This study defines and detects competitive and complementary links in a complex network and constructs theories illustrating how the variation of traffic flow is interconnected with network structure. To test the hypotheses, we extract a grid-like sub-network containing 140 traffic links from the Minneapolis – St. Paul highway system. We reveal a real-world traffic network comprises both competitive and complementary links, and there is a negative network dependency between a competitive link pair and a positive network dependency between a complementary link pair. We validate a robust linear relationship between standard deviation of flow in a link and its number of competitive links, its link correlation with competitive links, and its network dependency with both competitive and complementary links. The results indicate the number of competitive links in a traffic network is negatively correlated with the variation of traffic flow in congested regimes as drivers are able to take alternative paths. The results also signify that the more the traffic flow of a link is correlated to the traffic flow of its competitive links, the more the flow variation is in the link. Considering the network dependency, however, it is corroborated that the more the network dependency between a link and its competitive links, the more the flow variation in the link. This is also true for complementary links.
Using travel diary data from 2000-2001 and 2010-12 this research examines fundamental traffic relationships at the metropolitan level. The results of this paper can help to explain the causes of some traffic phenomena. First, trip numbers on the network in the Minneapolis – St. Paul, Minnesota (Twin Cities) region show a bimodal diurnal pattern, with more trips in the afternoon. This relationship holds for full-time work and for non-work trips, but not part-time work trips. Second, network average speed by time of day can be explained by trip length and cumulative number of vehicles on the road. A clockwise hysteresis loop is found in the Metropolitan Fundamental Diagram in the morning period and a reverse process happens in the afternoon.
Key words: Macroscopic Fundamental Diagram, Network Fundamental Diagram, Traffic Data, Travel Surveys
Up and Out: The Future of Travel Demand and Where We Live
Adapting the Built Environment
Reduce, Reuse, Bicycle
Accelerating the End of Traffic via Pricing
Post-script 1: What Happened to Traffic?
Post-script 2: Now extinct: the Traditional Transport Engineer
In this book we propose the welcome notion that traffic—as most people have come to know it—is ending and why. We depict a transport context in most communities where new opportunities are created by the collision of slow, medium, and fast moving technologies. We then unfold a framework to think more broadly about concepts of transport and accessibility. In this framework, transport systems are being augmented with a range of information technologies; it invokes fresh flows of goods and information. We discuss large scale trends that are revolutionizing the transport landscape: electrification, automation, the sharing economy, and big data. Based on all of this, the final chapters offer strategies to shape the future of infrastructure needs and priorities.
We aim for a quick read—and to encourage you and other readers to think outside your immediate realm. By the end of this book (today, if you so choose) you will appreciate the changing times in which you live. You will hopefully appreciate what is new about transport discussions and how definitions of accessibility are being reframed. You will be provided with new ways of thinking about the planning of transport infrastructure that coincide with this changing landscape. Even if transport is not your bailiwick, we like to think there is something interesting for you here. We aim to share new perspectives and reframe debates about the future of transport in cities.
We have long known in the transportation planning community that the use of trip generation for local area review, and ITE’s procedure for estimating trip generation is broken in any number of ways. Shoup’s Truth in Transportation Planning is a classic critique of the problems.
While we could (and perhaps should) throw the whole kit and caboodle into recycling, in practice trip generation methods will be with us decades from now (even as traditional work, shopping and driving disappear). So there is a small academic movement to make the methods better. The most recent issue of JTLU 8(1) has a special section on Trip Generation, including several papers about how to adjust and improve ITE’s Trip Generation methods based on better data.
Part of the problem is that ITE is functionally a for-profit organization, and makes bank on selling the Trip Generation Manual and associated software (recognizing the fact that use of ITE Trip Generation rates is ensconced in law and regulation).
What has long been needed is an open source database of trip generation studies so that better fits to actual site conditions can be used in analysis. I recall in my youth some engineers in Montgomery County, Maryland trying to set something up, but this was well before the world wide web made that easy.
Fortunately that day is upon us. Mike Spack and company have set up TripGeneration.org, which is populated with open access trip generation studies (licensed under a Creative Commons license), and for which they hope to grow the data set. This is new, and I assume as it grows the data will get better and better, as will the methods for inputting and extracting data. Kudos to Mike, Nate, and others at Spack Consulting for getting this going. I look forward to seeing where this goes, as Big Data and new sensors make data collection increasingly ubiquitous.
Speed humps are proposed in Sunnyside. I note there are alternatives.
David Levinson, a professor in the Department of Civil Engineering at the University of Minnesota, said speed humps are not the most efficient way to slow down traffic, as drivers get used to them and tend to speed after passing one, or just avoid them by using alternate routes.
Levinson said speed humps are only one part of a measure called traffic calming, which is a change in the infrastructure and environment of the roads to slow down traffic and make the streets safer for bikers and pedestrians. He said there are other more effective forms of traffic calming.
“Other solutions would be putting trees on the side of the road, changing the pavement material, putting on-street parking,” Levinson said. “A very good one is to narrow the streets intersections. If the intersection is narrow the sidewalk is extended and there is a change in the environment, so cars need to go slower because they are driving through a narrower region.”
He said speed humps also create difficulties for fire trucks, garbage removal vehicles, and snowplows. He said one solution to lower speeds and fewer accidents in residential areas would be to follow the woonerf movement in use in the Netherlands, a system of “living streets” where pedestrians and cyclists have legal priority over motorists.
Of course, almost every time a report on traffic comes out, it makes the headlines. But how do we make heads or tails out of them when they seem to contradict one another?
“They are not necessarily contradictory,” said David Levinson, a professor of civil engineering at the University of Minnesota. “They all have different data, but they are measuring roughly the same thing.”
The differences can start with how the report makers define congestion. MnDOT defines congestion as traffic flowing at speeds less than or equal to 45 miles per hour. TomTom defines it as increased travel time when compared to free-flow conditions.
The data used to compile reports comes from different sources, too. MnDOT uses loop detectors embedded in the pavement while INRIX uses GPS data. Another difference is that the INRIX report looked at traffic volume and delays while MnDOT’s congestion report details the location and percentage of freeways experiencing daily congestion.
New business or housing developments can alter traffic flow in areas, making roads that were adequate suddenly become packed, creating the perception that congestion is getting worse.
“You might see more this year because the economy has picked up, but generally it has been flat and has been for a while,” Levinson said. “If it gets too bad, people will change their behavior. … There are limits on how bad the congestion can get.”
Depending on the methodology, rankings put the Twin Cities between the 13th- and 16th-largest U.S. metro area, said David Levinson, a civil engineering professor at the University of Minnesota. “The fact that we’re ranked 16th in congestion seems about right,” he said.
Levinson said demographic trends are helping to mitigate road congestion.
“Travel times are declining in the U.S.,” he said. “People are aging. Old people don’t travel as much, and young people don’t travel as much as what young people used to. Fewer kids own cars. The big picture is that that the total amount of travel peaked in the U.S. a few years ago and it’s been declining ever since. We have some ups and downs during any given year depending on the price of the gas and whether the economy is doing a little bit better or not. Certainly [congestion is] more than in 2009 during the depths of the recession.”
Levinson and others are quick to point out that Twin Cities drivers could be dealing with much worse.
In Los Angeles, home to the nation’s most-congested roads, drivers spent 64 hours sitting in traffic, an increase of five hours from the previous year, according to the INRIX study. In Honolulu, the nation’s second-worst city for traffic, drivers sat behind the wheel 60 extra hours last year, while in No. 3 San Francisco it was 56 hours.
And, Levinson points out, there’s more good news for the Twin Cities. The average speed of travel in the metro area is the fifth-highest in the country.
“You sit in traffic at a particular bottleneck, but then when you’re moving on the freeway, you’re driving at 55 mph,’’ he said. “And when you’re driving on arterials, you’re driving at 45 mph, and that’s better than most metro cities.”
One of the interesting scientific questions that emerges from the tragedy of the I-35W Bridge Collapse is how traffic responds. There are several time horizons for looking at this.
Most immediately are those who are on the link leading up to the bridge. MnDOT’s traffic cameras show the cars turning around on the freeway within seconds of the bridge collapsing, before the dust clears literally. “Video footage of the collapse from Mn/DOT traffic camera 628. 6:05 p.m., Aug. 1, shows an edited two-minute clip from a traffic camera at the south end of the bridge. Initially, the camera is pointed to the south away from the bridge. When traffic comes to a stop, the camera pans to the north where the bridge has just collapsed. (wv file)”. This is a rational response on the part of drivers who don’t know what else may collapse. As my wife says, there are two types of people “those who run towards the meteorite and those who run from it”. Survivors are those who ran from it.
Over the next few minutes and hours, word of the bridge collapse spread. My student Shanjiang Zhu has organized MnDOT’s loop detector data into a movie that shows the 15 minute traffic counts on all the loop detectors in the Twin Cities, comparing that number with the previous Wednesday’s count at the same time of day. Blue indicates lower volumes, red higher volumes. Clearly after the collapse, people heard quickly through various sources (cell phone, variable message signs, radio, etc.), and avoided large swaths of I-35W in the vicinity (which turns blue) and complementary feeder links, while competititve substitute links (Mn 100, I 35E, parts of I-94) saw an increase. We still have to compute how overall traffic volume and Vehicle Kilometers Traveled changed.
Once people were informed, on subsequent days people searched for alternatives. The alternative the first day for some was to avoid driving, but that quickly changed, and different routes became natural substitutes. A second movie compares the counts on the 15 days after the collapse with the average of the previous 8 weeks same day of week (so a Thursday is compared with the eight pre-collapse Thursdays). This illustrates the changes network wide. The movie is available.
Finally, there may be some longer term adaptations, but we don’t have enough information only one month into the changed situation to know about this yet. With colleagues Henry Liu and Kathleen Harder, we have obtained a National Science Foundation Small Grant for Exploratory Research to look at all of these issues in some more depth.