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Artículos de revistas sobre el tema "Advanced traveler information systems"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 28 de enero de 2023

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1

Carrese, Filippo, Stefano Carrese, Sergio Maria Patella, Marco Petrelli y Simone Sportiello. "A Framework for Dynamic Advanced Traveler Information Systems". Future Transportation 1, n.º 3 (1 de noviembre de 2021): 590–600. http://dx.doi.org/10.3390/futuretransp1030031.

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This paper presents the framework for a dynamic Advanced Traveler Information System (ATIS). The ATIS currently in use provides users with stereotyped travel options, but the set of available modes in a given place and time is not the same for each traveler, and such a personal choice set varies within the context of daily trip chains. The research presented in this paper addressed these limitations by including dynamic features in the proposed system. The activity chain that the user performs as well as the personal mode availabilities are modelled simultaneously to define the logical architecture of an innovative information system. Such a technology was intended to assist travelers in performing their daily trip chaining. In order to provide some insight regarding the efficacy of the proposed procedure, a pilot test was performed using real travel time information. Results have shown that the ATIS proposed in this study might generate a significant reduction in travel times.

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2

Balakrishna, Ramachandran, Haris N. Koutsopoulos, Moshe Ben-Akiva, Bruno M. Fernandez Ruiz y Manish Mehta. "Simulation-Based Evaluation of Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1910, n.º 1 (enero de 2005): 90–98. http://dx.doi.org/10.1177/0361198105191000111.

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Traveler information has the potential to reduce travel times and improve their reliability. Studies have verified that driver overreaction from the dissemination of information can be eliminated through prediction-based route guidance that uses short-term forecasts of network state. Critical off-line tests of advanced dynamic traffic assignment–based prediction systems have been limited, since the system being evaluated has also been used as the test bed. This paper outlines a detailed simulation-based laboratory for the objective and independent evaluation of advanced traveler information systems, a laboratory with the flexibility to analyze the impacts of various design parameters and modeling errors on the quality of the generated guidance. MITSIMLab, a system for the evaluation of advanced traffic management systems, is integrated with Dynamic Network Assignment for the Management of Information to Travelers (DynaMIT), a simulation-based decision support system designed to generate prediction-based route guidance. Evaluation criteria and requirements for the closed-loop integration of MITSIMLab and DynaMIT are discussed. Detailed case studies demonstrating the evaluation methodology and sensitivity of DynaMIT's guidance are presented.

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3

Polydoropoulou, Amalia, Moshe Ben-Akiva, Asad Khattak y Geoffrey Lauprete. "Modeling Revealed and Stated En-Route Travel Response to Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1537, n.º 1 (enero de 1996): 38–45. http://dx.doi.org/10.1177/0361198196153700106.

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Advanced traveler information systems (ATISs) are being developed to provide travelers with real-time information about traffic conditions. To evaluate the benefits of ATIS products and services, questions concerning the potential market, use, and travel response must be addressed. The present focus is on the en-route travel response to ATISs. The main objective is to explore how travelers deal with unexpected congestion and how they might respond to qualitative, quantitative, prescriptive, and predictive information. Data on travelers' route-switching decisions were obtained through a survey of California Bay Area automobile commuters. The effects of various factors, such as sources of congestion information (radio traffic reports versus observation), trip characteristics, and route attributes on traveler response to unexpected congestion, were investigated. Future response to ATIS technologies was explored by using stated preferences, that is, hypothetical ATIS scenarios. A combined reported and stated preference model of traveler response was developed. The results indicate that expected delays on the usual route, travel time on alternative routes, perceived congestion level on alternative routes, and information sources are important determinants of travel decision changes in response to unexpected delays. The modeling methodology identifies the effects of experience and behavioral inertia on choices and captures inherent biases in the stated preference responses. Overall, travelers are more likely to respond to specific quantitative delay information.

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4

Hamerslag, Rudi. "Dynamic Traffic Information for Advanced Traveler Information Systems". IFAC Proceedings Volumes 27, n.º 12 (agosto de 1994): 609–14. http://dx.doi.org/10.1016/s1474-6670(17)47538-0.

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5

Khattak, Asad, Amalia Polydoropoulou y Moshe Ben-Akiva. "Modeling Revealed and Stated Pretrip Travel Response to Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1537, n.º 1 (enero de 1996): 46–54. http://dx.doi.org/10.1177/0361198196153700107.

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Advanced traveler information systems (ATISs) offer benefits to travelers and may improve transportation system performance in congested areas. An understanding of how information affects travelers' decisions can help in evaluating benefits and designing demand management strategies. The objective of the present study was to explore how people deal with unexpected congestion during the pretrip stage and how they might respond to ATISs. Travelers' routes, departure times, and mode selection decisions were investigated through a survey of Bay Area automobile commuters. The effects of various factors such as sources of congestion information (radio traffic reports versus observation), trip characteristics, and route attributes on travelers' responses to unexpected congestion were examined. The pretrip response to future ATIS technologies was explored through stated preferences (hypothetical scenarios). A combined reported preference and stated preference model of traveler response was developed by using the multinomial logit formulation. The estimations indicate that travel time and information are important determinants of changes in travel decisions in response to unexpected delays. The model indicates a strong relationship between reported and stated preferences. The results indicate that ATISs overcome behavioral inertia and that individuals are more likely to change their travel patterns in response to prescriptive information. More specific findings and their implications for ATIS design are also discussed.

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6

Thakuriah, Piyushimita (Vonu) y Ashish Sen. "Quality of information given by advanced traveler information systems". Transportation Research Part C: Emerging Technologies 4, n.º 5 (octubre de 1996): 249–66. http://dx.doi.org/10.1016/s0968-090x(97)82900-2.

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7

Perez, William A. y Truman M. Mast. "Human Factors and Advanced Traveler Information Systems (ATIS)". Proceedings of the Human Factors Society Annual Meeting 36, n.º 15 (octubre de 1992): 1073–77. http://dx.doi.org/10.1518/107118192786749694.

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The nation's motoring public is increasingly burdened by recalcitrant transportation problems, many of them directly attributable to increasing traffic congestion. In response to this, the US. Department of transportation is actively moving on several fronts to address this problem. One of the more promising approaches to relieving congestion is through the design and implementation of new technology in the Intelligent Vehicle/Highway System (IVHS). IVHS is composed of five elements: Advanced Traffic Management Systems (ATMS), Advanced Traveler Information Systems (ATIS), Commercial Vehicle Operations (CVO), Advanced Vehicle Control Systems (AVCS), and Advanced Public Transportation Systems (APTS). This paper will discuss human factors issues associated with ATIS.

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8

Jou, Rong-Chang, Ta-Yin Hu y Chien-Wen Lin. "Empirical Results from Taiwan and Their Implications for Advanced Traveler Pretrip Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1607, n.º 1 (enero de 1997): 126–33. http://dx.doi.org/10.3141/1607-17.

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A statistical analysis of travelers’ behavior is presented, and a study into the effects of pretrip information on travelers’ choice behavior is addressed. The study is based on an extensive home interview survey conducted in the Taichung metropolitan area of Taiwan. The main objectives were (a) to determine which types of information are more important to travelers; (b) to examine whether the provision of information alters the travelers’ choice behavior; (c) to relate travelers’ choice behavior, including departure time, route, and mode, to personal and travel behavior characteristics; and (d) to provide a basis for the subsequent development of a pretrip information system architecture. A binary logit model of whether a traveler switches departure time, route, mode, or any combinations of the three or does not switch after receiving traffic information is estimated. The results underscore the important relationship between the different characteristics and the propensity of travelers to change behavior. Separate binary models are developed for each of the individual trip purposes. The focus is on the urban (intraregional) trips.

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9

Wheeler, William A., John D. Lee, Mireille Raby, Rhonda A. Kinghorn, Alvah C. Bittner y Marvin C. McCallum. "Predicting Driver Behavior using Advanced Traveler Information Systems". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, n.º 16 (octubre de 1994): 1057–61. http://dx.doi.org/10.1177/154193129403801614.

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As a part of the Intelligent Vehicle Highway System (IVHS), Advanced Traveler Information Systems (ATIS) will offer tomorrow's drivers significantly expanded capabilities for getting where they want to go safely and efficiently. Vehicle-based navigation systems combined with information on highway conditions and services have the potential for improving driver performance. Though ATIS may offer considerable advantages, the system design must be consistent with the primary tasks of controlling and operating the vehicle. This paper describes an attempt to identify the likely interaction between what a driver must do to operate the vehicle safely while at the same time using the various ATIS systems. As such, it is an attempt to visualize what driving with these advanced systems will be like and to translate that vision into standard human factors task analytic techniques. Though a broad range of ATIS systems and functions were addressed in this project, this paper will address the macro-level task analyses that resulted from the examination of 165 tasks related to ATIS use.

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10

Polydoropoulou, Amalia, Dinesh A. Gopinath y Moshe Ben-Akiva. "Willingness To Pay for Advanced Traveler Information Systems: SmarTraveler Case Study". Transportation Research Record: Journal of the Transportation Research Board 1588, n.º 1 (enero de 1997): 1–9. http://dx.doi.org/10.3141/1588-01.

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The issue of travelers' adoption of an advanced traveler information system (ATIS) and willingness to pay for such information services is addressed. A case study is presented of SmarTraveler, an ATIS that provides, via telephone, real-time location-specific traffic and transit information in the greater Boston area. The model is an integrated system of discrete choice and latent variable models. It predicts travelers' frequency of use and subscription under varying pricing scenarios. Two models are presented: one for current SmarTraveler users, and one for nonusers. The SmarTraveler usage rate is modeled as a function of payment method and pricing, travelers' travel and socioeconomic characteristics, and their attitudes and perceptions toward ATIS. The data used in model estimation included willingness-to-pay scenarios involving two methods of payment: a flat monthly fee, and a charge per call. It was found that for nonusers, the higher the expected benefit from an ATIS, the higher the willingness to pay. This expected benefit is a latent variable indicated via the importance placed by individuals on ATIS attributes such as reliability, relevance, and coverage. For users, the utility of SmarTraveler is affected strongly by the users' level of satisfaction with the service. A modeling framework is developed that captures response biases and presents figures on willingness to pay for an innovative ATIS actually implemented in the market.

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11

Gandy, Ryan y Michael J. Meitner. "Advanced traveler information systems: Bringing environmental information to recreational driving". Landscape and Urban Planning 85, n.º 3-4 (abril de 2008): 185–94. http://dx.doi.org/10.1016/j.landurbplan.2007.11.008.

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12

Koutsopoulos, Haris N. y Haiping Xu. "An information discounting routing strategy for advanced traveler information systems". Transportation Research Part C: Emerging Technologies 1, n.º 3 (septiembre de 1993): 249–64. http://dx.doi.org/10.1016/0968-090x(93)90026-c.

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13

Lu, Jiangang, Xuegang Ban, Zhijun Qiu, Fan Yang y Bin Ran. "Robust Route Guidance Model Based on Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1935, n.º 1 (enero de 2005): 1–7. http://dx.doi.org/10.1177/0361198105193500101.

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In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.

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14

Hawas, Yaser E. "Calibrating Simulation Models for Advanced Traveler Information Systems/Advanced Traffic Management Systems Applications". Journal of Transportation Engineering 128, n.º 1 (enero de 2002): 80–88. http://dx.doi.org/10.1061/(asce)0733-947x(2002)128:1(80).

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15

Kraan, Mariëtte, Hani S. Mahmassani y Nhan Huynh. "Traveler Responses to Advanced Traveler Information Systems for Shopping Trips: Interactive Survey Approach". Transportation Research Record: Journal of the Transportation Research Board 1725, n.º 1 (enero de 2000): 116–23. http://dx.doi.org/10.3141/1725-16.

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This survey research describes an experiment that addresses travel decisions of trip makers for noncommuting trips. The survey considers recreational shopping trips for which trip makers can choose both destination and desired arrival time. An interactive stated preference instrument, the survey was designed and implemented for access and administration via the Internet. It was easily accessible and completely self-administered; participants could run the survey without the help of a project staff member. Data were also gathered automatically. Survey respondents were asked to make two shopping trips. They were provided with pretrip and en route traffic information for trips to shopping malls by predetermined routes. Respondents were asked their preferences concerning information items, factors affecting their mall choice, demographics, and familiarity with their residence and the shopping malls. The recruitment method by e-mail and the requirement to have Internet access led to a slightly biased sample—young males, many with higher education, were overrepresented in the sample. Results showed that respondents choose their shopping mall based on characteristics of the mall but that trip makers divert from their route when provided with delay information during their trip. It was found that about 25 percent of respondents even change their destination. Of the respondents who did not change their destination, nearly half chose to divert from their current route, whereas the other half stayed on the current route. Despite its current sample bias, the interactive Internet survey has proven to be a successful tool for gathering data on travel decisions. It is inexpensive, easy to administer, and provides reliable responses.

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16

Al-Deek, Haitham M., Asad J. Khattak y Paramsothy Thananjeyan. "A combined traveler behavior and system performance model with advanced traveler information systems". Transportation Research Part A: Policy and Practice 32, n.º 7 (septiembre de 1998): 479–93. http://dx.doi.org/10.1016/s0965-8564(98)00010-x.

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17

Bifulco, Gennaro Nicola, Giulio Erberto Cantarella y Fulvio Simonelli. "Design of Signal Setting and Advanced Traveler Information Systems". Journal of Intelligent Transportation Systems 18, n.º 1 (13 de mayo de 2013): 30–40. http://dx.doi.org/10.1080/15472450.2013.802156.

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18

Polydoropoulou, Amalia, Moshe Ben-Akiva y Dinesh Gopinath. "Modeling User Adoption of Advanced Traveler Information Systems (ATIS)". IFAC Proceedings Volumes 30, n.º 8 (junio de 1997): 1357–62. http://dx.doi.org/10.1016/s1474-6670(17)44010-9.

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19

Tarko, Andrzej P. y Nagui M. Rouphail. "Congestion Detection in Large-Scale Advanced Traveler Information Systems". IFAC Proceedings Volumes 28, n.º 10 (julio de 1995): 441–46. http://dx.doi.org/10.1016/s1474-6670(17)51558-x.

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20

Yang, C. Y. David y Jon D. Fricker. "Using Human Information Processing Principles to Design Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1759, n.º 1 (enero de 2001): 1–8. http://dx.doi.org/10.3141/1759-01.

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21

MIZOKAMI, Shoshi y Fumiaki TAKEKUMA. "Assessment for Advanced Traveler Information Systems by Network Equilibrium Analysis". INFRASTRUCTURE PLANNING REVIEW 20 (2003): 931–38. http://dx.doi.org/10.2208/journalip.20.931.

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22

Belz, Nathan P. y Per E. Gårder. "Maine Statewide Deployment and Integration of Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 2129, n.º 1 (enero de 2009): 16–23. http://dx.doi.org/10.3141/2129-03.

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23

Deakin, Ann K. "Potential of Procedural Knowledge to Enhance Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1573, n.º 1 (enero de 1997): 35–43. http://dx.doi.org/10.3141/1573-06.

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The closure of a portion of the Santa Monica Freeway following the 1994 Northridge earthquake afforded the opportunity to study the behavior of motorists as they found their way around the collapsed bridges along alternate and detour routes. In this study, 502 motorists, many of whom were displaced from the damaged Santa Monica Freeway, responded to a mailed questionnaire asking about changes in travel patterns, factors affecting alternate route choice, way-finding strategies used, difficulty in following alternate routes, attitudes toward the neighborhoods containing alternate routes, and attitudinal changes regarding the risk of traveling city freeways. A factor analysis of the responses revealed a procedural knowledge factor, indicating that motorists found landmarks, street signs, and written directions to be helpful aids in following alternate and detour routes. Procedural knowledge, the stored sequence of decisions about how to get from one place to another, is part of the spatial knowledge acquisition process. It is suggested that cues supporting a procedural level of knowledge could be used to enhance Advanced Traveler Information Systems (ATIS). Alternate and detour route information could include more emphasis on landmarks and street signs and be conveyed in the form of written or verbal directions.

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24

Toledo, Tomer y Ross Beinhaker. "Evaluation of the Potential Benefits of Advanced Traveler Information Systems". Journal of Intelligent Transportation Systems 10, n.º 4 (diciembre de 2006): 173–83. http://dx.doi.org/10.1080/15472450600981033.

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25

Lerner, Neil, Richard Huey y Jeff Harpster. "Driver Opinion of Message Requirements for Advanced Traveler Information Systems". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, n.º 18 (septiembre de 1999): 962–66. http://dx.doi.org/10.1177/154193129904301804.

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26

Chen, Chung-Jen, Ming-Chih Chung y Chien-Hung Wei. "Government policy of technology selection for advanced traveler information systems". R and D Management 36, n.º 4 (septiembre de 2006): 439–50. http://dx.doi.org/10.1111/j.1467-9310.2006.00445.x.

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27

Zhang, Jianwei, Feixiong Liao, Theo Arentze y Harry Timmermans. "A multimodal transport network model for advanced traveler information systems". Procedia - Social and Behavioral Sciences 20 (2011): 313–22. http://dx.doi.org/10.1016/j.sbspro.2011.08.037.

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28

Levinson, David. "The value of advanced traveler information systems for route choice". Transportation Research Part C: Emerging Technologies 11, n.º 1 (febrero de 2003): 75–87. http://dx.doi.org/10.1016/s0968-090x(02)00023-2.

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29

Zhang, Jianwei, Feixiong Liao, Theo Arentze y Harry Timmermansa. "A multimodal transport network model for advanced traveler information systems". Procedia Computer Science 5 (2011): 912–19. http://dx.doi.org/10.1016/j.procs.2011.07.127.

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30

Seebauer, Sebastian, Regina Stolz y Martin Berger. "Technophilia as a driver for using advanced traveler information systems". Transportation Research Part C: Emerging Technologies 60 (noviembre de 2015): 498–510. http://dx.doi.org/10.1016/j.trc.2015.10.009.

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31

Sun, Huijun, Jianjun Wu, Wei Wang y Ziyou Gao. "Reliability-based traffic network design with advanced traveler information systems". Information Sciences 287 (diciembre de 2014): 121–30. http://dx.doi.org/10.1016/j.ins.2014.07.036.

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32

Barfield, Woodrow y Fred Mannering. "Behavioral and human factors issues in advanced traveler information systems". Transportation Research Part C: Emerging Technologies 1, n.º 2 (junio de 1993): 105–6. http://dx.doi.org/10.1016/0968-090x(93)90007-3.

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33

Ng, Linda, Woodrow Barfield y Fred Mannering. "A survey-based methodology to determine information requirements for advanced traveler information systems". Transportation Research Part C: Emerging Technologies 3, n.º 2 (abril de 1995): 113–27. http://dx.doi.org/10.1016/0968-090x(95)00001-y.

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34

Antoniou, Constantinos, Amalia Polydoropoulou y Asad J. Khattak. "Advanced Traveler Information Systems: Behavioral Responses to Mobile Applications for Transportation". Journal of Intelligent Transportation Systems 19, n.º 2 (3 de abril de 2015): 107–8. http://dx.doi.org/10.1080/15472450.2015.1012867.

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35

LIN, WEI-HUA, AMIT KULKARNI y PITU MIRCHANDANI. "Short-Term Arterial Travel Time Prediction for Advanced Traveler Information Systems". Journal of Intelligent Transportation Systems 8, n.º 3 (julio de 2004): 143–54. http://dx.doi.org/10.1080/15472450490492833.

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36

YANG, HAI, OI SUET MA y S. C. WONG. "New Observations on the Benefit Evaluation of Advanced Traveler Information Systems". ITS Journal - Intelligent Transportation Systems Journal 5, n.º 3 (enero de 1999): 251–74. http://dx.doi.org/10.1080/10248079908903769.

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37

Ding, Zhongjun, Bokui Chen, Lele Zhang, Rui Jiang, Yao Wu y Jianxun Ding. "Segment travel time route guidance strategy in advanced traveler information systems". Physica A: Statistical Mechanics and its Applications 534 (noviembre de 2019): 120432. http://dx.doi.org/10.1016/j.physa.2019.01.001.

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38

Nygard, K. E. "Computing and modeling issues in wide-area Advanced Traveler Information Systems". Mathematical and Computer Modelling 22, n.º 4-7 (agosto de 1995): 431–37. http://dx.doi.org/10.1016/0895-7177(95)00148-u.

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39

Yang, Zhi Yong y Gui Yun Yan. "Dynamic Model Research of Departure Time and Route Choice Based on Prospect Theory". Advanced Materials Research 243-249 (mayo de 2011): 4418–21. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.4418.

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This paper takes commuters’ daily travel as research object to build model of travel choice which contains departure time and travel route based on Prospect Theory. Choosing the time of arriving destination as reference point, commuter will choose the time at which he/she can obtain the maximum value as departure time, then establishes choice model of departure time. Using Bayesian Theory to update and adjust route’s forecasting travel time in light of traffic information provided by Advanced Traveler Information Systems (ATIS) and travelers’ previous experience information. Gets decision weighting function after having analyzed traveler’s individual subjective probability which is about the possible result for route choice, then obtains the expression of travel route’s prospect value and gets route choice model. Finally, by designing a network to analyze the dynamic choice model, and achieves expected effect.

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40

Lou, Xiaoming, Lin Cheng, Dawei Li, Shibei Zhu y Jing Zhou. "Modeling Day-to-Day Dynamics of Travelers’ Risky Route Choices under the Influence of Predictive Traffic Information". Transportation Research Record: Journal of the Transportation Research Board 2672, n.º 48 (14 de mayo de 2018): 12–23. http://dx.doi.org/10.1177/0361198118773565.

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In stochastic transport networks, travelers tend to reserve a “travel time budget” (TTB) for every alternative route to assure their punctual arrival with a predefined confidence level. This paper incorporates the concept of TTB into a deterministic nonlinear dynamic system to model the day-to-day evolution process of travelers’ risk-taking route choices in stochastic degradable road networks. This paper also introduces consistent traffic information prediction, which can be provided by advanced traveler information systems (ATIS), into the dynamic system to investigate its influence on travelers’ route choice behaviors and the corresponding day-to-day network flow evolutions. In the proposed dynamic model, ATIS-equipped travelers are distinguished from those not so equipped to reflect traveler heterogeneity in access to traffic information. Some theoretical analyses are further conducted to investigate the existence and uniqueness of the fixed point, and the stability of the day-to-day dynamic model. The analysis results show that consistent predictive traffic information can help to stabilize day-to-day network flow fluctuations and thus can enhance fixed-point stability of the dynamic model. In addition, this paper investigates the effects of travelers’ attitudes to risk on the network flow evolution process. An illustrative example is presented to demonstrate the characteristics of the dynamic model as well as to verify the theoretical analysis results.

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41

Raj Mehndiratta, Shomik, Michael A. Kemp, Jane E. Lappin y Eric Nierenberg. "Likely Users of Advanced Traveler Information Systems: Evidence from the Seattle Region". Transportation Research Record: Journal of the Transportation Research Board 1739, n.º 1 (enero de 2000): 15–24. http://dx.doi.org/10.3141/1739-03.

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Findings about the claimed use of and interest in traveler information systems among segments of the general population in the Seattle metropolitan region are reported. The research is based on data collected from a 1997 intelligent transportation system supplement to the Puget Sound Regional Council’s transportation panel travel diary study. In general, interest in traffic information was found to be a function of complex travel behavior, demographics, and factors related to attitudinal and technology interest. It was also found that the potential market for advanced traveler information systems (ATIS) includes several market segments with different needs; each is drawn to ATIS for distinct and varying reasons. First, the so-called control seeker segment includes people who desire greater than average control over their environment. In a traffic information context, that translates to better knowledge of their travel times, especially in congested travel environments. The second segment of interest, dubbed webheads, includes individuals who are very comfortable with high technology, and it is possible that they would be drawn to ATIS at least partially through specific high-technology dissemination media. A third segment is made up of individuals who are less comfortable with (or less interested in) technology and are primarily interested in pretrip information, which they use to make departure time and route choice decisions.

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42

Toppen, Alan, Soojung Jung, Vaishali Shah y Karl Wunderlich. "Toward a Strategy for Cost-Effective Deployment of Advanced Traveler Information Systems". Transportation Research Record: Journal of the Transportation Research Board 1899, n.º 1 (enero de 2004): 27–34. http://dx.doi.org/10.3141/1899-04.

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43

GOULIAS, KONSTADINOS G., TAE-GYU KIM y ONDREJ PRIBYL. "A Longitudinal Analysis of Awareness and Use for Advanced Traveler Information Systems". Journal of Intelligent Transportation Systems 8, n.º 1 (enero de 2004): 3–17. http://dx.doi.org/10.1080/15472450490437717.

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44

Wang, F. Y., P. B. Mirchandani y S. Tang. "Guest Editorial Advanced Traveler Information Systems and Vision-Based Techniques for ITS". IEEE Transactions on Intelligent Transportation Systems 6, n.º 1 (marzo de 2005): 1–4. http://dx.doi.org/10.1109/tits.2005.845574.

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45

Sen, Suvrajeet, Rekha Pillai, Shirish Joshi y Ajay K. Rathi. "A Mean-Variance Model for Route Guidance in Advanced Traveler Information Systems". Transportation Science 35, n.º 1 (febrero de 2001): 37–49. http://dx.doi.org/10.1287/trsc.35.1.37.10141.

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46

Campbell, John L., Cher Carney y Barry H. Kantowitz. "Design Guidelines for Advanced Traveler Information Systems (ATIS): The User Requirements Analysis". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, n.º 2 (octubre de 1997): 954–58. http://dx.doi.org/10.1177/107118139704100252.

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Resumen

Past research has established that human factors guidelines following traditional formats for presenting information are not useful and are often ignored by designers. Within the human factors community, there is considerable uncertainty and concern regarding the nature and utility of human factors handbooks and guidelines materials. Indeed, many existing human factors reference materials have been criticized by designers for being “too wordy”, “too general,” and “too hard to understand.” The development of clear, relevant, and useful human factors guidelines requires a judicious mix of science and art to overcome such criticisms. This paper describes an on going effort to develop clear, relevant, and easy-to-use guidelines for the design of Advanced Traveler Information Systems (ATIS) and Commercial Vehicle Operations (CVO). It focuses on the methods and results of a user requirements analysis conducted to identify the appropriate content, organization, and format of the ATIS/CVO guidelines.

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47

Yang, Hai. "Multiple equilibrium behaviors and advanced traveler information systems with endogenous market penetration". Transportation Research Part B: Methodological 32, n.º 3 (abril de 1998): 205–18. http://dx.doi.org/10.1016/s0191-2615(97)00025-8.

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48

Al-Deek, Haitham y Adib Kanafani. "Modeling the benefits of advanced traveler information systems in corridors with incidents". Transportation Research Part C: Emerging Technologies 1, n.º 4 (diciembre de 1993): 303–24. http://dx.doi.org/10.1016/0968-090x(93)90004-y.

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Schofer, Joseph L., Asad Khattak y Frank S. Koppelman. "Behavioral issues in the design and evaluation of advanced traveler information systems". Transportation Research Part C: Emerging Technologies 1, n.º 2 (junio de 1993): 107–17. http://dx.doi.org/10.1016/0968-090x(93)90008-4.

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50

Adler, Jeffrey L. y Michael G. McNally. "In-laboratory experiments to investigate driver behavior under advanced traveler information systems". Transportation Research Part C: Emerging Technologies 2, n.º 3 (septiembre de 1994): 149–64. http://dx.doi.org/10.1016/0968-090x(94)90006-x.

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