Traffic Congestion: Intelligent Routing & Its Effects on Fuel Efficiency
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Traffic Congestion: Intelligent Routing & Its Effects on Fuel Efficiency & Total Congestion Costs Kofi Adofo Raymond Govus Andrew Jairam Michelle Udeli EXECUTIVE SUMMARY The average American has been shifting towards an increasingly vehicle-dependent lifestyle over the past quarter of a century due to changes in generational demographic and housing preferences. Current patterns in metropolitan growth have favored edge areas over city centers. Furthermore, most new growth is characterized as single-use land development, such as business parks, housing suburbs, or strip malls. This stratification of land uses necessitates additional driving and eliminates the ability to group vehicle trips. Additionally, the number of cars in the country has continued to dramatically outpace the construction of new highways or public transportation options. The combinations of these two factors result in ever increasing congestion rates and vehicle residence time among commuters. This translates to significant levels of unnecessary emissions which could be something targeted early in campaign to reduce national C02 levels. Furthermore, looking beyond emissions, the time and money wasted in congestion alone should necessitate a solution to the problem. The solutions which we proposed to the congestion problem are stratified by the time scale which they operate on. Short term solutions involve making the current system more efficient and distributing the traffic load among the available mass transit options. Long term solutions will require a different approach to the manner which we regulate growth and transportation. Incentives to decrease the proximity between housing and employment in metropolitan areas should be pursued as well as a large scale re-investment in mass public transit. For both solutions, a reinvestment in the national transportation infrastructure is required along with informed and targeted policy changes. BACKGROUND Traditionally, urban Americans both resided and worked within city centers. At the start of the last century, people lived relatively close to one another and thus their costs of transporting both themselves and their goods were relatively low. The primary method of transport was by foot with low need for vehicles within city quarters. Living and working at high densities allowed companies and factories to transport goods via shipping and the rail system. As time progressed, inhabitants of urban centers began moving further away to suburban locations. There were several causes of this urban sprawl, some of which were the increase population and household income, decreasing costs in transport, a strong economy and disjointed municipal governments. Around the late 1950s, the typical American city still had a large density where most people worked, however a majority of these workers resided in suburban areas and commuted to work using vehicles. As time progressed, the cost of transport continued to fall and thus allowed people to live further from their place of work. This phenomenon is known as urban sprawl. The high density walking city that existed last century had been transformed into a medium density driving city of today (Glaeser et al. 2001). Page | 2 A survey of the location of jobs in the 100 largest U.S. metropolitan areas, taken in 2001, found that across the largest 100 metropolitan areas, on average, only 22% of people work within three miles of the city center (Glaeser et al. 2001). That survey also recorded that over 35% of people work more than ten miles from the city centers. This increase in the population of workers traveling to and from city centers creates a demand for more travel space. This results in a large number of vehicles on the road, exceeding the road’s capacity and causing traffic congestion. Traffic congestion increases transit times as a consequence of increased idling during peak travel hours. Figure 1 - Congestion Growth Tend from 1982 to 2007. Source: 2009 Urban Mobility Report With the steady increase in the world’s population, the negative effects that congestion has on the climate system will increase. According to the 2009 Urban Mobility Report generated by Texas A&M University, the annual time delay per peak traveler increased from about 14 hours in the year 1982 to about 36 hours in the year 2007. Figure 1 shows the change in congestion growth for various population area sizes from the year 1982 to 2007. Congestion growth has shown a positive trend for all population area sizes. From this figure, it is apparent that the total hours of delay per peak traveler increases as the population area size increases. Page | 3 Figure 2- Wasted Fuel per Traveler from 1982 to 2007 for Small to Large Cities, Source: 2009 Urban Mobility Report As congestion time increases for commuters, fuel consumed or “wasted fuel” becomes very important factor. Figure 2 shows the total amount of fuel wasted while idling for peak drivers in 2007. For the year 2007, the total amount of wasted fuel (in gallons) per peak traveler for all city sizes was approximately 24. That average increased from an average of about 7 gallons in the year 1982. With these averages, the total congestion costs were calculated and analyzed by Texas A&M University. Figure 3 shows the results of the calculation of total congestion cost per peak traveler. Figure 3 - Total Annual Cost of Congestion per Peak Traveler, Source: 2009 Urban Mobility Report Page | 4 Total Congestion Cost = Cost of Wasted Fuel + Average Cost of Time To calculate the total cost of wasted fuel, the national average cost of fuel was multiplied by the total amount of fuel wasted per peak traveler. The average cost of time was estimated by Texas A&M University to be about $15.47/peak traveler, which was multiplied by the total delay per peak traveler. The sum of the two previous calculations yielded the total congestion cost per peak traveler from 1982 to 2007. For the year 2007, the total congestion cost per peak traveler was approximately $757 per year. For the entire U.S., the travel delay, in billions of hours, increased from 14 in 1982 to about 36 in 2007. The total wasted fuel for the nation per peak traveler increased from about 9 gallons in 1982 to about 24 gallons in 2007. The travel time index was introduced in the 2009 Urban Mobility Report to be an approximate measure of the additional time added to a route due to congestion. The travel time index is essentially the ratio of travel time in the peak time period to the travel time at free-flow (no congestion) conditions. An example of how this quantity is used goes as follows: A driver obtains directions from a popular navigation website. This navigation website estimates the driver’s arrival time based on local speed limits. If the travel time index (TTI) for the driver’s city is 1.35, and the estimated time of arrival is 20 minutes, the driver would reach their destination in approximately 27 minutes. That is to say that the estimated arrival time is multiplied by the TTI and the predicted travel time, with the inclusion of traffic congestion can be calculated. Figure 4 shows a plot of the daily fluctuations in TTI (blue line). Figure 4 - Daily Fluctuations in Time Travel Index (TTI), Source: 2009 Urban Mobility Report Page | 5 As the amount of congestion increases during peak times (6-9am, 3-7pm), the total TTI also increases. Since congestion causes and increase of time travel, the planning time index (PTI) can calculate determine how what time drivers should leave in order to arrive at their destination in ample time. The buffer index (BI) is just the difference between TTI and PTI. Figure 5 takes into account all of the previous terms, and compares a general plot of congestion, traffic volume, fuel cost and transit ridership. Figure 5 - Fluctuations of National Traffic Volume, Congestion, Transit Ridership and Fuel cost from 2005 to 2008. As the national average price of fuel increased, so did the total amount of transit ridership. The total volume of traffic as well as the total TTI decreased at this same time. The TTI dropped so low during the summer of 2008 that it fell below the TTI value for May 2008. Traffic congestion is an increasing problem throughout the entire world. In addition to being time-consuming, congestion has an impact on air quality and thus the climate system. Traffic congestion is becoming an even bigger problem with the steady increase of the world’s population. Transportation sources accounted for approximately 29% of the total U.S. greenhouse gas (GHG) emissions. Some of these GHG’s include Carbon dioxide, Methane and Nitrous oxide. Vehicles also emit toxins such as Hydrofluorocarbons (cooling systems) and black carbon. A gallon of regular grade gasoline contains about 8.8kg of CO2 or about 19.4 lbs. of CO2. On average, Methane, Nitrous oxide and Hydrofluorocarbons emissions represent roughly 5-6% of total GHG emissions from the transportation sector. Carbon dioxide emissions, however, account for about 94-95% of GHG emissions from the transportation sector. In the year 2008 the total amount of GHG emissions was about 1184.5 Tg CO2 Eq. Of that figure, 1111.2 Tg CO2 Eq. was Page | 6 from Carbon dioxide, 1.4 Tg CO2 Eq. was from Methane, 21.2 Tg CO2 Eq. was emitted from Nitrous oxide and 50.7 Tg. CO2 Eq. was emitted from Hydrofluorocarbons. With these figures predicted to amplify in the future due to an increasing population, it is imperative that some solutions be developed in order to alleviate this growing problem. This paper proposes both long term and short-term solutions to decrease the total emissions produced from congestion. The main focus will be on short-term solutions and their effectiveness.