Taveler Behavior.docx

26An Exploratory Study on Modeling Traveler Behavior: Survey and Game DesignAn Exploratory Study on Modeling Traveler Behavior: Survey and Game DesignDong PanGeorge Washington University AbstractThis study first aims to understand the potential determinants of active transportation. Using the current existing model: theory of planned behavior, it is supposed that traveler behavior is completely reasoned generated by intention and habit is added as a supplementation to this model. To settle the determinants of intention and habit into a finite number and measureable set, surveys are designed to investigate participants intention and habit. Then in the gaming environment, participants would go through an entire trip chain (from initial decision making to arriving at destination, re-decision allowed before and along the trip). Data collection is ongoing on the process. This would further help analyze how to lead travelers to overcome their habits and provide guideline for the transportation management strategies (specifically, ATDM in this study).Keywords: traveler behavior, theory of planned behavior (TPB), habit, survey and game design1 Literature Review1.1 The Theory of Planned BehaviorTo gain an understanding of the determinants of active transportation, models from several research areas have been used, including social marketing, social-ecologic models, and urban design and planning models (Giles & Donovan, 2003; Handy & Boarnet, 2002; Baranowski et al., 2003; Saelens et al., 2003; as cited by de Bruijn et al., 2009). Additionally, theoretical models that emphasize cognitive evaluations and planned intentions have guided research on the determinants of active transportation. These latter models include social-cognitive theory, the transtheoretical model, and the theory of planned behavior (Ajzen, 1991). According to the latter, behavior is primarily determined by intention, a motivational state toward being physically active. Intention is theorized to be a function of three social-cognitive constructs: attitude (i.e., the degree to which being physically active is positively or negatively valued); subjective norm (i.e., perceived social pressure from significant others to be physically active); and perceived behavioral control (i.e., the extent to which being physically active is easy and under ones control). These three determinants are assumed to interact with each other (see Figure 1). Figure 1. Theory of Planned Behavior (Source: Ajzen I., 1991)The theory of planned behavior is the extension of Fishbein and Ajzens (1985; Ajzen & Fishbein, 1980) theory of reasoned action that is widely considered the dominant attitude-behavior model. According to the theory of reasoned action, only attitudes and subjective norms trigger intention, which further generates behaviors. However, many behaviors pose difficulties of execution that may limit volitional control, thus rendering it useful to consider perceived behavioral control in addition to intention. To the extent that people are realistic in their judgments of a behaviors difficulty, a measure of perceived behavioral control can serve as a proxy for actual control and can contribute to the prediction of the behavior in question (Ajzen, 1991). With the addition of perceived behavioral control in the theory of planned behavior, Ajzen (1987) argues that the predictability of intentions is significantly improved. The theory of planned behavior has been applied successfully to predict and explain diverse behaviors, such as voting choices, weight loss, smoking cessation, and committing traffic violations (Ajzen, 1991; Conner & Armitage, 1998; as cited by Heath & Gifford, 2002).The theory of planned behavior works on the presupposition that social behavior is completely reasoned. Thenit is arguedfrequency of prior behavior should have only an indirect link to later behavior; its effect should be mediated by intention and perceived behavioral control. However, when added to the regression equation, past behavior is typically found to significantly improve the prediction of later behavior, over and above the effects of intentions and perceptions of behavioral control (Ajzen, 1991; Bagozzi, Bentler & Speckart, 1979; Fredricks & Dossett, 1983; Ouellette & Wood, 1998; as cited by Bamberg, Ajzen& Schmidt, 2003). Findings of this kind are generally taken to mean that the behavior under consideration, rather than being completely reasoned, is at least in part under the direct control of the stimulus situation, that is, that it habituates with repeated performance. According to this view, frequency of past behavior is an indicator of habit strength, and it can be used as an independent predictor of later action.More recent models on physical activity (Kremers et al., 2006; Pikora et al.) have acknowledged the potentially important role of habit. Habit is behavior that has been routinely performed and is, as a result of repetition, automaticallyrather than intentionallyset in motion (Ouellette & Wood, 1998; Bargh & Chartrand, 1999). This would imply that once behavioral patterns are established and have become habitual, intentions are less relevant predictors of behavior. Thus, strong habits put a boundary limitation on the applicability of reasoned-action models, such as the theory of planned behavior (Ouellette & Wood, 1998; Aarts et al., 1998)De Bruijn et al. (2009) had done a research on the cause-and-effect relationship between habit and bicycle use. The results showed that habit strength was the strongest correlate of bicycle use, indicating that bicycle use as a means of active transportation can become habitual. Additionally, these results further showed that when bicycle use was strongly habitual, intention to use a bicycle was a weak and nonsignificant correlate of actual bicycle use. In contrast, when bicycle use was a weak habit, intention was a stronger and significant correlate of actual bicycle use. These findings are in line with other recent empirical evidence (de Bruijn et al., 2007; de Bruijn et al., 2008; Kremers & Brug, 2008)However, some other researchers argue that an independent measure of habit failed to mediate the effects of past on later behavior. They concluded that choice of travel mode is largely a reasoned decision; that this decision can be affected by interventions that produce change in attitudes, subjective norms, and perceptions of behavioral control; and that past travel choice contributes to the prediction of later behavior only if circumstances remain relatively stable. Bamberg & Schmidt (2003) had focused on public transportation (e.g., bus) in their researchChoice of Travel Mode in the Theory of Planned Behavior: The Roles of Past Behavior, Habit, and Reasoned Action. They revealed that taking the bus to work on a regular basis may reflect a positive value placed on public transportation, the convenience of nearby bus stops, lack of alternative modes of transportation, a feeling of relative security on the bus, and so forth. As long as the configuration of controlling factors remains stable over time, there is no reason for the behavior to change. Later behavior, being under the control of an unchanging set of factors, will by necessity resemble previous behavior. In the context of the theory of planned behavior, behavioral stability over time is attributed to the stability of intentions and of perceived behavioral control. These factors presumably determined the behavior in the past and, if they remain unchanged, will produce corresponding behavior in the future. Thus, there is no need to assume that the behavior has come under the control of habit. Further, Bamberg & Schmidt had conducted an experiment among college students at the University of Giessen in Germany on whether taking a car or bus to the campus. They introduced a prepaid “semester ticket” that permitted unlimited rides on the local bus system by presentation of a valid student identification. And the pre-payment was required as part of the tuition fee. Based on the theory of planned behavior, it was believed that the “semester ticket” make attitudes, subjective norms, and perceptions of control to riding the bus more favorable. Therefore, a considerable proportion (declined from 46% to 31%) of students that habituated to driving to campus was persuaded to take a bus instead. It was concluded that, with sufficient change in attitudes, subjective norms, or perceptions of behavioral control, the effect of prior on later behavior should become nonsignificant.Briefly, according to the theory of planned behavior, human action is guided by three kinds of considerations: beliefs about the likely consequences of the behavior (behavioral beliefs), beliefs about the normative expectations of others (normative beliefs), and beliefs about the presence of factors that may further or hinder performance of the behavior (control beliefs). In their respective aggregates, behavioral beliefs produce a favorable or unfavorable attitude toward the behavior; normative beliefs result in perceived social pressure or subjective norm; and control beliefs give rise to perceived behavioral control, the perceived ease or difficulty of performing the behavior. Though habit does play a role that affects behavior, it has not reached an agreement whether it should be treated as an independent determinant.1.2 Active Transportation and Demand ManagementActive Transportation and Demand Management (ATDM) is the dynamic management, control, and influence of travel demand, traffic demand, and traffic flow of transportation facilities. Through the use of available tools and assets, traffic flow is managed and traveler behavior is influenced in real-time to achieve operational objectives, such as preventing or delaying breakdown conditions, improving safety, promoting sustainable travel modes, reducing emissions, or maximizing system efficiency.Transportation agencies currently use several strategies to support management and operation of the transportation system. Many of the strategies, however, are currently reactive in nature and are not used to actively manage the system. Various strategies could help achieve the ATDM vision and define ATDMs responsibilities and features. ATDM actions can be classified into:1. Active Demand Management (ADM). Strategies focused on managing the trip demand on the network. Dynamic Fare Reduction Dynamic High-Occupancy Vehicle (HOV) / Managed Lanes Dynamic Pricing Dynamic Ridesharing Dynamic Routing Dynamic Transit Capacity Assignment On-Demand Transit Predictive Traveler Information Transfer Connection Protection2. Active Traffic Management (ATM). Strategies focused on managing the flow of vehicle traffic on the network. Adaptive Ramp Metering Adaptive Traffic Signal Control Dynamic Junction Control Dynamic Lane Reversal or Contraflow Lane Reversal Dynamic Lane Use Control Dynamic Merge Control Dynamic Shoulder Lanes Dynamic Speed Limits Queue Warning Transit Signal Priority3. Active Parking Management (APM). Strategies focused on managing the parking requirements of vehicles. Dynamic Overflow Transit Parking Dynamic Parking Reservation Dynamic Wayfinding Dynamically Priced Parking(Source: Federal Highway Administration)2 Methods2.1 Objects of Study2.1.1 I-66 Corridor with Orange and Silver Lines Section in Northern VirginiaThis paper aims to conduct an exploratory study in the Washington, D. C. Metropolitan Area along the I-66 Corridor used by travelers moving from Northern Virginia to Washington, D. C. (see Figure 2)Figure 2 Interstate 66 highway and the Orange and the Silver Lines in Northern Virginia(Source: Google Maps)The I-66 corridor has a number of features that makes it a good candidate for analysis. First, the constant heavy load (the most heavily loaded section is located in Fairfax Country, with the annual average daily trafficAADT among 80,000 to 90,000 in each direction, source: Average Daily Traffic Volumes with Vehicle Classification Data on Interstate, Arterial and Primary Routes, Virginia Department of TransportationVDOT, 2014) of the corridor coupled with the high population density in the surrounding areas allows for access to a rich, diverse set of potential test subjects. Second, the corridor already features a number of ATDM/ITS strategies such as ramp metering, speed harmonization, and peak-hour HOV restrictions. Data has previously been collected on the corridor by VDOT and other agencies, and with the changes that are being made these data collection efforts are ongoing. Similarly, the data of metro ridership is continuously available through Washington Metropolitan Area Transit AuthorityWMATA.The Orange line starts at its western termini at the Vienna/Fairfax-GMU station in Virginia, the tracks run on the median strip of Interstate 66 until they enter a tunnel under Fairfax Drive just before the Ballston-MU station (it was originally proposed to follow I-66 through Arlington, but then argued to be relocated to Fairfax Drive). While the Silver line is planned to link Washington, D.C. by rail to Washington Dulles International Airport and the edge cities of Tysons Corner, Reston, Herndon, and Ashburn. Generally, these two metro lines aim to reduce the overall reliance on highway traffic (specifically, the I-66) between Washington, D. C. and Northern Virginia. According to latest 2015s report, the average weekday passenger boarding of the Orange and Silver lines in Northern Virginia (from Vienna to Rosslyn for the Orange line, from Wiehle-Reston East to Rosslyn for the Silver line) is 81,816, occupying 11.5% of the entire boarding of the metro system (source: WMATA), which is considered to be critical. In this case, the Orange and the Silver lines are expected to coordinate with I-66 and they serve as a network system with alternative transportation modes provided. Thus optimizing the efficiency of such systems and enhancing their reliability is one of the ATDMs concentrations.2.1.2 Trip of pre-trip decision making, alternative travel mode and route availableThe usage of the I-66 corridor, orange and silver line is multi-purpose (e.g., daily commute, recreation, visit), and for that reason this study had focused mainly on AM commuter trip. AM commuter trip is considered to be steady due to peoples traveling to work/school on a regular basis. It is believed that people have made pre-trip decision for departure time, travel mode and route. Therefore, this kind of travel behavior is considered to be largely reasoned, fixing the presumption of the theory of planned behavior.In order to virtually mimicked the AM commute trip and study its determinants, two critical issues should be paid attention to: (1) the number of determinants of the commute this study applies must be reduced to a finite and manageable set that could be accurately analyzed; and (2) in the gaming environment, participants will be put under a virtual condition to make their initial departure time, travel mode and route choice decisions. Then, they will receive new information (probably a change in the conditions that previously provided) from a variety of sources within the game, for example, other users feedback, roadside message signs, release of governmental administration, television or radio news reports. Participants will be provided the opportunity to either rearrange their commute ahead or explore alternative modes and routes along their commute.2.2 Survey DesignIn order to settle the determinates of a commute decision making into a finite number and measurable set, surveys were deliberately designed. These surveys consist of multiple-choice questions and using regression analysis, the relationship between the determinants and decisions could be founded when the sample is large enough. 2.2.1 SamplingThe target population of this survey (i.e., the group of people who are expected to take the survey) is the commuter that travel to work/school from Northern Virginia to Washington, D.C via either the Interstate 66 highway or the Orange/Silver lines of the metro. With the target population defined, simple random sampling is used as a probability sampling strategy. Briefly, if the number of target population is N, then the probability of each individual to be selected equals to 1/N. To decide the sample size, there should make a balance between the accuracy and financing feasibility. Using the simplified formula for a proportion of error: s=z2(p(1-p)/e2, with a confidence level of 95% (i.e., z=1.96) and the default p=0.5, suggests that a sample size of 385 individuals is required for a level of precision (i.e., the proportion of error we are prepared to accept) e=5%. Increasing the level of precision to 3% requires a sample size of 1068 individuals. Similarly, if the confidence level of 99% (i.e., z=2.576) is expected, then a sample size of 664 individuals is needed for e=5% and a sample size of 1844 individuals is needed for e=3%.2.2.2 Question design and Response FormatsAccording to Iarossi (2006), two basic rules form the foundation of a good question: (1) relevance (which is achieved when the survey designer is intimately familiar with the questions, knows exactly the questions objectives and the type of information needed) and (2) accuracy (where a question is accurate if it collects the information sought in a reliable and valid manner). To ensure that questions elicit expected information for the purpose of data collection, such guidelines below should be followed: keeping questions concise and using simple language; avoiding the use of ambiguous phrases and vague terminology; asking questions that evoke concrete answers (not a vague yes or no); avoiding biased or slanted questions; avoiding double-barrel quest