On-road emissions, a dominant source of urban air pollution, damage human health. Emissions increase air pollution intake (and damage health) of travelers (internal costs), and of non-travelers (external costs). This research constructs a framework modeling the microscopic production of emission cost from the vehicle and link level and applies it to a metropolitan road network. It uses project-level Motor Vehicle Emission Simulator (MOVES) simulations to model link-specific on-road emissions, and then employs the RLINE dispersion model to estimate on- and off-road concentrations of pollutants from vehicles. The internal and external emission costs are measured accordingly by counting the health damage costs of travelers and gen- eral population because of exposure. The framework is applied to the Minneapolis-St. Paul (Twin Cities) Metropolitan Area as a proof-of-concept. The estimates show that highways have higher emission concentrations because of higher traffic flow, but that the internal and external emission costs per vehicle kilometer traveled are lower. The emission costs that commuters impose on others greatly exceeds that which they bear. This modeling process is replicable for planners and practitioners assessing emission costs in other regions.
Welcome to the latest issue of The Transportist, especially to our new readers. As always you can follow along at the transportist.org or on Twitter.
Plants vs. Animals
I posit, as a first order approximation, humans are going to bifurcate as a species into plants and animals. Plants work from home and have everything delivered. Animals have out of home jobs and travel.
A quick Twitter poll shows nearly 2/3 of my followers admit to being plants, disturbingly high.
30-Minute City: Designing for Access is now Open Access
This book describes how to implement The 30-Minute City. The first part of the book explains accessibility. We next consider access through history (chapter 2). Access is the driving force behind how cities were built. Its use today is described when looking at access and the Greater Sydney Commission’s plan for Sydney. We then examine short-run fixes: things that can be done instantaneously, or nearly so, at low budget to restore access for people, which include retiming traffic signals (chapter 3) and deploying bike sharing (chapter 5) supported by protected bike lane networks (chapter 4), as well public transport timetables (chapter 6). We explore medium-run fixes that include implementing rapid bus networks (chapter 7) and configuring how people get to train stations by foot and on bus (chapter 8). We turn to longer-run fixes. These are as much policy changes as large investments, and include job/worker balance (chapter 10) and network restructuring (chapter 9) as well as urban restoration (chapter 11), suburban retrofit (chapter 12), and greenfield development (chapter 13). We conclude with thoughts about the ‘pointlessness’ of cities and how to restructure practice (chapter 14). The appendices provide detail on access measurement (Appendix A), the idea of accessibility loss (B), valuation (C), the rationale for the 30-minute threshold (D), and reliability (E). It concludes with what should we research (F).
Ji, Ang and Levinson, D. (2020) An energy loss-based vehicular injury severity model. Accident Analysis and Prevention. 146 October 2020, 105730. [doi]
How crashes translate into physical injuries remains controversial. Previous studies recommended a predictor, Delta-V, to describe the crash consequences in terms of mass and impact speed of vehicles in crashes. This study adopts a new factor, energy loss-based vehicular injury severity (ELVIS), to explain the effects of the energy absorption of two vehicles in a collision. This calibrated variable, which is fitted with regression-based and machine learning models, is compared with the widely-used Delta-V predictor. A multivariate ordered logistic regression with multiple classes is then estimated. The results align with the observation that heavy vehicles are more likely to have inherent protection and rigid structures, especially in the side direction, and so suffer less impact.
This study focuses on path flow for road network, as the sum of individual route choices from individual travelers, associated with specific path type for each cost factor of auto travel that finds the optimal route with the minimum cumulative cost from the perspective of the corresponding cost analyst interest. The considered cost factors include time, safety, emission, and monetary costs, as well as their composite, internal and full cost of travel. We further explore the extent to which each cost factor explains the observed link traffic flows given an estimated home-to-work demand pattern. The results of the Minneapolis – St. Paul metropolitan area indicate that flows from multiple path types, associated with internal cost components, additionally to the factor of distance, provides the best fit.
We use the term “crash” not “accident” as “accident” implies no one was at fault and lack of intention and crash is more neutral. Crashes have causes.
The safety community likes to say “Safety is a shared responsibility”, and the responsibility lies with drivers, road engineers, vehicle designers, public policy, and others. [Though I think it is often about shirking responsibility and putting it back on the victim rather than taking it themselves].
Every crash has individual causes, but there are trends.
The Lockdowns associated with COVID-19 are a factor. A study from Ohio State Univeresity found that COVID, which decreased the amount of automobile (and all) travel saw an increase in Speeding.
Speeding is a known cause of crashes. Higher speeds have two major effects:
1. Speeding reduces the available time for drivers to react to events, increasing the likelihood of a collision.
2. Higher speed increases the severity of impact, increasing the likelihood of fatality.
Solutions include better driver training and testing, more rigorous enforcement, keeping intoxicated drivers off the road, better engineering of roads, lower speed limits (which are both enforced, and designed into the road).
Roy, Avipsa, Daniel Fuller, Kevin Stanley, and Trisalyn Nelson. 2020. “Classifying Transportation Mode from Global Positioning Systems and Accelerometer Data: A Machine Learning Approach.” Transport Findings, September. https://doi.org/10.32866/001c.14520.
Young, Mischa, and Steven Farber. 2020. “Using Wait-Time Thresholds to Improve Mobility: The Case of UberWAV Services in Toronto.” Transport Findings, August. https://doi.org/10.32866/001c.14547.
Du, Jianhe, and Hesham Rakha. 2020. “Preliminary Investigation of COVID-19 Impact on Transportation System Delay, Energy Consumption and Emission Levels.” Transport Findings, July. https://doi.org/10.32866/001c.14103.
Branion-Calles, Michael, Kate Hosford, Meghan Winters, Lise Gauvin, and Daniel Fuller. 2020. “The Impact of Implementing Public Bicycle Share Programs on Bicycle Crashes.” Transport Findings, September. https://doi.org/10.32866/001c.16724.
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