No Outlet: A Review of Twin Cities Premium Outlets

Last year, to much fanfareTwin Cities Premium Outlets were opened. While the center has recently encountered some controversy about the atrocious treatment of black shoppers, this post is about the design (recognizing its isolating design and nature as private property may have some relationship about how shop managers and police think about the presence of others).

Aerial of Twin Cities Premiums Outlets from Google Maps.
Aerial of Twin Cities Premiums Outlets from Google Maps.

Located in Eagan, on the Red Line (Cedar Grove Station), it is just a short transit hop from the Mall of America, and a shorter drive, at the intersection of Cedar Avenue (Highway 77) and Sibley Memorial Highway (Highway 13). With a “race track” design, the expectation is users will flow through the center in a circular pattern and return where they started, shopping both sides of the “street” simultaneously. As the first new mall in 13 years, it represents the last gasp of traditional bricks and mortar retail before the full onslaught of online shopping decimates what is left.

Twin Cities Premium Outlets:  Source: http://www.premiumoutlets.com/pdfs/twincities.pdf
Twin Cities Premium Outlets: Source:

Some photos are attached. I suppose the traffic is suppressed since this was a Sunday in February, though the stores were all open, and the temperature was above average. The Google maps shows a fairly full surface parking lot (though the top deck of the “garage” (you know, they meant “ramp”, even though the sign says “Garage” and the map says “Deck”) was largely empty. The site apparently has 3000 parking spaces (doesn’t look like it).

Slippery when wet. Imagine.
Slippery when wet. Imagine.
Twin Cities Premium Outlets, a plaza in the snow.
Twin Cities Premium Outlets, a plaza in the snow.
A color coded guide. It would be more effective if they named the streets.
A color coded guide. It would be more effective if they named the streets.
A street through the center enters a covered but not climate controlled section. Feel the wind.
A street through the center enters a covered but not climate controlled section. Feel the wind.
An open plaza faces the parking ramp.
An open plaza faces the parking ramp.
Cedar Grove Parking Garage is a few short steps (the Transit Center is farther away)
Cedar Grove Parking Garage is a few short steps (the Transit Center is farther away)
The food court has a wide variety of specialty vendors
The food court has a wide variety of specialty vendors

I do not understand the appeal of outdoor shopping in February in Minnesota. While there is a covered section, it is not enclosed, and thus remains cold. This design has many of the worst features of a shopping mall:

  • Parking (and transit)) far from the shops, the transit center is about 1000 feet (almost 1/4 mile) from the first store.
  • A finite space without any opportunity for discovery or serendipity, I really cannot accidentally leave the site. There are anchors at the end of the internal streets, foreclosing opportunities to extend the internal grid onto the surface parking. Is it really too much to consider the possibility you might want to expand this center without tearing down functional buildings and thus would have built an extensible grid.
  • Mostly ubiquitous chain stores (or the outlet versions thereof) with almost nothing local or unique.
  • Parking acting as a barrier to integration of the mall shops with the rest of the community. It could not have been difficult to have the parking garage back onto the highway so the stores could integrate with the neighborhood. Instead it is a fortress. I realize this might have cost some visibility from the highway from the shops themselves, but really, that’s what signs are for. Existing surface streets should have established the alignment of the pedestrian streets in the mall

without the best:

  • Climate control. This is not California, people. Has no one learned anything from the AMC Rosedale debacle.

It does of course prohibit cars on shopping streets, which is something we can only dream of in actual cities, and is an improvement over the fake Main Streets of places like the Shoppes Arbor Lakes in Maple Grove (which isn’t even Main Street).

There are plans to reconfigure the Cedar Grove Transit Station on the Red Line so that it will an on-line station, saving time for users (though potentially making it even farther from the Mall) [Forum Discussion]. It apparently serves 200 employees and shoppers at the center per day. Notably there has not been much crime at the center, with 630 calls for service since its opening (reported Jan 20), or about 3 calls per day .

Cross-posted at streets.mn

Modeling the commute mode share of transit using continuous accessibility to jobs

Recently published

Owen, Andrew and  David M. Levinson (2014) Modeling the commute mode share of transit using continuous accessibility to jobs Transportation Research Part A: Policy and Practice Volume 74, April 2015, Pages 110–122

Fig. 5.  Transit accessibility coefficient of variation over 7–9 AM period.
Fig. 5.
Transit accessibility coefficient of variation over 7–9 AM period.

Highlights

  • Accessibility to jobs by transit is calculated for departures at each minute.•
  • Detailed spatial resolution more accurately reflects walking trip components.
  • Higher transit mode share is associated with higher average transit accessibility.
  • Higher transit mode share is associated with lower variation in transit accessibility.

Abstract
This paper presents the results of an accessibility-based model of aggregate commute mode share, focusing on the share of transit relative to auto. It demonstrates the use of continuous accessibility – calculated continuously in time, rather than at a single of a few departure times – for the evaluation of transit systems. These accessibility calculations are accomplished using only publicly-available data sources. A binomial logic model is estimated which predicts the likelihood that a commuter will choose transit rather than auto for a commute trip based on aggregate characteristics of the surrounding area. Variables in this model include demographic factors as well as detailed accessibility calculations for both transit and auto. The mode achieves a ρ2 value of 0.597, and analysis of the results suggests that continuous accessibility of transit systems may be a valuable tool for use in modeling and forecasting.

Elsevier is offering a FREE DOWNLOAD Until April 26, 2015.

Elements of Access: Risk Compensation

Risk Compensation

BikeHelmetby Wes Marshall

 

 THE LINE BETWEEN SAFE AND UNSAFE IS NOT ALWAYS CLEAR

Kids should always wear a helmet when they are bicycling. It is hard to question that statement, and in many places, it is the law (even in a few places without mandatory motorcycle helmet laws). A funny thing happens when some people wear a helmet; they ride faster and more recklessly. Why? The helmet makes them feel safer, and the risk of getting hurt subconsciously fades away. Thus, they behave differently. Changing one’s behavior due to a change in perceived safety risk is called risk compensation. But who is really safer: a reckless rider with a helmet or a careful rider without one?

Risk compensation is ubiquitous. You see it in sports like football and hockey where additional protective equipment facilitates bigger hits. You also see it in race car driving, which is a notoriously dangerous sport. So much so that the sport has gone to great lengths to improve safety with better helmets, seat belts, roll cages, fire retardant uniforms, and softwall technology. All of these efforts have reduced the chance of a fatality when a crash occurs. Yet, the research shows that as the casualty rate drops, the number of crashes increases. Drivers can push the limits of their race car to an even greater extent because they feel safer knowing that the risk of death or severe injury is relatively low.

In terms of road safety for the rest of us, the outcomes are not all that different. For most of the last fifty years, the conventional approach to improving safety focused on: vehicle improvements such as seat belts, air bags, and crumple zones; and road designs that were wide and straighter with increased sight distances and clear zones. This safety paradigm emanated from the U.S. Congressional road safety hearings of the 1960s. The new mindset focused on engineering measures – such as better vehicle and street designs – that were far easier to influence that the behavior of millions of drivers. While some of these efforts did in fact improve road safety, this was not always the case.

Many of the so-called safer road designs, for instance, did not fulfill their promise. If behaviors remained constant, the underlying theory would have been successful. Unfortunately, a driver feeling safe on the road can profoundly impact behavior. Whether the driver is more likely to speed, divert their attention from the road by talking on the phone or listening to music, or even fall asleep at the wheel, the research suggests that many such road safety improvements actually decreased overall safety.

The problem is risk compensation. It’s the same reason you now rely on your vehicle’s back-up camera instead looking over your shoulder and using the camera to enhance the information you used to gather manually. For transportation engineers and planners, the line between safe and unsafe is not always very clear. To have a chance for clarity, we need to better account for risk compensation – and the impact of the resulting behavior changes – in our designs.

 

References:

Adams, J. (1995). Risk. London England Bristol, PA: UCL Press.

Dumbaugh, E. (2005). “Safe Streets, Livable Streets.” Journal of the American Planning Association 71 (3):16p.

Keating, P. (2001). “The Danger of Safer Equipment.” ESPN The Magazine,

Nader, R. (1965). Unsafe at any speed; the designed-in dangers of the American automobile. New York,: Grossman.

Noland, R. (2000). “Traffic Fatalities and Injuries: Are Reductions the Result of ‘Improvements’ in Highway Design Standards?” Transportation Research Board 80th Annual Meeting, Washington, D.C.

Potter, J. (2011). Estimating the Offsetting Effects of Driver Behavior in Response to Safety Regulation: The Case of Formula One Racing. Journal of Quantitative Analysis in Sports. 7(3), 1-20.

Vanderbilt, T. (2008). Traffic: why we drive the way we do (and what it says about us). New York: Alfred A. Knopf.

Weingroff, R. (2003). Highway History: President Dwight D. Eisenhower and the Federal Role in Highway Safety. Washington, D.C.: Federal Highway Administration.

 

 

 

Multiday GPS Travel Behavior Data for Travel Analysis

I am pleased to report that a new FHWA-supported study: “Multiday GPS Travel Behavior Data for Travel Analysis” (PDF) is out. Our contribution is Chapter 4.0 An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths. The report was coordinated by RSG, and the overview is below:

Introduction to Report

By Mark Bradley (RSG)

The use of GPS devices to collect trip-specific data as part of household travel surveys has increased steadily in recent years, and will likely become the main mode of travel survey data collection in the future as smartphone-based platforms for collecting travel data come into use. Compared to diary-based methods, the advantages of GPS data capture include the following:

  • The time and location of each trip end can be captured with more precision.
  • There is less potential for respondents to omit entire trips or activities from the survey.
  • The data can be used to trace the route traveled for any particular trip.
  • It becomes more cost-effective to capture multiple days of travel for each respondent.

These unique aspects of GPS data enable new types of behavioral analysis relative to those conducted with more traditional travel survey data. In particular, multiday data capture, in combination with more precise and complete travel data on each day, allows researchers to investigate day-to-day variability in travel behavior at the individual and household level. Such analyses can provide more insight into peoples’ travel patterns at a broader level, and guide future efforts in modeling and predicting travel behavior and designing transportation policies.

Large-sample, multiday GPS datasets from household travel surveys are still relatively limited in quantity, as is the expertise required to process point-by-point GPS trace data into trip-level data that can be used by most analysts. To address these issues, the US Department of Transportation and the National Renewable Energy Laboratory (NREL) have created the Transportation Secure Data Center (TSDC).[1] The TSDC allows researchers to access preprocessed data from almost one dozen different multiday GPS travel datasets from across the United States; it also allows researchers to analyze these data in a secure environment that ensures the protection of data privacy.

The two main objectives of this project are: 1) to provide new examples of the type of valuable research that can be done using multiday GPS travel survey data; and 2) to demonstrate that such research can be conducted in the TSDC research environment. Each of the following four chapters describes a research project that was funded and carried out as part of this project. The four research topics were originally specified by RSG, with input from FHWA, and then further refined by the authors during the course of their research.

In “The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices,” Jennifer Dill, PhD, and Joseph Broach, PhD (candidate), of Portland State University address the important research topic of measuring the effect of auto network reliability on drivers’ choices. Using data from a 7-day vehicle-based GPS survey in the Atlanta region and a longer-duration vehicle-based GPS survey in the Seattle region, the authors identified several cases where respondents made multiple car trips between the same origin-destination (O-D) pairs during the survey period, and measured the actual experienced day-to-day travel time variation for those O-D pairs. The authors report several interesting analyses showing that such variability is related to trip and traveler characteristics, including trip purpose, distance, and household income.

In “Multiday Variation in Time Use and Destination Choice in the Bay Area Using the California Household Travel Survey,” Kate Deutsch-Burgner, PhD, of Data Perspectives Consulting, investigates day-to-day variation in the number, types, and level of dispersion (distance) of destinations visited during the specific days of a 3-day person-based GPS survey in the California Bay Area. Using the technique of latent class cluster analysis (LCCA), she is able to distinguish clearly different patterns of variability in terms of number of trips and type and dispersion of destinations. This analysis method shows promise for addressing the complexity of multiday travel data, and may become even more useful as future person-based (e.g., smartphone-based) GPS datasets include a greater number of travel days and a potentially wider variety of different patterns across the days.

In “Capturing Personal Modality Styles Using Multiday GPS Data-Findings from the San Francisco Bay Area,” Yanzhi “Ann” Xu, PhD, and Randall Guensler, PhD, of Trans/AQ, Inc., analyze the same multiday GPS dataset from the Bay Area that was used for the analysis described in the preceding chapter. In this analysis, however, the focus is on day-to-day variation in mode choice-research for which person-based, rather than vehicle-based, GPS data collection is clearly necessary. The authors were able to identify distinct groups of individuals in terms of whether they always used the same mode or used a variety of modes, and in terms of whether auto or alternative modes were used more often. They were also successful in relating these groupings to different person and household characteristics. The propensity to use multiple modes would benefit standard travel modeling methods, as someone who usually uses auto but also uses transit one or two days per week may be more likely to increase his or her transit use in response to service changes, as compared to someone who never uses transit at all.

Finally, in “An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths,” Wenyun Tang, PhD (candidate), and David Levinson, PhD, of the University of Minnesota, use multiday person-based GPS data from the Minneapolis region Travel Behavior Inventory (TBI) to determine how often drivers use the shortest path for their home-to-work trip, and look at patterns in the deviation in travel time between the shortest path and the actual path. In terms of day-to-day variability, the authors were not able to identify many cases where respondents made the same direct home-to-work auto trip on multiple days. This outcome indicates that analyses that measure travel behavior across multiple days (rather than simply treating them as separate single days) will tend to require large sample sizes, particularly when the analysis focuses on a specific type of behavior (e.g., direct home-to-work auto trips).

The research presented in the following four chapters provides interesting findings in their own right, and insights into the types of research designs and methods that will be valuable in analyzing multiday GPS data as it becomes more ubiquitous and accessible in the future. The authors generally recognize that their methods could benefit from larger sample sizes, in terms of the number of respondents, and particularly in terms of the number of days per respondent. (For example, use of 7-day GPS data capture periods would allow analysis of patterns, including both weekdays and weekends.) The authors also note the critical importance of how the GPS trace data are processed into trip-level data, and the need for evolving practices and standards in GPS data processing. Finally, the authors describe the value of the TSDC in making these unique datasets available while providing a secure and productive research environment.

Role Model

At streets.mn we focus on Minneapolis and Minnesota. We praise what is praise-worthy and condemn what is condemnable. We feel we do it for our own community. And of course we do.

But what we do in Minneapolis is important not just for residents of Minneapolis, rather it matters for residents of the world. You my fellow Minneapolitans (and St. Paulites perhaps), and those in the surrounding environs are residents of communities that should aim to serve as a role model for the rest of the US, and the world, about how cities should function. We are perfecting the Twin Cities not simply for our own benefit, but for that of humanity.

If we fail, people will look to Portland, or Europe, instead. Surely we can do better.

Cross-posted at streets.mn

Elements of Access: Resilience

800px-Resilience-figure001 800px-Resilience-figure002 800px-Resilience-figure003

Graph theory defines resilience such that: if graph G has property P, what is the minimum number of edges (E) (links) that need to be removed so that G no longer has P? For example, consider the graph in the Figure on the Left above and its resilience with respect to connectivity. Removing any one edge leaves a connected graph. It is necessary to remove two edges to produce a graph that is not connected (Middle Figure). Thus, we could say that this graph has a resilience of 2 with respect to connectivity. Note that this does not mean that removing any two edges will destroy connectivity in this graph. The Figure on the Right demonstrates the possibility of removing two edges while leaving the graph connected.

Under this definition, a given graph will have different values of resilience with respect to different properties. As a result the definition is concrete but flexible, and can be usefully applied to real-world networks where properties are of variable importance from different perspectives.

The example above highlights the difference between random edge removal and targeted edge removal. If edges are removed randomly, a property might survive the removal of many edges. Targeted edge removal implies that the graph is analyzed and edges are chosen to maximize effect. The effect on the network of either type of edge removal depends in part on degree distribution.

Graphs following a power-law distribution (scale-free) tend to be highly resilient to random edge removal because there is a very good chance that the edges removed will connect only low-degree vertices – and therefore the overall graph structure will be affected only slightly. Graphs are much more vulnerable, however, to targeted removal of edges attached to high-degree nodes, especially to the removal of those nodes themselves. In scale-free graphs, these high-degree vertices are critical in connecting subgraphs.

A graph with a low resilience with respect to a property can lose that property as a result of only a few edge removals. We can say that the graph is vulnerable with respect to that property.

But this is only half of a complete consideration of vulnerability. The other half has to do with the effect on the network’s performance if the property in question has been lost.

In graph theory, resilience is a binary concept: an edge either exists or it does not; a graph either has a property or it does not. In real-world transportation networks, links have additional properties such as capacity, utilization, demand, and cost.

References:

  •  This post is adapted from the Wikibook Transportation Geography and Network Science  originally written by the research team.
  •  Sudakov, B. and V. H. Vu (2008). Local resilience of graphs. Random Structures & Algo- rithms 33(4), 409–433.
  •  Newman, M. E. (2003). The structure and function of complex networks. SIAM review 45 (2), 167–256.

MARC – Multi-Agent Route Choice Game

Our  Multi-Agent Route Choice (MARC) game is designed to engage students in the process of making route choice, so that they can visualize how traffic gradually reaches a user equilibrium (UE). In addition, the Braess’ paradox phenomenon, a concept not generally taught by undergraduate transportation courses, is embedded into the game so that students can explore this phenomenon through game-play.

The software, developed by Xuan Di, is now available for download.

The paper evaluating the application is in press at TRR. A pre-print is here:

 

Elements of Access: What are we talking about, when we talk about trips?

by Kay AxhausenTrips2 Trips1

 

 

 

The image shows a daily schedule with 2 tours including 1 subtour, 8 trips and 18 stages.

No surprise, but professional and everyday language overlap in their vocabulary, while not identical in their meanings. In many fields this is not a big problem, as the technical and professional discourse do not overlap, as laymen rarely read or hear the technical discourse, but for the students at the beginning of their training. In transportation it is a problem, as the professionals have to address the public as voters, decision makers or respondents in surveys. They talk with each other continuously.

As expected, transport planners and engineers have developed a detailed vocabulary to talk about movement, unfortunately even differing ones for the different modes of transportation. Roget’s Thesaurus give as synonyms trip, journey, excursion, cruise, expedition, foray, jaunt, outing, run, swing, tour, travel, trek, errand, hop, junket, peregrination, ramble. They clearly have connotations, which make one more suitable for certain occasions, but they can be used interchangeably for a movement from A to B and back.

Observing and thinking about movements for a moment it becomes clear, that a ‘trip’ will have smaller building blocks and might belong to something bigger, say a vacation. The professionals have given names and a structure to these, so that they can measure and talk about them clearly. The layman might want to appreciate the differences when he or she listens to a policy proposal arguing with them.

The key terms are stage, trip, tour, daily schedule with their variants in different countries and industries. A stage is the smallest unit, the movement from A to B with one mode ore one vehicle of that mode: walking from home to the bus stop or flying from the first airport to the hub airport are stages. The airline industry talks about legs and means the same thing, as do American planners when they talk about ‘unlinked trips’. In logistics the stage with the longest distance is generally called the ‘main haul’.

A sequence of stages from one activity to the next is a trip, which now requires a definition of activity. Following the example of time use studies and sociology, the activity is defined as a meaning interaction with another person or task. In transportation a trip is always one way.

A sequence of trips from A via various other locations back to A form a tour. Journey is used to specify tours starting and ending at home. One runs into problems, if one wants to talk about tours within tours, for example going to lunch and coming back to the workplace. Some parts of the literature talk about sub-tours then. Equally, other parts of the discussion need a word to talk about the movement from home to the main stop of the tour. You will find the word ‘commute’ to describe just this, even if it includes activities, such as dropping out the children at school, a quick coffee at Starbucks and time at the gym before arriving at work.

The daily schedule are all the tours undertaken between getting up and going to bed again.

It should be clear now, that discussion about mode choice should always refer to the element talked about. Walking (stages) will always be part a trip to reach the vehicle(s). Walking will therefore always have the highest mode share among the stages, but not of the miles travelled. At the higher levels the planners have to decide which mode they allocate to the trip or tour. Normally they choose the mode of the stage with the longest distance. In this process the other stages are forgotten and often their mileage allocated to the maid mode. The chances for confusion are endless, unless this is made clear.

Remember: Tours are sequences of trips, which are sequences of stages.