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.
Abstract: The replacement I-35W bridge in Minneapolis saw less traffic than the original bridge though it provided substantial travel time saving for many travelers. This observation cannot be explained by the classical route choice assumption that travelers always take the shortest path. Accordingly, a boundedly rational route switching model is proposed assuming that travelers will not switch to the new bridge unless travel time saving goes beyond a threshold or “indifference band”. To validate the boundedly rational route switching assumption, route choices of 78 subjects from a GPS travel behavior study were analyzed before and after the addition of the new I-35W bridge. Indifference bands are estimated for both commuters who were previously bridge users and those who never had the experience of using the old bridge. This study offers the first empirical estimation of bounded rationality parameters from GPS data and provides guidelines for traffic assignment.
Keywords: Route Choice, Travel Demand Modeling, Bounded Rationality, Indifference Band, GPS Study, Travel Behavior, Networks
The increasing sophistication of data collection and analysis gives us deeper insights into human behavior — and how we make decisions about everyday travel.
Transportation debates, from the local to national level, are invariably waged between competing interests. There are players representing economic development, road construction, the environmental lobbies, and diverse groups of transportation users — just to name a few. But there is also an important role for independent experts to play — not just as honest brokers, but as analysts who can assess what they learn from the increasingly sophisticated collection of data about travel and human behavior. And this is where academics can step in. Research that I have conducted with colleagues at the University of Minnesota has allowed us break down travel behavior and draw some surprising lessons that can guide transportation policy.
Symposium is a relatively new venue, aimed at building bridges across disciplinary domains in the academy.
Thomas Flores on the rise of technocrats; Scott Taylor on what historians can teach us about climate change; Chuck McCutcheon on how one law professor has played a part in both sides of the surveillance debate; and Judith Sebesta on whether college really is for everyone.
Route choice analysis investigates the path travelers follow to implement their travel plan. It is the most frequent, and thus arguably the most important decision travelers make on a daily basis. Long established efforts have been dedicated to a normative model of the route choice decision, while investigations of route choice from a descriptive perspective have been limited. Wardrop’s first principle, or the shortest path assumption, is still widely used in route choice models. Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. Moreover, factors beyond travel time, such as preferences for travel time reliability, inertia in changing routes, and travel experience that could also have significant impacts on route choice, have not been fully explored and incorporated in route choice modeling. The phenomenon that people use more than one route between the same origin and destination during a period of time is not addressed by conventional route choice models either.
To bridge these gaps, this dissertation systematically evaluates people’s route choice behavior using data collected in the Minneapolis – St. Paul metropolitan area after the I-35W Bridge Collapse. Both aggregate traffic data and individual survey data show gaps between models based on shortest travel time assumption and traffic conditions observed in the field. This study then employs the individual GPS trajectory and GIS maps to systematically evaluate the characteristics of routes people actually use. Merits of route choice set generation algorithms widely used in practice are assessed. The phenomenon of route diversity is clearly revealed through analysis of field data. A route portfolio model is proposed to explain the rationale of choosing a portfolio of routes under uncertainty about network conditions. It is posited that a rule-based model,
comprehensively considering travelers’ characteristics, additional network metrics, and previous travel experience will better replicate observed route choices than the tradi- tional assumption of simply minimizing travel time or travel cost. Findings from this dissertation could also inform other parts of travel demand modeling.
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.
A major strategy of federal ITS initiatives and state departments of transportation is to provide traveler information to motorists through various means, including variable message signs, the internet, telephone services like 511, in-vehicle guidance systems, and TV and radio reports. This is relatively uncontroversial, but its effectiveness is unknown. Drivers receive value from traveler information in several ways, including the ability to save time, but perhaps more importantly, other personal, social, safety, or psychological impacts from certainty. This information can be economically valued. The benefits of reduction in driver uncertainty when information is provided at the beginning of the trip by various means is the main variable we aim to measure in this research, in which we assess user preferences for routes as a function of the presence and accuracy of information, while controlling for other trip and route attributes, such as trip purpose, travel time, distance, number of stops, delay, esthetics, level of commercial development, and individual characteristics. Data is collected in a field experiment in which more than 100 drivers, given real-time travel time information with varying degrees of accuracy, drove four of five alternative routes between a pre-selected OD pair in the Twin Cities metro area. Ordinary regression, multinomial, and rank-ordered logit models produce estimates of the value of information with some variation. In general, results show that travelers are willing to pay up to $1 per trip for pre-trip travel time information. The value of information is higher for commute and event trips and when congestion on the usual route is heavier. The accuracy of the traveler information is also a crucial factor. In fact, there do not seem be incentives for travelers to use traveler information at all unless they perceive it to be accurate. Finally, most travelers (70%) prefer that such information should be provided for free by the public sector, while some (19%) believe that it is better for the private sector to provide such service at a charge. Over 35% of subjects are willing to pay for OD-customized pre-trip travel time information.
Keywords: Value of Information, Advanced Traveler Information System (ATIS), Real-Time Traffic Operations, Travel Behavior, Spatial behavior, Wayfinding Behavior, Route Choice.