This research quantifies the variation of network structure within the Minneapolis – St. Paul metropolitan area and relates it to average travel time to work for each Minor Civil Division (MCD) in the metro area. The variation of these measures within the metropolitan area is analyzed spatially. The measures of network structure are then related to observed travel. Better connected networks have lower average travel times, all else equal. The results corroborate a relation between network structure and travel and point to the importance of understanding the underlying street network structure.
Planning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework.
The Federal Highway Administration’s Transportation Policy Unit has a series of reports on Transportation Futures. I was involved in one of them as an advisor to the consultant, though my name is not on the report, so I am not responsible. The report is now online:
Impacts of Millenial Student Loan Debt on Transportation Choices
Now the largest generation in America, the Millennials are not driving at the same rates of their predecessor generations, the Baby Boomers and Generation X. There have been plenty of studies about the millennial generation’s lack of interest in driving. Many conclude that Millennials are fascinated by technology or urban culture.
According to AAA’s findings of the 2013 ‘Your Driving Costs’ study, annual automobile spending for an average sedan owner are $9,122 (Based on 15,000 miles annual usage). For someone newly out of college with student loan debt, automobile ownership may feel out of reach. Millennial student loan debt is a widely discussed topic. Approximately 40 million Americans hold student loan debt. Currently more than 70 percent of U.S. students who graduate with a bachelor’s degree leave with debt, averaging $28,400. According to the White House Council of Economic advisors, 61% of adult millennials attended college, compared to 46% of their Baby Boomer parents. In 2014, the total outstanding student loan debt in the US surpassed $1 trillion.
This paper attempts to investigate the impact of student loan debt along with other variables on the millennial transportation choices.
The data was tortured looking for a relationship. If there is one, it is weak. For instance see the finding buried on p. 34
“In general, cutting back on transportation expenses may not be a central priority for those with student loans, as their job earnings enable such individuals to handle rising transportation costs. Indeed, the data shows a positive relationship between income and student loan value (Figure 15).
Taking into account all student loan holders, the relationship between student loans and transportation expenses appears unclear. While some analyses suggest a slightly positive relationship (i.e., the uptick in transportation expenses for loan-holders), many of the other trends can be explained by Figure 15. Student loan holders in our data are generally well-off, which would contribute to higher transportation expenses.
Across the board, people spent less on transportation as a percentage of household expenditures post- 2008, but Millennials showed a particularly large difference between loan-holders (Figure 16, right) and those without loans (Figure 16, left). While we might consider this drop to be connected to student loan commitments, a number of analyses seem to refute this idea.”
Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder if it is possible to quantitatively characterize our difficulty to navigate in them and whether such navigation exceeds our cognitive limits. A transition between different searching strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of another limit associated to the cognitive overload and caused by large amounts of information to process. In this light, we analyzed the world’s 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the “Dunbar number,” which represents a limit to the size of an individual’s friendship circle, our cognitive limit suggests that maps should not consist of more than about 250 connections points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks: in large cities such as New York, Paris, and Tokyo, more than 80% of trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and consequently the traditional view of navigation in cities has to be revised substantially.
My take is this greatly supports things like Grid networks and network simplification (see the work of Jarrett Walker). This looked at rail. Think about buses. In a few years, people will just let their apps navigate them, and human cognition limits may fall off the chart.
Few empirical studies of revealed route characteristics have been reported in the literature. This study challenges the widely applied shortest-path assumption by evaluating routes followed by residents of the Minneapolis–St. Paul metropolitan area, as measured by the GPS Component of the 2010 Twin Cities Travel Behavior Inventory conducted by the Metropolitan Council. It finds that most travelers used paths longer than the shortest path. This is in part a function of trip distance, trip circuity, number of turns, and age of the driver. Some reasons for these findings are conjectured.
This paper analyzes the relationship between road network structure and the percentage of speeding using GPS data collected from 152 individuals over a 7 day period. To investigate the relationship, we develop an algorithm and process to match the GPS data and GIS data accurately. Comparing actual travel speed from GPS data with posted speed limits we measure where and when speeding occurs, by whom. We posit that road network structure shapes the decision to speed. Our result shows that the percentage of speeding, which is calculated by travel distance, is large in high speed limit zones (e.g. 60 mph ) and low speed limit zone (less than 25 mph); in contrast, the percentage of speeding is much lower in the 30 – 50 mph zone. The results suggest driving pattern depends on the road type. We also find that if there are many intersections in the road, average link speed (and speeding) drops. Long links are conducive to speeding.
Using travel diary data from 2000-2001 and 2010-12 this research examines funda- mental 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
Recent trends in the United States suggest a movement toward saturation of vehicle ownership. This paper examines this trend through an analysis of car ownership in the Minneapolis- St. Paul, Minnesota (USA) metropolitan region. Data from pooled cross-sectional household surveys are used to calibrate a model of car ownership that includes birth cohort effects to capture unobserved variations in preference toward car ownership across generations. Declines in household size and worker status have significant impacts in limiting the growth of car ownership, but they are also coupled by an apparent softening of preferences toward ownership among young adults.
Keywords: car ownership; cohort; generational effect; aging; income; saturation; United States
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