Bruce N. Janson

Estimating Annual Average Daily Bicyclists: Error and Accuracy

Cities around the United States are investing in bicycle infrastructure, and to secure additional transportation funding, cities are reporting bicycle use and safety improvements. Data on bicyclist traffic volume is necessary for performing safety studies and reporting facility use. Meeting the need for data, available manual bicycle counting programs count cyclists for a few hours per year at designated locations. A key issue in the design of counting programs is determining the timing and frequency of counts needed to obtain a reliable estimate of annual average daily bicyclists (AADB). In particular, in which days of the week, hours of the day, and months of the year should counts be collected? And, most important to program cost, how many hours should be counted? This study used continuous bicycle counts from Boulder, Colorado, to estimate AADB and analyze the estimation errors that would be expected from various bicycle-counting scenarios. AADB average estimation errors were found to range from 15% with 4 weeks of continuous count data to 54% when only 1 h of data was collected per year. The study found that the most cost-effective length for short-term bicycle counts is one full week when automated counting devices specifically calibrated for bicycle counting are used. Seasons with higher bicycle volumes have less variation in bicycle counts and thus more accurate estimates.

Automated Bicycle Counts: Lessons from Boulder, Colorado

Quantifying bicycle use is fundamental to understanding bicycle travel. Methods of counting bicycles vary from limited-time, manual counts to permanent overhead imaging sensors. One common permanent counting method uses inductive loops embedded in the pavement to count cyclists on paths. Although inductive loop detectors have been found to be a highly accurate method of counting bicycles under ideal test conditions, their accuracy after years of use has not been systematically studied. This study focuses on bicycle counts collected by the City of Boulder, Colorado, since 1998, on multiuse paths with inductive loop detectors. To estimate the accuracy of the devices in use, two individuals manually counted path users at six locations. On average, the loop detectors counted 4% fewer bicycles than the manual counters at the same locations. Of the 22 detector channels with sufficient counts to judge their accuracy, roughly 68% were considered accurate. The most dramatic inaccuracies were caused primarily by detector settings and software-related problems. This study found that inductive loop detectors can provide accurate measures of bicycle use on a pathway, but only when detectors are properly installed, calibrated, maintained, and free of external interference.

Bicyclist safety performance functions for a U.S. city

Efforts have intensified to apply a more evidence-based approach to traffic safety. One such effort is the Highway Safety Manual, which provides typical safety performance functions (SPFs) for common road types. SPFs model the mathematical relationship between frequency of crashes and the most significant causal factors. Unfortunately, the manual provides no SPFs for bicyclists, despite disproportionately high fatalities among this group. In this paper, a method for creating city-specific, bicycle SPFs is presented and applied to Boulder, Colorado. This is the first time a bicycle SPF has been created for a U.S. city. Such functions provide a basis for both future investigations into safety treatment efficacy and for prioritizing intersections to better allocate scarce funds for bicycle safety improvements. As expected, the SPFs show that intersections with higher bicyclist traffic and higher motorist traffic have higher motorist-cyclist collisions. The SPFs also demonstrate that intersections with more cyclists have fewer collisions per cyclist, illustrating that cyclists are safer in numbers. Intersections with fewer than 200 entering cyclists have substantially more collisions per cyclist.

Spatial Allocation of Transportation Greenhouse Gas Emissions at the City Scale

Greenhouse gas (GHG) accounting for individual cities in large metropolitan areas is confounded by spatial scale and boundary effects that impact the allocation of transportation fuels used for surface transport and airline travel. This paper expands on a demand-based methodology to spatially allocate transportation fuel use (surface and airline) among colocated cities in the United States that are part of a larger metropolitan area commutershed on the basis of demand for surface vehicle miles traveled (VMT) exerted by individual cities. By using travel demand models for metropolitan planning organizations, the demand method was first applied as part of the city of Denver’s GHG inventory in 2005 to develop a material flow analysis (MFA) of gasoline, diesel, and jet fuel consumption at the city scale. This paper reports on the application of the same method to six major metropolitan regions across the United States and a detailed analysis of all 27 cities within the larger Denver metro region. The analysis...

Preliminary analysis of light rail crashes in Denver, Colorado: implications for crash prediction and hazard index models based on railroads

This paper presents a preliminary analysis of light rail crashes at at-grade crossings in Denver, Colorado, based on Regional Transportation District data for 1999 through 2009. Differences in design and operation of at-grade crossings are discussed for light rail versus common carrier railroad (railroad). The differences appear to warrant the development of separate crash prediction and hazard index models because models developed for railroad at-grade crossing operations may not accurately predict the number and severity of crashes at light rail at-grade crossings. In addition, the models developed for railroads do not predict crashes at crossings for some traffic control device types such as traffic signals. The lack of information for crossings controlled by traffic signals in the railroad crash prediction equations is one reason why equations specific to light rail may need to be developed. This study identifies patterns in light rail crossing crash data that warrant further investigation and support the development of crash prediction models and hazard index equations specific to light rail at-grade crossing configurations and operations.

ANALYSIS OF LANE-BLOCKING EVENTS WITH AN ANALYTICAL DYNAMIC TRAFFIC ASSIGNMENT MODEL

An analytical-based dynamic traffic assignment (DTA) model intended for off-line analysis and evaluation of non-recurring lane-blocking events is proposed. Formulated as a variational inequality (VI), this model solves an ideal dynamic user-optimal (DUO) route choice problem. A diagonalization algorithm is proposed to solve the model to a prespecified convergence using a targe-scale, real-life traffic network. Adjustments of link capacities triggered by external lane-blocking events and internal modeled traffic phenomena are explicitly considered in the solution algorithm to capture queue formation and dissipation. The proposed solution procedure approximates route choices based on anticipatory and non-anticipatory traffic

Dynamic network modelling of travel guidance impacts

This paper describes network modelling of travel guidance impacts using combined dynamic distribution and assignment (CDDA) in which a subset of area motorists are provided real-time directives as to destination, route, and departure time decisions. Examples illustrate potential magnitudes of system-wide impacts, including travel time and fuel consumption, achieved by different percentages of motorists given directives aimed at improving user equilibrium conditions. Travel guidance regarding modal choice is not included in this paper, although that extension is similar to other network models. Results are given for a Pittsburgh network in which freeway lanes are partially blocked during rush hour due to a truck accident. Indications are that significant reductions in system-wide impacts can be achieved by guiding a subset of area motorists to specific travel choices based on real-time information. Future research issues are types, amounts, and locations of real-time data needed to provide reliable travel guidance, and the relative impacts of in-vehicle versus point-of-departure travel guidance systems.

Estimating Lives Saved and Injuries Reduced by Motorcycle Helmet Use in Colorado, 2006–2014

Many rigorous studies have shown the effectiveness of helmet use in reducing severe injuries and fatalities to motorcyclists. NHTSA reports estimates of lives saved by U.S. motorcyclists wearing or potentially wearing helmets. This calculation uses a statistically derived estimate of helmet effectiveness in preventing deaths to motorcyclists involved in crashes. Two parts of the estimate of lives saved are (a) deaths prevented assuming crashes occurred that did not kill helmeted motorcyclists in proportion to those that did kill helmeted motorcyclists and (b) deaths that may have been prevented if unhelmeted motorcyclists killed in crashes had been helmeted. Similar estimates can be made for injuries. More readily available crash data enable analysts to investigate whether crashes in their jurisdictions support the inherent assumptions of these estimates. Moreover, the estimates can be stratified to include other crash characteristics such as crash type and rider gender and age. This study compared estimates of potential fatality and injury reductions based on NHTSA averages with estimates with the use of all reported crashes on Colorado state and federal roads in the years 2006 to 2014. Helmet effectiveness is found to depend on crash characteristics in Colorado and does vary from national averages.

Light rail crossing safety performance functions

This paper presents the development of safety performance functions (SPFs) for light rail crossings or roadways. The paper also develops an empirical Bayes method for adjusting the initial crash estimates from the SPF to account for the actual crash experiences at light rail crossings. The validity of the light rail crossing SPFs is compared with that of the U.S. Department of Transportation (DOT) crash prediction models. It is found that SPFs specific to light rail crossings provide improvements to crash estimates that are statistically significant compared with that of the U.S. DOT crash prediction models.

Roadway Incident Analysis with a Dynamic User-Optimal Route Choice Model

The transportation system conveys interdependencies. When analysing the costs and benefits of transport investment projects, it is therefore necessary to address the question of linkages among projects. Such linkages can occur in terms of economies of scale in arising from the combination of projects during the construction phase. Intelligent Transportation Systems (ITS), also known as Intelligent Vehicle Highway Systems (IVHS), are applying advanced technologies (such as navigation, automobile, computer science, telecommunication, electronic engineering, automatic information collection and processing) in an effort to bring revolutionary improvements in traffic safety, network capacity utilization, vehicle emission reductions, travel time and fuel consumption savings, etc. Within the framework of ITS, Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) both aim to manage and predict traffic congestion and provide historical and realtime network-wide traffic information to support drivers’ route choice decisions. To enable ATMS/ATIS to achieve the above described goals, traffic flow prediction models are needed for system operation and evaluation. Linkages may also arise in supply through interaction among network components, or among the producers of transportation services. Linkages may also emerge in demand through the creation of new opportunities for interaction.