensive hazard categories. We demonstrate that (1) certain hazards are interlinked to specific lane types, and (2) the individual hazard perceptions and objective parameters, i.e., accident data, are often congruent spatially; consequently, (3) dangerous hot spots can be derived. By considering cyclists’ needs, this approach outlines how a people-based perspective can supplement regional planning on the local scale.

Investigation of the consequences of the modifiable areal unit problem in macroscopic traffic safety analysis: A case study accounting for scale and zoning

Traffic safety analysis at the macroscopic level usually relies on previously defined areal traffic analysis zones (TAZs) that are used as the units of investigation. Hence, statistical inference is made on the basis of such units, implying that the consideration of a certain TAZ configuration may influence the results and conclusions achieved. Regarding this, the modifiable areal unit problem (MAUP) is a well-known issue in the field of spatial statistics, which refers to the effects that arise in statistical properties and estimations when there is a change in areal units of analysis. In this paper, the consequences of MAUP have been investigated through a dataset of traffic crashes that occurred in Valencia within the years 2014 and 2015 and two common statistical models: a conditional autoregressive model and a geographically weighted regression. In the absence of an established TAZ scheme for the city, four classes of basic spatial units (BSUs) were considered: census tracts, hexagonal units and two types with construction based on the structure of main roads and intersections of the city. Each of these BSU types was specified at different levels of spatial aggregation. The main research objective was to investigate the final effects that changes in BSU type and scale have on model parameter estimations, but also the specific alterations that MAUP causes to data in terms of the distributional characteristics of the response, multicollinearity among the covariates and covariates' spatial autocorrelation. The results showed the presence and severity of MAUP for the dataset and area that were analysed. Although effects from scale variations were more moderate, changing the BSU type affected the results severely. The joint use of hexagonal units and a conditional autoregressive model achieved the best performance among all the possibilities explored, but the choice of a proper BSU unit should rely on more factors. Despite MAUP effects, educational centres showed a consistent (and negative) association with traffic crashes, a fact possibly related to their distribution across the whole city. Other covariates revealed a positive correlation with crash counts, but these findings were more uncertain given the discrepancies found at different scales and zonings.

Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers' ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with one- or two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4-way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.

[A spatially explicit analysis of traffic accidents involving pedestrians and cyclists in Berlin]

BACKGROUND

In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive.

OBJECTIVES

1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions.

MATERIAL AND METHODS

Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS).

RESULTS

While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area.

CONCLUSIONS

In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.