Our I-35W Bridge study is now out, the short article from CTS is below: I-35W bridge collapse had complex effect on metropolitan traffic flows, researchers find
The collapse of the Interstate 35W bridge over the Mississippi River in Minneapolis on August 1, 2007, instantly transformed the Twin Cities’ transportation network. Thousands of commuters were forced to revise their daily travel routes literally overnight, resulting in dramatic changes in traffic patterns around the busy downtown area. Recognizing that the tragedy afforded researchers a unique opportunity to study real-world responses to sudden network disruption, University of Minnesota researchers including associate professor and Richard P. Braun/CTS Chair in Transportation Engineering David Levinson and civil engineering assistant professor Henry Liu initiated a suite of research projects designed to capture and analyze data on travel behavior in the immediate aftermath of an unexpected large-scale disruption. Findings from these studies may help the Minnesota Department of Transportation (which sponsored the research) and other transportation agencies prepare for and respond to catastrophic network disruptions. Levinson and graduate student Shanjiang Zhu used a variety of data sources to understand the changes in traffic flows resulting from the collapse, including traveler surveys, GPS tracking of study participants’ travel, and aggregate data on traffic volumes, traffic controls, and transit ridership. Data collection incorporated both the post-collapse period and, insofar as possible, the pre-collapse period.
The researchers found that an unexpected disruption produces an avoidance response among travelers whose routes are affected. Drivers initially avoid the area around the disruption site until the perceived risk of traveling through it is reduced with time. This response produces an oscillation in travel patterns, as traffic levels on links near the disruption drop precipitously and then rebound as travelers adjust to the altered topology of the travel network.
Comparing this phenomenon to the effects of preplanned disruptions such as the closure of bridges or highway segments for reconstruction, the researchers found that the impacts of such expected closures were much smaller. The researchers speculate that the psychological shock of a sudden collapse or other catastrophic event is much more powerful than that produced by a “normal” network disruption, and suggest that rapid implementation of an effective system of detours may be key to minimizing this effect.
Network redundancy–the availability of alternate routes, including other bridges across the Mississippi–was a critical factor in accommodating the excess traffic produced by the bridge collapse. Mn/DOT was able to detour traffic along alternate freeway routes including I-94/Minnesota Highway 280 soon after the collapse, mitigating some of the negative effects of the event. However, Levinson and Zhu note in their research report, if the I-94 bridge had collapsed instead, the asymmetrical nature of the road network in the area would have made the I-35W bridge route much less able to absorb excess traffic. This finding appears to have important implications for analyses of network robustness. The addition of a temporary fourth lane on the I-94 bridge also proved to be very important to maintaining effective traffic flow in the area.
Based on their analysis of travel demand data, Levinson and Zhu conclude that the new I-35W bridge (which opened one year after the collapse with greater capacity and faster average travel speeds than its predecessor) helped reduce travel costs most of the time, but that this benefit was fairly small–on the order of 0.2 to 0.3%. This finding is consistent with a preliminary study by Levinson and graduate student Feng Xie using planning models developed at the University of Minnesota. This agreement between the models and observed travel demand data, the researchers say, suggests that forecasting models incorporating elastic demand (varying in response to travel cost) can provide good first-order estimates of the impacts caused by network disruptions. “Quick-response” travel demand models could also be useful in developing mitigation plans for planned network disruptions.
Traffic Flow and Road User Impacts of the Collapse of the I-35W Bridge over the Mississippi River (Mn/DOT 2010-21) is available from the CTS Web site. More information on University of Minnesota research on the bridge collapse is also available online.
Joe Cortwright just released a very nice takedown of the TTI Urban Mobility Report (an essential, yet incomplete source of transportation data) Driven Apart. It is well worth reading. From the press release:
A new report from CEOs for Cities relased today unveils the real reason Americans spend so much time in traffic and offers a dramatic critique of the 25 year old industry standard created by the Texas Transportation Institute’s Urban Mobility Report (UMR) – often used to justify billions of dollars in expenditures to build new roads and highways. The surprising analysis by Joseph Cortright, senior policy advisor for CEOs for Cities, says the solution to this problem has much more to do with how we build our cities than how we build our roads.
The report, titled Driven Apart: How sprawl is lengthening our commutes and why misleading mobility measures are making things worse and supported by the Rockefeller Foundation takes a new look at what’s really causing traffic congestion in America and says that compact cities are the real answer to reduscing traffic delays. These conclusions are far different than those of the UMR, which has long been used to measure traffic congestion.
Congratulations to soon-to-be Dr. Shanjiang Zhu (September 2010- PhD Civil Engineering) for completing and successfully defending his Ph.D Dissertation –
The Roads Taken: Theory and Evidence on Route Choice in the wake of the I-35W Mississippi River Bridge Collapse and Reconstruction He will be joining the University of Maryland, College Park as a post-doc.
Route choice analysis investigates the path travelers follow to implement their travel plan. It is the most frequent, and thus arguably the most important decision travelers make on a daily basis. Long established efforts have been dedicated to a normative model of the route choice decision, while investigations of route choice from a descriptive perspective have been limited. Wardrop’s first principle, or the shortest path assumption, is still widely used in route choice models. Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. Moreover, factors beyond travel time, such as preferences for travel time reliability, inertia in changing routes, and travel experience that could also have significant impacts on route choice, have not been fully explored and incorporated in route choice modeling. The phenomenon that people use more than one route between the same origin and destination during a period of time is not addressed by conventional route choice models either.
To bridge these gaps, this dissertation systematically evaluates people’s route choice behavior using data collected in the Minneapolis – St. Paul metropolitan area after the I-35W Bridge Collapse. Both aggregate traffic data and individual survey data show gaps between models based on shortest travel time assumption and traffic conditions observed in the field. This study then employs the individual GPS trajectory and GIS maps to systematically evaluate the characteristics of routes people actually use. Merits of route choice set generation algorithms widely used in practice are assessed. The phenomenon of route diversity is clearly revealed through analysis of field data. A route portfolio model is proposed to explain the rationale of choosing a portfolio of routes under uncertainty about network conditions. It is posited that a rule-based model,
comprehensively considering travelers’ characteristics, additional network metrics, and previous travel experience will better replicate observed route choices than the tradi- tional assumption of simply minimizing travel time or travel cost. Findings from this dissertation could also inform other parts of travel demand modeling.
Sad, but I guess ironic, news from Daily Mail:
Segway owner Jimi Heselden dies after riding one of his machines off cliff: “”
Haselden bought the company in early 2010 according to wikipedia.
For a nice, older history of the Segway, see Reinventing the Wheel by Scott Kemper
I will be in DC at the
AASHTO Center for Excellence in Project Finance—Congressional Finance Forum on September 30, talking about Value Capture and the National Infrastructure Bank
I don’t think Google are the smartest guys in the room, after all they funded Schweeb as the best idea to “Drive innovation in public transportation”. Unless their sole objective is attention-seeking, this is a miss.
PG asks me to elaborate:
There are any number of critiques, quote James from the GigaOm post on the subject:
“This may be one of the silliest, most impractical ideas I’ve ever heard get funding. It can only have two stops, one at each end. It only goes as fast the slowest rider. And there’s no way to return the cars without riders. And where do you put your stuff? How about this: build a bike path. All the advantages, probably 1/100th the cost and you get all the same political challenges!”
Also passing seems prohibited (though switches might enable that).
This does not seem to help public transport at all, and is just the worst of PRT meets the worst of bicycle advocacy. It is a toy, and an amusement park ride, but not a serious attempt at solving a serious problem. To work, the network needs to be everywhere people want to go. We have a network that solves that problem, it is called the road network. Deploying a new network (with all the network economics issues of it isn’t valuable until it is ubiquitous) will require enormous subsidy. If Google wants to put their money in great, I don’t own their stock.