Category Archives: GPS

A survival analysis-based choice set formation approach for single-destination choice using GPS travel data

SDC figure

Recent working paper:

This research investigates how land use and road network structure influence home-based single-destination choice in the context of trip chains, using the in-vehicle GPS travel data in the Minneapolis-St. Paul Metropolitan area. We propose a new choice set formation approach which combines survival analysis and random selection. Our empirical findings reveal that: (1) Accessibility and diversity of services at the destination influences individuals’ destination choice. (2) Route-specific network measures such as turn index, speed discontinuity, and trip chains’ travel time saving ratio also display statistically significant effects on destination choice. Our approach contributes to methodologies in modeling destination choice. The results improve our understanding on travel behavior and have implications on transportation and land use planning.

This paper is part of Arthur Huang’s Dissertation.

Accessibility, network structure, and consumers’ destination choice: a GIS analysis of GPS travel data

Working paper:

Walking areas around a trip destination
Walking areas around a trip destination
  • Huang, Arthur and Levinson, David (2011) Accessibility, network structure, and consumers’ destination choice: a GIS analysis of GPS travel data.

    Anecdotal and empirical evidence has shown that road networks, destination accessibility, and travelers’ choice of destination are closely related. Nevertheless, there have not been systematic investigations linking individuals’ travel behavior and retail clusters at the microscopic level. Based on GPS travel data in the Twin Cities, this paper analyzes the impacts of travelers’ interactions with road network structure and clustering of services at the destination on travelers’ destination choice. A multinomial logit model is adopted. The results reveal that higher accessibility and diversity of services in adjacent zones of a destination are associated with greater attractiveness of a destination. Further, the diversity and accessibility of establishments in an area are often highly correlated. In terms of network structure, a destination with a more circuitous or discontinuous route dampens its appeal. Answering where and why people choose to patronize certain places, our planning, our findings shed light on the design of road networks and clusters from a travel behavior perspective.

    (working paper)

Google Killed Map Traffic Estimates Because It Just Didn’t Work

From Gizmodo: Google Killed Map Traffic Estimates Because It Just Didn’t Work

If you’re wondering how road traffic’s gonna slow you today, don’t turn to Google Maps anymore—the site’s killed its estimates. Not because it wasn’t popular. It turns out those road calculations didn’t exactly correlate to, you know, reality.
The Atlantic describes the discovery of perturbed Maps users, who complained to Google when they noticed the change. Its answer?

[W]e have decided that our information systems behind this feature were not as good as they could be. Therefore, we have taken this offline and are currently working to come up with a better, more accurate solution. We are always working to bring you the best Google Maps experience with updates like these!”

Translation: traffic didn’t work. And as the Atlantic’s Nicholas Jackson asks, how could Google be sucking down so much locational data from Android drivers and be botching it to the point that they pulled it down entirely? [The Atlantic]”

A big defeat for the biggest information provider. But using in-vehicle GPS on mobile phones as a probe is coming, and will eventually get it right (approximately, if lagged). The problem of course is that traffic is dynamic, and even a 5 minute lag will be quite off if there is an incident or something non-steady state. However as a signal of whether things are normal, it probably works.
See:

Information provision is probably best for what an individual will not know from routine behavior—random incidents and unfamiliar territory. The qualitative conclusion that incidents and the unexpected are where the greatest gains from traveler information are to be found reinforces the results from our simulations. Those models show that a low level of probes can provide useful information by rapidly detecting incidents, whereas a much greater number is needed to provide any gains from recurring congestion.

New air guidance system threatened with delays

The Washington Post reports on NextGen:

New air guidance system threatened with delays : “Now the Obama administration has embarked on the single most ambitious and expensive
national transportation project since completion of the interstate highway system: a program called the Next Generation Air Transportation System (NextGen).
The NextGen concept sounds simple: Replace an air traffic system based on 60-year-old radar with a satellite-based Global Positioning System (GPS) network that would be far more versatile and efficient. In reality, it is an extraordinarily complex undertaking, threatened with delay by airline fears that the government will not deliver the system in time to justify their expenditures.
NextGen demands the largest investment ever made in civil aviation: between $29 billion and $42 billion for equipment, software and training by 2025. The cost would be shared by a federal government struggling with budget constraints and an airline industry that has been drained by years of recession and high fuel prices. Those tensions over funding threaten to slow the launch of NextGen, despite near-universal support for the program, and delays could prove costly.”

TomTom user data sold to Dutch police, used to determine ideal locations for speed trap

TomTom user data sold to Dutch police, used to determine ideal locations for speed traps — Engadget

BY TIM STEVENS
POSTED APR 27TH 2011 01:53PM
TomTom user data sold to Danish police, used to determine location of speed traps
We like it when the accumulated speed data from GPS devices helps us avoid traffic incidents and school zones. As it turns out, though, there are some other uses for the same stats. Dutch news outlet AD is reporting that such data captured by TomTom navigation devices has been purchased by the country’s police force and is being used to determine where speed traps and cameras should be placed. TomTom was reportedly unaware its data was being used in such a way, but if the police would only agree to sell the data on the location of its speed cameras and traps back to TomTom, why, this could be the beginning of a beautiful relationship.
Update: TomTom has issued a statement, which we have embedded after the break. To be totally clear all this data is being collected anonymously and the police have no idea exactly who is speeding, just that speeding has taken place.
Update 2: We have an English-language video from TomTom CEO Harold Goddijn embedded after the break. In it he says that the company will ‘prevent that type of usage’ of the navigation data going forward. So, no need to turn off the ‘ol GPS when you’re late for work tomorrow morning.

PR Statement
1) Customers come first at TomTom;
When you use one of our products we ask for your permission to collect travel time information on an anonymous basis. The vast majority of you do, indeed grant us that permission. When you connect your TomTom to a computer we aggregate this information and use it for a variety of applications, most importantly to create high quality traffic information and to route you around traffic jams.
We also make this information available to local governments and authorities. It helps them to better understand where congestion takes
place, where to build new roads and how to make roads safer.
We are actively promoting the use of this information because we believe we can help make roads safer and less congested.
We are now aware that the police have used traffic information that you have helped to create to place speed cameras at dangerous locations where the average speed is higher than the legally allowed speed limit. We are aware a lot of our customers do not like the idea and we will look at if we should allow this type of usage.
2) This is what we really do with the data;
– We ask for your permission to collect historical data. You can opt in or opt out and can disable the data collection function at any time.
– If you are using a LIVE device, you receive traffic information in real time and you automatically contribute to generating traffic information.
– We make all traffic data anonymous. We can never trace it back to you or your device.
– We turn anonymous data into traffic information to give you the fastest route available and route you through traffic jams in real time.
– We are working with road authorities around the world to use anonymous traffic information to help make roads flow more efficiently and safer.
– Our goal is to create a driver community capable of reducing traffic congestion for everyone.

Value of Reliability: High Occupancy Toll Lanes, General Purpose Lanes, and Arterials

Recent working paper:

In the Minneapolis-St. Paul region (Twin Cities), the Minnesota Department of Transportation (MnDOT) converted the Interstate 394 High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes (or MnPASS Express Lanes). These lanes allow single occupancy vehicles (SOV) to access the HOV lanes by paying a fee. This fee is adjusted according to a dynamic pricing system that varies with the current demand. This paper estimates the value placed by the travelers on the HOT lanes because of improvements in travel time reliability. This value depends on how the travelers regard a route with predictable travel times (or small travel time variability) in comparison to another with unpredictable travel times (or high travel time variability). For this purpose, commuters are recruited and equipped with Global Positioning System (GPS) devices and instructed to commute for two weeks on each of three plausible alternatives between their home in the western suburbs of Minneapolis eastbound to work in downtown or the University of Minnesota: I-394 HOT lanes, I-394 General Purpose lanes (untolled), and signalized arterials close to the I-394 corridor. They are then given the opportunity to travel on their preferred route after experiencing each alternative. This revealed preference data is then analyzed using mixed logit route choice models. Three measures of reliability are explored and incorporated in the estimation of the models: standard deviation (a classical measure in the research literature); shortened right range (typically found in departure time choice models); and interquartile range (75th – 25th percentile). Each of these measures represents distinct ways about how travelers deal with different sections of reliability. In all the models, it was found that reliability was valued highly (and statistically significantly), but differently according to how it was defined. The estimated value of reliability in each of the models indicates that commuters are willing to pay a fee for a reliable route depending on how they value their reliability savings.

Waze: Crowd-sourced real-time (lagged) traffic information

Waze is a mobile smart-phone application that lets you see real-time traffic information from other Waze users, and share it. Basically, it uses your smart-phone as a probe. It also lets you update the network (of course if the network is still incomplete, real-time traffic data is almost assuredly sparse). This really depends on critical mass, as I described in this paper:

And lagged information may in some instances be worse than no (or historical average) information.