El punto de partida: la construcción del derecho de dominio en el ius commune

7.1.1 LTS Applications

The LTS modeling results presented in this report are part of an exploratory study where variables are analyzed one at the time utilizing cumulative logistics models (a.k.a. ordinal logistics models). A complete modeling effort will require many more steps including the analysis of correlations among independent variables as well as the analysis of informative interactions among variables. For example, to quantify the impact of peak hour traffic on comfort levels, especially on direct routes for commuters, is the interaction between short/fast route selection and time of day variables.

The preliminary study only explored a subset of variables and one variable at the time; i.e. if there was a relationship between route comfort and some trip attributes (length, duration, and average speed), trip temporal characteristics (weekday/time of day), route choice factors, route stressors, user attitudes, and socio-demographics.

Many potentially important and useful variables should be explored in the future. Some of these variables include: bicycle facility and street typology, topography, traffic volume, and roadway posted traffic speed. With a large number of observations it may be even possible to explore what design elements affect comfort, e.g. bulb outs, chicanes, and speed bumps in a bicycle boulevard. Some of these variables such as facility type, traffic volume, and traffic speed are the key variables currently used to determine LTS levels. However, before performing this study is necessary to geo-match GPS coordinates to a GIS network already loaded with the roadway type, bicycle facilities, traffic volume, and traffic speed attributes. This task is beyond the scope and budget of this research project.

A comprehensive comfort modeling study must simultaneously study pooled (i.e. not one

variable at the time) models were many groups of variables are jointly estimated. It is possible to estimate cumulative logistic regression models by carefully running forward or backwards stepwise regressions.

Based on the results observed in the pilot study it seems possible to calibrate cyclist LTS levels utilizing empirical data from Oregon facilities and users. The current LTS levels seem intuitive but have not been yet empirically validated. In addition, potential applications include the development of LTS tables that target different demographics (age, gender, etc.), trip purposes (e.g. commuters vs. recreational), as well as urban vs. suburban or rural environments.

7.1.2 Prioritization of Network Improvements

Cyclists’ routes by comfort levels and purpose can be compared to shortest paths to identify long detours. By identifying mismatches between actual routes and shortest paths transportation planners can identify where users take longer detours that lead to more comfortable routes; it is also possible to identify nodes or areas along the shortest paths where improvements are needed. Previous LTS work has also utilized the existing tables to identify islands or areas that are not connected by links with adequate LTS level. The same can be done by utilizing revealed data. A unique feature of ORcycle is that users can also submit reports regarding safety issues and crashes; this is an additional source of data that can complement route comfort and LTS data. Similarly, it may be possible to perform before/after analysis of bicycle infrastructure

improvements and how new infrastructure impact route comfort levels. Given uninterrupted ORcycle data planners can quantify the difference in volumes using a particular facility after it is improved. Perhaps more importantly, planners can use the demographic questions associated with cyclists to see if different types of cyclists are using a new facility. Further, transportation planners can analyze if the comfort experienced by a single cyclist (or group) changed with the provision of new infrastructure.

7.1.3 Crash and Injury Risk Models

Researchers in Montreal have successfully combined GPS routes from Mon RésoVélo with bicycle counts and geocoded crash data to develop an injury risk model (Strauss et al. 2015). The GPS traces from the Mon RésoVélo application were combined with point bicycle counts to form bicyclist exposure rates for each link in Montreal’s network. The crash/injury data is then modeled over the exposure rates to model the risk of injury in the network. The data from

ORcycle in combination with bicycle counts and geocoded crash data could be used to reproduce or improve upon this modeling in Oregon. ORcycle dataset has the potential to build an

enhanced crash model since it also collects crash information from its users.

7.1.4 Oregon User Types

When compared with previous applications, ORcycle has more demographic and cyclist type questions, more details about riders’ trips, and more safety data. Factor and cluster analyses could be used to group Oregon cyclist types. While Geller’s “Four types of cyclists”

(in Portland and elsewhere), the categories are based on limited empirical data. Geller’s original categorization in 2006 made educated guesses about the proportion of the Portland population falling with the four categories. Geller’s proportions were approximately validated by a randomized phone survey (Dill and McNeil 2012). However, this typology was not validated using revealed preference data and has not been validated outside of Portland.

This application could be crucial to validate assumptions about what facility types are preferred by different types of cyclists in different types of environments that include not also urban but also suburban and rural areas.

7.1.5 Enhanced Route Choice Models

Oregon Metro’s bicycle route choice model was developed utilizing empirical data collected in Dill and Gliebe’s bicycle GPS study (Dill and Gliebe 2008). While this model was a positive development it was based on a relatively small sample of cyclists (164 cyclists) and trips (1,449 trips); in addition, most trips are contained within the limits of the City of Portland. As ODOT and other local transportation agencies make cycling an increasingly central focus of their transportation planning efforts, it will be useful to develop bicycle route choice models to

effectively analyze and predict the needs of growing cycling populations in other urban networks with high connectivity. In suburban and rural areas LTS data and analysis can be utilized to identify links that need to be improved.