Julia B. Griswold

Association Between Roadway Intersection Characteristics and Pedestrian Crash Risk in Alameda County, California

Each year from 1998 to 2007, an average of approximately 4,800 pedestrians were killed and 71,000 pedestrians were injured in traffic crashes in the United States. Because many pedestrian crashes occur at roadway intersections, it is important to understand the intersection characteristics that are associated with pedestrian crash risk. The present study uses detailed pedestrian crash data and pedestrian volume estimates to analyze the pedestrian crash risk at 81 intersections along arterial and collector roadways in Alameda County, California. The analysis compares pedestrian crash rates (the number of crashes per 10,000,000 pedestrian crossings) with intersection characteristics. In addition, more than 30 variables were considered for use in the development of a statistical model of the number of pedestrian crashes reported at each study intersection from 1998 to 2007. After the pedestrian and motor vehicle volumes at each intersection were accounted for, negative binomial regression showed that significantly more pedestrian crashes occurred at intersections with more right-turn-only lanes, more nonresidential driveways within 50 ft (15 m), more commercial properties within 0.1 mi (161 m), and a greater percentage of residents within 0.25 mi (402 m) who were younger than age 18 years. Raised medians on both intersecting streets were associated with lower numbers of pedestrian crashes. These results, viewed in combination with other research findings, can be used by practitioners to design safer intersections for pedestrians. This exploratory study also provides a methodological framework for future pedestrian safety studies.

Factors Associated with Hit-and-Run Pedestrian Fatalities and Driver Identification

As hit-and-run crashes account for a significant proportion of pedestrian fatalities, a better understanding of these crash types will assist efforts to reduce these fatalities. Of the more than 48,000 pedestrian deaths that were recorded in the United States between 1998 and 2007, 18.1% of them were caused by hit-and-run drivers. Using national data on single pedestrian-motor vehicle fatal crashes (1998-2007), logistic regression analyses were conducted to identify factors related to hit-and-run and to identify factors related to the identification of the hit-and-run driver. Results indicate an increased risk of hit-and-run in the early morning, poor light conditions, and on the weekend. There may also be an association between the type of victim and the likelihood of the driver leaving and being identified. Results also indicate that certain driver characteristics, behavior, and driving history are associated with hit-and-run. Alcohol use and invalid license were among the leading driver factor associated with an increased risk of hit-and-run. Prevention efforts that address such issues could substantially reduce pedestrian fatalities as a result of hit-and-run. However, more information about this driver population may be necessary.

Pilot Models for Estimating Bicycle Intersection Volumes

Bicycle volume data are useful for practitioners and researchers to understand safety, travel behavior, and development impacts. Several simple models of bicycle intersection volumes were developed for Alameda County, California. The models were based on 2-h bicycle counts performed at a sample of 81 intersections in the spring of 2008 and 2009. Study sites represented areas with a wide range of population density, employment density, proximity to commercial property, neighborhood income, and street network characteristics. The explanatory variables considered for the models included intersection site, land use, transportation system, and socioeconomic characteristics of the areas surrounding each intersection. Four alternative models were developed with adjusted R2 values ranging from .39 to .60. The models showed that bicycle volumes tended to be higher at intersections surrounded by more commercial retail properties within1 .10mi, closer to a major university, with a marked bicycle facility on at least one leg of the intersection, surrounded by less hilly terrain within1 .2mi, or surrounded by a more connected roadway network. The models also showed several important differences between weekday and weekend intersection volumes. The positive association between bicycle volume and proximity to retail properties or a large university was greater on weekdays than on weekends, whereas bicycle facilities had a stronger positive association and hilly terrain had a weaker negative association with bicycle volume on weekends than on weekdays. The study found that further testing and refinement was necessary before accurate count predictions could be made in Alameda County or other communities.

Visual assessment of pedestrian crashes

Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

Tradeoffs between Costs and Greenhouse Gas Emissions in the Design of Urban Transit Systems

Recent investments in the transit sector to address greenhouse gas emissions have concentrated on purchasing efficient replacement vehicles and inducing mode shift from the private automobile. There has been little focus on the potential of network and operational improvements, such as changes in headways, route spacing, and stop spacing, to reduce transit emissions. Most models of transit system design consider user and agency cost while ignoring emissions and the potential environmental benefit of operational improvements. We use a model to evaluate the user and agency costs as well as greenhouse gas benefit of design and operational improvements to transit systems. We examine how the operational characteristics of urban transit systems affect both costs and greenhouse gas emissions. The research identifies the Pareto frontier for designing an idealized transit network. Modes considered include bus, bus rapid transit (BRT), light rail transit (LRT), and metro (heavy) rail, with cost and emissions parameters appropriate for the United States. Passenger demand follows a many-to-many travel pattern with uniformly distributed origins and destinations. The approaches described could be used to optimize the network design of existing bus service or help to select a mode and design attributes for a new transit system. The results show that BRT provides the lowest cost but not the lowest emissions for our large city scenarios. Bus and LRT systems have low costs and the lowest emissions for our small city scenarios. Relatively large reductions in emissions from the cost-optimal system can be achieved with only minor increases in user travel time.

Unintended greenhouse gas consequences of lowering level of service in urban transit systems

Public transit is often touted as a ‘green’ transportation option and a way for users to reduce their environmental footprint by avoiding automobile emissions, but that may not be the case when systems run well below passenger capacity. In previous work, we explored an approach to optimizing the design and operations of transit systems for both costs and emissions, using continuum approximation models and assuming fixed demand. In this letter, we expand upon our previous work to explore how the level of service for users impacts emissions. We incorporate travel time elasticities into the optimization to account for demand shifts from transit to cars, resulting from increases in transit travel time. We find that emissions reductions are moderated, but not eliminated, for relatively inelastic users. We consider two scenarios: the first is where only the agency faces an emissions budget; the second is where the entire city faces an emissions budget. In the latter scenario, the emissions reductions resulting from reductions in transit level of service are mitigated as users switch to automobile.

Old Road, New Directions: Plan for Adeline Street in Berkeley, California

This paper presents a plan for transforming a major arterial and a transit station that divide a low-income business and residential district into an economically healthy, context-sensitive, transit-oriented development. Adeline Street is a major point of entry into Berkeley, California, linking the city of Oakland on the south to the central districts of Berkeley. Formerly a major streetcar corridor, Adeline is now the location of a Bay Area Rapid Transit (BART) station and a surface parking lot, for which housing and businesses were removed in the 1960s. The streets 180-ft right-of-way, fast-moving traffic, and many uncontrolled intersections present an imposing barrier to pedestrians and bicyclists and detract from the retail uses that remain on portions of the street. The goal of this study was to redesign Adeline to be safer and more inviting. The design envisions Adeline as a balanced, multimodal link in the transportation network and a safe, attractive district for residents and visitors. The proposed redesign reconfigures the corridor, by reclaiming underutilized street and parking space for new housing, commercial uses, and parks. A road diet approach is used, with fewer travel lanes on some sections and redesigned intersections that shorten crossings and provides new pedestrian, bicycle, and transit facilities. A reconfiguration of the BART parking lot creates land for the development of new housing and open space. The proposed implementation plan includes a low-build alternative with initial basic improvements and more dramatic ones as funding becomes available.

Optimizing Urban Bus Transit Network Design Can Lead to Greenhouse Gas Emissions Reduction

The high contribution of greenhouse gas (GHG) emissions by the transportation sector calls for the development of emission reduction efforts. In this paper, we examine how efficient bus transit networks can contribute to these reduction measures. Utilizing continuum approximation methods and a case study in Barcelona, we show that efforts to decrease the costs of a transit system can lead to GHG emission reductions as well. We demonstrate GHG emission comparisons between an optimized bus network design in Barcelona and the existing system. The optimization of the system network design involves minimizing system costs and thereby determining optimal network layout and transit frequency. In this case study, not only does the cost optimal design lead to a 17% reduction in total costs, but even more notably, the optimal design leads to a 50% reduction in GHG emissions. Furthermore, the level of service to the user is not detrimentally affected and in fact, it is slightly improved. We therefore extrapolate and hypothesize that the optimization of transit networks in many cities would result in significant GHG emission reductions. The analysis in this paper specifically focuses on the effects of bus technology with fixed ridership corresponding to the Barcelona case study, but the methods implemented could be easily applied to other transit modes in different cities.

Comparison of Pedestrian Count Expansion Methods: Land Use Groups versus Empirical Clusters

Expansion factors based on the trends in long-term count data are useful tools for estimating daily, weekly, or annual volumes from short-term counts, but it is unclear how to differentiate locations by activity pattern. This paper compares two approaches to developing factor groups for hour-to-week pedestrian count expansion factors. The land use (LU) classification approach assumes that surrounding LUs affect the pedestrian activity at a location, and it is easy to apply to short-term count locations based on identifiable attributes of the site. The empirical clustering (EC) approach uses statistical methods to match locations based on the actual counts, which may produce more accurate volume estimates, but presents a challenge for determining which factor group to apply to a location. We found that both the LU and EC approaches provided better weekly pedestrian volume estimates than the single factor approach of taking the average of all locations. Further, the differences between LU and EC estimation errors were modest, so it may be beneficial to use the intuitive and practical LU approach. LU groupings can also be modified with insights from the EC results, thus improving estimates while maintaining the ease of application. Ideal times for short-term counts are during peak activity periods, as they generally produce estimates with fewer errors than off-peak periods. Weekly volume estimated from longer-duration counts (e.g., 12 h) is generally more accurate than estimates from shorter-duration counts (e.g., 2 h). Practitioners can follow this guidance to improve the quality of weekly pedestrian volume estimates.