TIRPS - Testudo's Informed Route Planning System

From Cmsc434_f08
Jump to: navigation, search

This page will focus on the user needs: tasks, scenarios, and references.

Group Members

Thomas Graves
Calvin
Juan Ramirez
Kenny

Background

Washington DC and its surrounding areas are the “worst bottlenecks in the nation.” Traffic on these routes causes nearly 19 million hours of delays a year, 69 hours per peak traveler per year, and costs travelers roughly $1169 per year. In this area, the interchanges between I-495 and I-270 in Montgomery County and between I-495 and I-95 in Prince George Country are dubbed the worst commuting sports in the nation by AAA. The interchanges see daily car volumes of 243,425 and 185,125 respectively. Unfortunately, the University of Maryland’s location is right next to these two interchanges making the commute extremely difficult and inconvenient for faculty, students and staff.


If commuters know when and where the traffic occurs and ways to avoid it, the frustration and headaches of commuting in the Washington DC area can be reduced. The variability and uncertainty of traveling in the metropolitan area can be reduced and possibly eliminated by constructing a database that aggregates trip data under all different kinds of traveling conditions for the University of Maryland and its surrounding areas. A robust query engine built on top of the database can enable all commuters who must deal with the severe traffic congestion inherit in beltway and connecting interstate travel to better account for their traveling time and be prepared for delays. To accommodate the need of travel information among commuters, an informed route planning system can be developed that combines a large database of user-provided trip information and an associated query engine to make that information available to all commuters. Such a system will be referred to from here on out as Testudo’s Informed Route Planning System or TIRPS for short.


The TIRPS system can work not only for Washington DC and its surrounding areas (mainly the University of Maryland), but also for any other high congestion area. Such a system could be extended to other areas of mass commuter convergence.

Tasks

The main purpose of the TIRPS system is to address this problem, namely the issue of providing concrete information that commuters can use to make informed predictions about the length of a trip. To make the system successful, two different types of users are needed. The TIRPS system requires a user community of commuters dedicated to posting their specific trip information. The community must be large, active, and committed to generating a large dataset of trip information. The other set of users includes those commuters seeking trip and route information. The TIRPS system is only worthwhile to this user base if a large dataset that spans a wide range of trip and traveling related criteria is available for searching.


When functioning as intended, the TIRPS system will be self-sustaining through a cycle of information contribution and information use. The trip data resulting from the information use will be fed back into the system and the cycle will begin again. Specific benefits a user can gain from the TIRPS system include a reduction in trip length by making previously-unknown, alternate routes available to users. Information about traffic congestion during specific time periods can let a commuter adjust their departure time to avoid periods of high congestion. A reduction in travel time and the stop-and-go nature of most beltway commutes can improve fuel economy and result in a monetary benefit to commuters. In terms of psychological preparation for a trip, simple awareness of traffic conditions can reduce driver frustration by eliminating the surprise of a congestion related delay. Such an aggregation of commuter data to and from the University of Maryland can be used to construct traffic models for given driving conditions.

Scenarios

  • A new commuter student
Alice is a first-year student at the University of Maryland from Bowie, MD. She is planning her Tuesday commute to the University, for the first day of classes, so that she can arrive on time to her first class at 9:30am in the Jiménez Building. She first begins to think of all routes that she can take to get to the university. The easiest one that comes to mind is to take US-50 W to I-495 N and finally down US-1 S until she arrives to campus. Alice knows that I-495 has terrible rush hour traffic but does not know between which hours is traffic the worst. Alice has never had to deal with I-495 N’s morning rush hour traffic; she walked to her high school, Bowie High School, which was a mere six blocks away from her house, and drove a little less than five minutes down MD-197 to get to Bowie Town Center, where she worked. She uses Google Maps to get an estimate of how long her commute will be to campus and it returns 22 minutes. She figures that it will take her about 20 minutes to find a parking spot and to walk to Jiménez. So she decides that she should leave home a little bit before 9:00am.


On Tuesday, Alice leaves at 8:50am and runs into traffic on I-495 N, before the exit to I-295. She again runs into slow-moving traffic as she exits onto US-1 S. By the time she arrives to campus, Alice is very agitated from her struggle with rush hour traffic and is also late to her 9:30am lecture. Had Alice consulted the TIRPS database, she would have been able to better plan her trip by reading suggestions left by other commuter students’ who also take the route Alice chose. Alice would have known to anticipate rush hour traffic on her route by leaving her house at 8:50am. She could also choose to leave home earlier, after reading one commuter student’s message which suggested that rush hour traffic is lighter at 8:00am. Lastly, she could have changed her commuting route after reading one student’s itinerary that got him to campus in 25 minutes from Bowie in the morning.


  • A seasoned commuter student
Daniel is a junior Environmental Science major at the University of Maryland. Originally from Allentown, PA, Daniel lived in residential housing on campus for his first two years without a car. After finding a house to lease with three other Maryland students in Silver Spring, MD Daniel has brought his car from home so that he can drive himself and his roommates to campus in the morning. There are various routes that Daniel can take to get to the university campus such as taking I-495 S to US-1 S or taking US-193/University Boulevard E. Mapping the two routes on Google Maps, the first returns an 11-minute estimated travel time and the second returned 13 minutes. Daniel has taken both routes before and has never seen either route free of traffic. When he takes I-495 S he runs into moderate to heavy traffic because of the I-95 exit, often doubling the trip duration. US-193/University Boulevard E, on the other hand, does not pose much rush hour traffic as I-495, but has delayed Daniel longer than if he would have taken I-495. The large amount of local school buses, along with a greater number of street lights that he must drive through, can greatly vary Daniel’s trip duration.


Daniel has had to struggle with traffic too many times and is constantly looking for more efficient routes to campus that will reduce his trip duration. He knows that a large percentage of commuter students reside in Montgomery County. He especially understands how frustrating it is to find the most efficient route to campus and does not want new commuters to go through the same hardships that he has had to endure. So he logs onto the TIRPS database and shares his commuter experience from Silver Spring in hopes that it will help other commuters find a better route or be informed about what to anticipate if they were to take the same route that Daniel uses. He specifies his starting destination, the route that he takes to get campus, the time that he leaves for campus, traffic levels, and his suggestions. Daniel is informed of how helpful other users found his experience to be and is also informed if any user has commented on it.


  • Generate traffic models based on trip information
A University of Maryland grad student, Tammy, is compiling a report on the different campus commuter traffic trends for various months of the year in order to help professors devise a scheme to reduce traffic on the congested routes. Tammy needs a general idea of the congested routes that students try to avoid during different times of year; for example, during the summer months, Greenbelt road might be a better travel route since the school buses won’t be running to the local high school. She needs average times of travel from different areas for the morning commute and also for the ride home across various hours so that she can build traffic models for her report.


  • A group of students who live in the same community
A group of Maryland commuters all live along various parts of MD-193 in the Greenbelt community and drive separately each day. They generally do not like the College Park traffic and may be open to carpooling but really have no way of reaching out to other commuters in their immediate area. One of the commuters gets a flyer for TIRPS on his car one day when he returns to his car to sit in traffic and drive home. He decides to check it out and finds that there are 3 other commuters who live near and he reaches out to those that have their contact information posted. Now the commuters can potentially save gas and rotate driving days so that they each don’t have to drive in the traffic everyday.

References

Abdel-Aty, Mohamed A., Ryuichi Kitamura, Paul P. Jovanis (1997) Using Stated Preference Data for Studying the Effect of Advanced Traffic Information on Drivers' Route Choice. Transportation Research Part C 5 (1), 39 – 50.

The researchers used a computer aided phone survey along with a mailed survey to collect data on commuters’ route choices. The study showed that route was dependent on the information provided, namely traffic information. People seek to avoid uncertain travel times when choosing a route. The TIRPS system hopes to provide traffic information that will enable commuters to make judgments on the traffic conditions affecting a route at a specific time and thus reduce their uncertainty of taking the route.


"America's 24 Worst Highway Bottlenecks." Infoplease: Encyclopedia, Almanac, Biographies, Dictionary, Thesaurus. 2007. 2008 <http://www.infoplease.com>.

Presents research data on U.S.'s 24 worst highways, including I-495/I-95 and I-495/I-270 interchange. Data includes hours of delay per year, and vehicles allowed per day.


Bottom, J., "A Framework for Traffic Assignment with Travel Information," Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE , vol., no., pp.218-223, 2006 <http://ieeexplore.ieee.org>.

This article discusses the impact of traveler information on not only travelers but on the actual travel networks themselves. A driver’s choice of route could have negative consequences on the travel network in addition to their own travel time. The paper then proposes a framework for “addressing traffic assignment with traffic information.” This paper demonstrates that a system like TIRPS could not only have benefits for the individual traveler, it could also have benefits for general network condition as well.


Bovy, Piet H., and Mart Tacken. "Behavioural Reactions to Traffic Congestion." Colloquium Vervoersplanologisch Speurwerk (1995): 465-83.

Discusses the various causes of traffic congestion, the psychological effects of traffic on motorists, and the effects it has on activity of surrounding areas.


Burnett, Steven R. "Drive Smart Not Hard, You Could Be Saving Yourself Thousands!" Protium Fuel Systems Research & Development. Sept. 2008. 28 Sept. 2008 <http://www.protiumfuelsystems.com/tips.html>.

Discusses various ways to reduce fuel consumption. The author has been frequently updating this article to reflect current technologies and practices that help consumers. If College Park commuters are aware of ways they can increase gas mileage while driving on major roads or sitting in traffic they can reduce their negative impact on the environment.


Cooper, Thomas E., and John T. Jones. "Asymmetric Competition on Commuter Routes: The Case of Gasoline Pricing." Southern Economic Journal 74 (2007): 483-504.

The intensity of competition among firms depends on commuting patterns due to the fact that commuters are able to travel to any station located on their route without incurring incremental travel costs. This gives insight into our estimation of a retail gasoline price function for Lexington, Kentucky, by treating each commuter route as a separate market. Competition in these markets, however, displays an asymmetry because all the commuters travel to the Central Business District (CBD). To accommodate this asymmetry, each market segment on in each firm is a distinct submarket and includes independent variables (number of competitors and submarket length) from each submarket. Both sets of structural variables influence gasoline prices in the expected direction, but the variables representing the submarket near the CBD have significantly stronger effects. This data might come in handy when determining which routes are generally most populated during morning commutes.


Curtis, Carey, and Carlindi Holling. Universities TravelSmart Resource Kit. Diss. Curtin University, 2003. TravelSmart. 2003. 21: 32-36. Sept. 2008 <http://www.travelsmart.gov.au>.

Discusses marketing, promotion, and information dissemination techniques that could open commuters to changing their driving habits.


Demere, Marc. "Tips to improve your Gas Mileage." US Environmental Protection Agency. 2001. 2001 <http://www.fueleconomy.gov>.

Discusses tips to improve gas mileage, as well as recommends that motorists drive at 60 mph, remove excess weight and avoid excessive idling.


Downs, Anthony. Still Stuck in Traffic : Coping with Peak-Hour Traffic Congestion. New York: Brookings Institution P, 2003. Google Book Search. 26 Sept. 2008 <http://books.google.com>.

Focuses on psychological effects that traffic has on motorists, discusses why traffic is becoming increasingly worse and irreparable, and discusses various ways to change driving habits in order to avoid being delayed by traffic congestion.


"Getting More per Gallon." Meetings & Conventions Sept. 2008: 17-17.

Offers tips on how to obtain better gas mileage with automobiles. It suggests that drivers not speed past 60 miles per hour as fuel efficiency decreases exponentially past that point. Furthermore it suggests that gas mileage is reduced by two percent for every 100 lbs. extra on a vehicle. Lastly, it claims that gas mileage is also reduced as a result of poor automobile maintenance.


Levinson, David. The value of advanced traveler information systems for route choice, Transportation Research Part C: Emerging Technologies Volume 11, Issue 1, February 2003, Pages 75-87. <http://www.sciencedirect.com>.

Discusses the economic benefit of ATIS systems to travelers in terms of supply and demand. It states that ATIS systems have the greatest benefit in traffic reduction by keeping drivers informed of non-recurrent traveling conditions. The TIRPS system can target non-recurrent traveling conditions by allowing users to flag a trip with different attributes such as the presence of an accident. Such flagged trips can be aggregated to present a model of traffic conditions as a result of non-recurrent incidents.


Malone, Robert. "America's 12 Worst Traffic Traps." Business News and Financial News at Forbes.com. 11 June 2007. 11 June 2007 <http://www.forbe.com>.

Discusses twelve worst traffic cities in America. Data was based on US Department of Transportation research.


Maple Publishing, ed. "Tweaks can reduce fuel consumption." Fleet Equipment May 2007.

Discusses various vehicle modifications and driving habits that can help increase gas mileage while driving and sitting in traffic. The ideas are proposed by Telargo, a provider of mobile asset management solutions.


Minett, Paul. "Achieving High Volume Carpooling by Eliminating the." Flexible Carpooling. 2008. Trip Convergence Ltd. 28 Sept. 2008 <http://www.flexiblecarpooling.org/flexiblecarpoolingresearchproposal.pdf>.

Highlights the benefits from carpooling and the amount of energy saved on a day to day basis as a result of carpooling. Data was gathered in the San Francisco area and the author presents it to show the significant benefits motorists would experience as a result of carpooling, such as a decrease in traffic.


Nivala, Annu-Maaria; Brewster, Stephen; Sarjakoski, Tiina L (2008). ‘Usability Evaluation of Web Mapping Sites’, Cartographic Journal, The, Volume 45, Number 2, May 2008, pp. 129-138(10).

Examines four of the web’s most popular mapping sites and runs usability tests on them. Google Maps is one of the four sites tested and has better results than its competitors. The paper additionally suggests guidelines for developing online mapping software. Since the TIRPS system will utilize the Google Maps API and provide a mapping interface, such guidelines are important for its development. Additionally, the paper serves as further support that the Google Maps API is the best backbone to build the system upon.


Skarlatidou, A. and Haklay, M. (2006) ‘Public Web Mapping: Preliminary Usability Evaluation’, GIS Research UK 2005, Nottingham.

Presents a usability study of different types of online mapping software. The paper gave Google Maps a favorable score in terms of a reduced task time and high success rate. This is relevant to the TIRPS system because the Google Maps API will be used as the backbone of the system.


Studios, Velir, ed. "Annual Cost of Traffic Congestion Per Peak Traveler." Metropolitan Quality of Life Data. 2007. Harvard School of Public Health. 2007. <http://diversitydata.sph.harward.edu>.

Discusses annual cost of traffic congestion per peak traveler.


Trzcinski, Adam J., and Ross B. Corotis. "Alternative valuation of highway user delay costs." Civil Engineering and Environmental Systems 24 (2007): 87-97.

Discusses how time spent in traffic is associated with an increase in aggravated driving and travel expenses; also discusses how motorists are more open to changing driving habits when the possible benefit is spending less time in traffic and reducing overall trip duration.