Bicyclist Exposure Estimation Using Heterogeneous Demand Data Sources

Quantifying risks and the effects of risk factors requires controlling for exposure,/nor the number of opportunities for the adverse outcome in question to occur. In the/ncontext of traffic crashes, traffic volumes are frequently used as an exposure measure./nEfforts to study bicyclist crash risk have historically been hindered by the lack of/nwidespread exposure data. This study presents methods to estimate bicycle traffic/nvolumes across an entire urban network./nThe first major chapter of the dissertation presents a data schema for classifying/nbicycle demand datasets. There is an ever-growing abundance of transportation data,/nwith some of the fastest growth seen in realm of non-motorized demand. However,/nall of the available datasets provide incomplete information about the system. For/nexample, some only represent a time series of observations at a single location in/nspace (automated counters), while others cover all space and time but only represent/na small subset of the population of people and trips (crowdsourced data). In order/nto understand how these heterogeneous sources of information correspond to one/nanother, it was deemed necessary to first identify their differences. Six metadata/ncharacteristics were defined, which are termed the population scope, trip aggregation,/ntemporal scope, temporal resolution, spatial scale, and demographics. Levels are/ndefined for each dimension, and examples of generic datasets are discussed in terms/nof their metadata dimension./nThe second major chapter of the dissertation presents a method of fusing multiple/nlink-level demand estimates to infer peak-hour bicycle traffic volumes. While/nthe method is agnostic to the specific sources being used, it is presented with a/ncase study of San Francisco, CA using data from regional travel demand models,/na smartphone crowdsourcing application, and bikeshare system ridership. The defined process entails first converting the datasets to a common format in terms of/ntheir metadata dimensions, and then fitting these homogenized link-level estimates/nto observed counts using a weighted regression technique modeled after Geographically/nWeighted Regression. The fitting parameters associated with each dataset are/nhypothesized to vary geospatially, and the means by which this variation occurs is/ncontrolled by the specified weighting scheme. A distance decay weighting, where observations/nfurther from a given location contribute less to the parameter estimates, is/nfound to produce the best results. Cross-validation is employed for model comparison/nand the selection of features and hyperparameter values. It is shown that, on the/nbasis of cross-validated Root-Mean Square Deviation, that fusing data sources provides/ngreater predictive accuracy than can be achieved using any individual source,/nand that utilizing localized regression is more predictive than using a single global/nparameter for each data set./nThe final chapter is about inferring the temporal distribution of traffic based on/ncontinuous automated count data. Latent Dirichlet Allocation is applied as a signal/ndecomposition model to identify latent spatio-temporal patterns in the observed/ncount data, which appear to correspond to coherent activity patterns such as AM/ncommuting, PM commuting, and midday cycling. Each link’s temporal distribution/ncan thus be expressed in terms of the extent to which each latent pattern is observed/non it. The mixture of these patterns on unobserved links is interpolated using a/npurely autoregressive model, in contrast to the historically ad hoc methods used to/ndetermine the temporal characteristics of bicycle traffic on unobserved links./nThe primary conclusion of this work is that the lack of exposure data should no/nlonger be considered an insurmountable problem for studying bicycle crashes. Using/nadvanced analytical methods, such as those presented here, in conjunction with/nthe abundance of new datasets provides a means of generating defensible retrospective/nvolume estimates for the entire network. This dissertation paves the way for/nmany future lines of inquiry, including both refinements upon the methods presented/nhere and application of the volume estimates developed here to problems requiring/nexposure quantities, such as the evaluation of crash risk.

Advisor: Pozdnukhov, Alexey
Grembek, Offer