Gordon Richard Lovegrove

Macrolevel Collision Prediction Models to Enhance Traditional Reactive Road Safety Improvement Programs

The reactive use is described of 35 recently developed macrolevel collision prediction models (CPMs) to conduct a black spot study with data from 577 urban and rural neighborhoods across Greater Vancouver in British Columbia, Canada. The research objective was to investigate macrolevel CPM use in a traditional reactive safety application (macroreactive use): identification, diagnosis, and remedy of hazardous locations. The results suggested that macroreactive use has the potential to complement traditional road safety improvement programs. Several collision-prone zones were identified and ranked for diagnosis. Two zones were analyzed in detail and revealed several potential enhancements to conventional methods. If adopted for normal use by practitioners, macrolevel CPMs could facilitate improved decisions by community planners and engineers and ultimately could facilitate improved neighborhood road safety for residents and other road users.

Sustainable road safety: A new (?) neighbourhood road pattern that saves VRU lives

Both the UN (2007) and World Health Organizations (2004) have declared the enormous social and economic burden imposed on society by injuries due to road collisions as a major global problem. While the road safety problem is not new, this prominent global declaration sends an important signal of frustration regarding progress to date on reducing road collisions. It is clear that governments, communities, businesses and the public must discover ways of reducing this burden, especially as it relates to vulnerable road users (VRUs), typically meaning pedestrian and bicyclist road users. Recent comparisons of global VRU collisions statistics suggest that, in addition to mixed land use density, the layout of neighbourhood roads plays a vital role in the encouragement of walkable, safe and quiet, yet accessible and sustainable communities. The purpose of this paper was to: The Dutch Sustainable Road Safety (SRS) Program has produced a number of innovative land use and transportation initiatives for vehicular road users as well as non-vehicular VRUs. Following from the Dutch initiatives, these new 3-way offset, and fused grid neighbourhood patterns appear to not only have positive effects in encouraging mode split (i.e. increasing walking and bicycling, and transit), slowing traffic, and reducing energy consumption and GHG emissions; but also, to hold potential to improve road safety. To test the road safety hypothesis, UBCO researchers evaluated the level of road safety relative to five neighbourhood patterns - grid, culs-de-sac, and Dutch Sustainable Road Safety (SRS) (or limited access), 3-way offset, and fused grid networks. Analysis using standard transportation planning methodology revealed that they would maintain both mobility and accessibility. Analysis using standard road safety analysis methodology further revealed that these 3-way offset, and fused grid patterns would significantly improve road safety levels by as much as 60% compared to prevalent patterns (i.e. grid and culs-de-sac). It is important to note that these results ignore the road safety effects of providing convenient off-road trails along trip desire lines to shift mode choice from auto to non-auto VRU modes. Subject to further research, it is intuitive that shifting trips from auto to pedestrian/bike modes will lead to reduced auto collisions. Hence, these initial results should be considered as conservative estimates, subject to further research. In before and after studies to date, researchers have shown that increasing bicycle use does not lead to a commensurate increase in bicycle collisions, but no predictive relationship has been found in the literature. Therefore, the next steps in this research are to develop collision prediction models that provide insight on VRU mode split and overall road safety.

Community-Based, Macrolevel Collision Prediction Model Use with a Regional Transportation Plan

This paper describes the application of previously developed community-based, macrolevel collision prediction models (CPMs) to evaluate the road safety of a regional transportation plan. The research objective was to present and test model-use guidelines in a regional road safety planning application. The data was extracted from over 400 Greater Vancouver neighborhoods in British Columbia, Canada, including output from the regional transportation model. The CPMs predicted a lower mean collision frequency region-wide due to a proposed three-year transportation plan, versus a do-nothing scenario. Recommendations have been made for future use of the CPMs in regional road safety planning applications. The application of macrolevel CPMs to this regional case study proved a solid step in the development of new and improved empirical tools for planners and engineers to include road safety in the planning process. It is hoped that these models and model-use guidelines will facilitate improved decisions by community planners and engineers, and ultimately, facilitate improved neighborhood traffic safety for residents and other road users.

Community-based, macro-level collision prediction models

The burden on communities due to the enormous economic and social costs associated with road collisions has been recognized worldwide as a major problem of epidemic proportions. Given the magnitude and persistence of the problem, spanning many decades, organizations worldwide have initiated engineering and research programs to improve road safety. There are two main transportation engineering approaches to improving the safety performance of the road component: reactive and proactive. The reactive or traditional engineering approach has been to address road safety in reaction to existing collision histories. While it has proven to be very successful, road safety authorities and researchers are also pursuing more proactive engineering approaches. Rather than working reactively to improve the safety of existing facilities, the proactive engineering approach to road safety improvement focuses on predicting and improving the safety of planned facilities. Reactive and proactive programs both rely heavily on reliable empirical techniques, including collision prediction models (CPMs). Reactive programs use micro-level collision prediction models, which focus on a single facility. Reliable micro-level C P M methods and techniques have been well researched and refined. However, while micro-level CPMs successfully support the reactive engineering approach, several shortcomings have been identified related to unsuccessful attempts by planners and engineers to use them in proactive road safety planning. Given these shortcomings of micro-level CPMs in planning-level (i.e. macro-level) road safety evaluations, there exists a research gap of reliable empirical tools to pursue road safety in a proactive manner. In view of this lack of reliable macro-level empirical tools, the main goal of this thesis was to develop macro-level CPMs, and to provide guidelines for their use by planners and engineers, so that road safety could be explicitly considered and reliably estimated in all stages of the road planning process. The approach taken included developing macro-level CPMs using extensive data extraction and Generalized Linear Regression Modeling (GLM) regression techniques, and then developing guidelines for use of those models based on several case studies of road safety planning applications.

Macro-Level Collision Prediction Models Related to Bicycle Use

The growing automobile transport results in severe traffic congestion, pollution and road safety problems. Bicycling, as one of sustainable transportation mode, is encouraged in most developed countries for its attributes of convenience, low cost, non-fuel use, and zero-emissions. It is generally accepted that increasing bicycle use could improve road safety. Based on a comprehensive literature review, this paper discusses potential factors influencing bicycle use and bicycle collisions. Understanding these bicycle-related factors is useful to develop new bicycle-related Collision Prediction Models (CPMs) with generalized linear regression. These CPMs can support economic justification of much-needed major bicycle infrastructure investments, and also help policy makers to promote bicycling in an effective and economic manner. Also, a brief methodology of developing such macro-level CPMs is suggested. Based on a case study of City of Kelowna, BC, Canada, several new macro-level CPMs are proposed. Results reveal that the increase of bicycle use can lead to a decrease in total collisions despite an increase in bicycle collisions, which is consistent with the actual case. Also, the bicyclerelated exposure variable, bicycle lane length, has a significantly positive relationship with dependent variables: total collision frequency. In this case, it is concluded that increasing bike lanes (on-street and off-street) can be a good measure to improve road safety. Still, aimed on the research gap, this paper identifies potential works of high interests in future.

Freight Demand Forecast for a Proposed Railway in Canada With New Approach to Freight Rail Assignment

This research examined the freight demand forecast for a new short railway linking the Okanagan Valley in southern British Columbia to American railways in the South (Orville), and to Canadian railways in the North (Kamloops). An Origin-Destination (O-D) table including local, domestic and international demands for the Okanagan freight rail was developed based on available surveys and observed truck freight data. In the absence of data to derive utility functions, the current mode share for each commodity in the base year as well as current elasticities between truck and rail was used to forecast the mode share in the future year. Rail assignment techniques are among the forgotten problems of freight demand forecasting due to their complexities, including: 1) written and unwritten practices of the rail industry, and 2) cost functions that are classically employed in truck or auto assignments. In this study, a comprehensive review was conducted on the rail freight demand assignment techniques. A new assignment procedure was introduced by combining the available mathematical choice models and new initiatives of the Canadian government toward rail industry. Finally, the predicted share of freight rail was assigned to the rail network using three methods, which provided three independent freight demand forecasts. The mid-range forecast was selected as the freight demand for the Okanagan Valley while two others (low/high) were used for sensitivity analysis.Copyright