Journal articles on the topic 'Advanced Travel Information System'
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Carrese, Filippo, Stefano Carrese, Sergio Maria Patella, Marco Petrelli, and Simone Sportiello. "A Framework for Dynamic Advanced Traveler Information Systems." Future Transportation 1, no. 3 (November 1, 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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Brown, Steven. "ROMANSE Public Transport Information Systems." Journal of Navigation 50, no. 1 (January 1997): 14–26. http://dx.doi.org/10.1017/s0373463300023547.
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ROMANSE — Road Management System for Europe — is multi-million pound pilot project based in Southampton. It aims to use Advanced Transport Telematics (ATT) to develop the city as a model for transport management systems across Europe. ROMANSE achieves this by providing realtime traffic and travel information to influence travel behaviour, increase the use of public transport, maximize the efficiency of the transport system and provide high-quality information for use in strategic policy decisions.
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Khan, Ata M. "Bayesian predictive travel time methodology for advanced traveller information system." Journal of Advanced Transportation 46, no. 1 (October 4, 2010): 67–79. http://dx.doi.org/10.1002/atr.147.
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Lu, Jiangang, Xuegang Ban, Zhijun Qiu, Fan Yang, and Bin Ran. "Robust Route Guidance Model Based on Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 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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Glina, Elyse Marie, and Byeong-Ho Kang. "Conversation System with State Information." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (September 20, 2010): 741–45. http://dx.doi.org/10.20965/jaciii.2010.p0741.
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Most current approaches to conversation system development invoke a complex set of language parsing rules or development tools difficult for novices to handle and are unable to convincingly simulate advanced natural language features such as topic awareness or conversation thread involvement. This study proposes an alternate approach based on the Ripple Down Rules (RDR) algorithm, presently used to enable more effective maintenance of expert systems. This tree-based algorithm enables a conversation system to travel incrementally deeper into a particular topic, then to switch based on context-dependent information to the correct previously discussed topic – resulting in a highly reusable method of developing conversation systems based around a variety of topics.
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Jayan, Akhilesh, and Sasidharan Premakumari Anusha. "Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors." Periodica Polytechnica Transportation Engineering 48, no. 3 (December 16, 2019): 276–89. http://dx.doi.org/10.3311/pptr.13779.
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Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.
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Lerner, Neil, Richard Huey, Paul Zador, Jeff Harpster, and Doug Duncan. "User Preferences for Information Types in Advanced Traveler Information System Applications." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 17 (October 1998): 1200–1204. http://dx.doi.org/10.1177/154193129804201705.
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This experiment addressed the types of ATIS information travelers prefer to acquire, and how the information influenced route choice. Hypothetical trip scenarios provided information such as primary and alternative routes, time of day, current weather, trip purpose, etc. The participant had a set of from 9 to 16 sealed envelopes for each scenario, labeled as to the type of information inside. The participant opened as many envelopes as desired, in any order, to reach a decision as to what route to take. Participants typically only acquired three items of information, even though there were no constraints on information acquisition. “Incident location” was the most frequently selected, and most often opened first, but “delay” was most often cited as the most important item. Estimated travel times on the primary or alternate route were sought by some participants. Cluster analysis revealed three primary groups of individuals in terms of common information strategies. The findings are interpreted in terms of implications for the design of ATIS messages.
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Song, Bing Yue, Jian Ping Wu, and Yi Man Du. "Study of Travel Time Prediction by Using Online ATIS and Probe Vehicle in Beijing." Applied Mechanics and Materials 253-255 (December 2012): 1627–30. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1627.
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With the rapid development of urban traffic, travel time had become the crucial factor for road-user to make decisions on means of transportation, path finding. Besides, emerging information technology, such as Traffic information technology, GIS (Geographic Information System) technology and etc, that have promoted the tendency that people are inclining to plan personal travel through Dynamic Route Guidance System characterized as travel time prediction according to real-time traffic data. In this paper, it first proposed the factors of selecting the test O-D (original-destination), which travel time will be predicted by GoogleMap, BaiduMap, SogouMap and SOSOMap, for comparison, probe vehicle would run along the route provided by those online ATIS (advanced traveler information system) to check the accuracy of travel time predicted. Finally, it analyzes and assesses the service of travel time prediction of such systems.
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Raj Mehndiratta, Shomik, Michael A. Kemp, Jane E. Lappin, and Eric Nierenberg. "Likely Users of Advanced Traveler Information Systems: Evidence from the Seattle Region." Transportation Research Record: Journal of the Transportation Research Board 1739, no. 1 (January 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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Balakrishna, Ramachandran, Haris N. Koutsopoulos, Moshe Ben-Akiva, Bruno M. Fernandez Ruiz, and Manish Mehta. "Simulation-Based Evaluation of Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1910, no. 1 (January 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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Guo, Xiaoyu, Yongxin Peng, Sruthi Ashraf, and Mark W. Burris. "Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 20, 2020): 120–33. http://dx.doi.org/10.1177/0361198120940311.
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Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.
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Choi, Keechoo. "Data fusion methodology for link travel time estimation for advanced traveler information system." KSCE Journal of Civil Engineering 3, no. 1 (March 1999): 1–14. http://dx.doi.org/10.1007/bf02830731.
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Shah, Vaishali P., Karl Wunderlich, Alan Toppen, and James Larkin. "Potential of Advanced Traveler Information System to Reduce Travel Disutility: Assessment in Washington, D.C., Region." Transportation Research Record: Journal of the Transportation Research Board 1826, no. 1 (January 2003): 7–15. http://dx.doi.org/10.3141/1826-02.
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A study quantified the potential benefit of a pretrip advanced traveler information system (ATIS) to travelers in the Washington, D.C., region for the commuter objective of consistently on-time arrival. Paired driver commutes were simulated with a 15-month archive of regional travel times from Smar Traveler.com, an Internet provider of real-time data on travel time. One driver used a personalized pretrip notification service in selecting departure time and route, while the paired counterpart maintained a habitual trip departure time and route. The pairs’ trip arrivals were compared to quantify the effects on time management of a prospective notification service prescribing departure time and route. Approximately 25.9 million paired driver trials were modeled across a network of 55 nodes and 150 links, traversing 711.8 mi of roads in the Washington, D.C., metropolitan region. Trips were simulated for arrivals at 15-min intervals between 6:30 a.m. and 6:30 p.m. on workdays from each node to every other node. Overall, regular use of the pretrip ATIS is shown to benefit commuters whose objective is arriving on time. At low levels of ATIS market penetration, routine ATIS use could reduce early arrivals by 56% and late arrivals by 52%. Benefit in vehicle travel time is minimal. By applying a utility function to each trip, it was calculated that ATIS service reduces aggregate travel disutility by 15%. Approximately 67% of trips (origin–destination) derive a net benefit from routine ATIS use, while 40% benefit by more than $60/year. Benefits are fairly distributed across the year and are generally greatest during the evening peak.
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ISHIDA, Haruo, Hiroshi MIURA, Naohisa OKAMOTO, and Hideki FURUYA. "Sampling Rates for Travel Speed Survey with Advanced Information Systems." INFRASTRUCTURE PLANNING REVIEW 18 (2001): 81–88. http://dx.doi.org/10.2208/journalip.18.81.
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Saricks, Christopher L., Joseph L. Schofer, Siim Sööt, and Paul A. Belella. "Evaluating Effectiveness of Real-Time Advanced Traveler Information Systems Using a Small Test Vehicle Fleet." Transportation Research Record: Journal of the Transportation Research Board 1588, no. 1 (January 1997): 41–48. http://dx.doi.org/10.3141/1588-06.
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ADVANCE was an in-vehicle advanced traveler information system (ATIS) providing route guidance in real time that operated in the northwestern portion and northwest suburbs of Chicago, Illinois. It used probe vehicles to generate dynamically travel time information about expressways, arterials, and local streets. Tests to evaluate the subsystems of ADVANCE, executed with limited availability of test vehicles and stringent scheduling, are described; they provided useful insights into both the performance of the ADVANCE system as a whole and the desirable and effective characteristics of ATIS deployments generally. Tests found that the user features of an in-route guidance system must be able to accommodate a broad range of technological sophistication and network knowledge among the population likely to become regular users of such a system. For users who know the local network configuration, only a system giving reliable real-time data about nonrecurrent congestion is likely to find a market base beyond specialized applications. In general, the quality and usefulness of systemwide real-time route guidance provided by other means are enhanced significantly by even a small deployment of probes: probe data greatly improve static (archival average) link travel time estimates by time of day, although the guidance algorithms that use these data should also include arterial traffic signal timings. Moreover, probe- and detector-based incident detection on arterial networks shows considerable promise for improved performance and reliability.
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Khattak, Asad, Amalia Polydoropoulou, and 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, no. 1 (January 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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Turner, Shawn M. "Advanced Techniques for Travel Time Data Collection." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (January 1996): 51–58. http://dx.doi.org/10.1177/0361198196155100107.
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Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.
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LIN, WEI-HUA, AMIT KULKARNI, and PITU MIRCHANDANI. "Short-Term Arterial Travel Time Prediction for Advanced Traveler Information Systems." Journal of Intelligent Transportation Systems 8, no. 3 (July 2004): 143–54. http://dx.doi.org/10.1080/15472450490492833.
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Ding, Zhongjun, Bokui Chen, Lele Zhang, Rui Jiang, Yao Wu, and Jianxun Ding. "Segment travel time route guidance strategy in advanced traveler information systems." Physica A: Statistical Mechanics and its Applications 534 (November 2019): 120432. http://dx.doi.org/10.1016/j.physa.2019.01.001.
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Marchal, Fabrice, and André de Palma. "Measurement of Uncertainty Costs with Dynamic Traffic Simulations." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 67–75. http://dx.doi.org/10.3141/2085-08.
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Nonrecurrent congestion in transportation networks occurs as a consequence of stochastic factors affecting demand and supply. Intelligent transportation systems such as advanced traveler information systems and advanced traffic management systems are designed to reduce the impacts of nonrecurrent congestion by providing information to a fraction of users or by controlling the variability of traffic flows. For these reasons, the design of these systems requires a reliable forecast of nonrecurrent congestion. A new method is proposed to measure the impacts of nonrecurrent congestion on travel costs by taking risk aversion into account. The traffic model is based on the dynamic traffic simulation model METROPOLIS. Incidents are generated randomly by reducing the capacity of the network. Users can instantaneously adapt to the unexpected travel conditions or can also change their behavior through a day-to-day adjustment process. Comparisons with incident-free simulations provide a benchmark for potential travel time savings that can be brought about by a state-of-the-art information system. The impact of variable travel conditions is measured by describing the willingness to pay to avoid risky or unreliable journeys. Indeed, for risk-averse drivers, any uncertainty corresponds to a utility loss. This utility loss is computed for several levels of network disruption. The main result of the study is that the utility loss due to uncertainty is of the same order of magnitude as the total travel costs.
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Marasigan, Johann Carlo, Gian Paolo Mayuga, and Elmer Magsino. "Development and testing of braking and acceleration features for vehicle advanced driver assistance system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 2047. http://dx.doi.org/10.11591/ijece.v12i2.pp2047-2057.
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<span lang="EN-US">Traffic congestion is a constant problem for cities worldwide. The human driving inefficiency and poor urban planning and development contribute to traffic buildup and travel discomfort. An example of human inefficiency is the phantom traffic jam, which is caused by unnecessary braking, causing traffic to slow down, and eventually coming to a stop. In this study, a brake and acceleration feature (BAF) for the advanced driver assistance system (ADAS) is proposed to mitigate the effects of the phantom traffic phenomenon. In its initial stage, the BAF provides a heads-up display that gives information on how much braking and acceleration input is needed to maintain smooth driving conditions, i.e., without sudden acceleration or deceleration, while observing a safe distance from the vehicle in front. BAF employs a fuzzy logic controller that takes distance information from a light detection and ranging (LIDAR) sensor and the vehicle’s instantaneous speed from the engine control unit (ECU). It then calculates the corresponding percentage value of needed acceleration and braking in order to maintain travel objectives of smooth and safe-distance travel. Empirical results show that the system suggests acceleration and braking values slightly higher than the driver’s actual inputs and can achieve 90% accuracy overall.</span>
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Chen, Jie, and Maobin Hu. "Reduce energy consumption with an advanced eco-routing guidance strategy." E3S Web of Conferences 260 (2021): 01008. http://dx.doi.org/10.1051/e3sconf/202126001008.
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Route guidance is an efficient method to improve the capacity and efficiency of traffic system. This paper proposes an advanced ecorouting guidance strategy based on the real-time feedback information of both fuel consumption and travel time under the background of intelligent transportation system. Compared with the geographical shortest path strategy (GSP) and the time shortest path strategy (TSP), simulation experiment results show that the new strategy can help save fuel consumption and traffic cost by less detours. The distribution of travel cost among the roads is also provided, which is consistent with analysis. The implementation of the strategy in real cities might greatly reduce the energy consumption for transportation.
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Polydoropoulou, Amalia, Moshe Ben-Akiva, Asad Khattak, and 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, no. 1 (January 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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KOV, Phyrum, Yasuhiro HIROBATA, and Yasuyuki NAKAHIRA. "TRAVEL TIME RELIABILITY ANALYSIS IN MIXED TRAFFIC NETWORK UNDER PROVISION OF ADVANCED TRAVELER INFORMATION SYSTEM." INFRASTRUCTURE PLANNING REVIEW 23 (2006): 789–96. http://dx.doi.org/10.2208/journalip.23.789.
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Lian, Jiangong, and Dan Liang. "Design and Application of Multiattribute Tourist Information Recommendation Model Based on User Interest." Wireless Communications and Mobile Computing 2022 (July 22, 2022): 1–16. http://dx.doi.org/10.1155/2022/1805128.
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There exist various challenges introduced by a large number of multimedia photos and videos for personalized travel recommendation in the era of big data. In order to resolve such challenges, a context-aware personalized travel recommendation system based on data mining is proposed in this study. It is a framework that can locate and summarize travel locations based on a user-given collection of geotagged photos and build up each user’s travel history to obtain their travel preferences, so as to perform contextual multiattribute personalized queries, thereby recommending travel locations that best suit their interests. The primary objective is to lay the foundation for developing personalized travel solutions and help the transformation and upgrading of the tourism industry. Thus, this paper proposes a design and application of a multiattribute travel information recommendation model based on user interests for the contradiction between the personalized travel demand of tourists and traditional travel methods. It analyzes the designed travel transportation network and builds a prototype system for travel recommendation by mining a large number of scenic spot information datasets. In association to this, an advanced recommendation algorithm is also designed. The experimental results reveal the fact that by integrating various attributes, the comprehensive evaluation mechanism of scenic spots is capable of providing enhanced reasonable and comprehensive evaluation of scenic spots, which lays the foundation for subsequent route recommendation. Secondly, in comparison to the existing path recommendation algorithms, the recommendation algorithm proposed in this paper has the potential to meet various constraints and goals of the users and recommend routes that have better reasonableness and diversity. Also, this algorithm has low complexity in terms of running time which acts as an added advantage.
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M, Lakshmipathy, Santosh B. Panjagal, and S. Manjula Kumar. "Advanced Display System for Public Bus Transportation." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 1 (May 30, 2013): 302–10. http://dx.doi.org/10.24297/ijct.v6i1.4453.
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The bus arrival time at bus stops in urban traffic environment is highly unpredictable. This is due to random fluctuations in travel demands and interruptions caused by traffic system, incidents, and weather conditions. Providing real-time bus arrival information would enhance the credibility of the public transit system and thus render it more competitive among various other transportation modes. With the emergence of Global Positioning System (GPS) technologies, traffic data collection can be performed more efficiently.In this paper we are implementing The “ADVANCED DISPLAY SYSTEM†to enhance the public transportation system by giving the prior information of the buses arriving towards the bus stop to the people waiting for the bus. It consists of two modules. The first one is In-bus module and second one is Bus stop module. The In-bus module integrates the GPS receiver, GSM Modem, Microcontroller and control switches. This module is mounted to the bus. The Bus stop module is integrated with GSM modem, Microcontroller and LED matrix display. This Bus-Stop module is mounted at the Bus stops. the microcontroller processes the SMS received by the GSM modem and displays it in the LED matrix display. In the LED matrix display the bus number, source, destination station names of the bus, current location name of the bus, the time at which the bus is at the current location, actual time will be displayed.
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Zietsman, Josias, and Laurence R. Rilett. "Aggregate- and Disaggregate-Based Travel Time Estimations: Comparison of Applications to Sustainability Analysis and Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1725, no. 1 (January 2000): 86–94. http://dx.doi.org/10.3141/1725-12.
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Travel time estimation is important for a wide range of applications, including advanced traveler information systems (ATIS), sustainability analysis, and discrete choice modeling. Approaches to travel time estimation traditionally have been based on aggregate data sets that examine travel times over a number of days or travel times in previous time intervals. Automatic vehicle identification data make it possible to analyze travel time data at a totally disaggregate or individual commuter level. It is postulated in this research that the capability of modeling travel characteristics on a disaggregate level can improve the accuracy with which performance measures are quantified. The test beds examined are a 22-km section of the I-10 corridor and a 21-km section of the US-290 corridor in Houston, Texas. It was found that aggregation across days, which does not consider the effect of individual days, is 63 percent less accurate than aggregation by days, which does consider the effect of individual days. Even though the latter technique was found to be more accurate, it was illustrated that 40 percent of the regular commuters’ travel times are statistically different from these aggregate estimates. Similarly, for travel time variability, it was found that for approximately 20 percent of the cases the travel time standard deviations for regular commuters are statistically different from the aggregate estimates. These results illustrate the uniqueness of an individual commuter’s travel patterns and emphasize the benefit of conducting analyses at the level of the individual commuter for both ATIS and sustainable transportation.
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Claes, Rutger, and Tom Holvoet. "Weighing communication overhead against travel time reduction in advanced traffic information systems." Progress in Artificial Intelligence 1, no. 2 (May 15, 2012): 165–72. http://dx.doi.org/10.1007/s13748-012-0014-x.
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Wang, Jianqiang, and Shiwei Li. "Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 03 (September 2017): 1750031. http://dx.doi.org/10.1142/s1793962317500313.
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Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.
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Hobeika, Antoine, R. Sivanandan, Karen M. Jehanian, and Mary D. Ameen. "Advanced Traveler Information System Users' Needs in I-95 Northeast Corridor." Transportation Research Record: Journal of the Transportation Research Board 1537, no. 1 (January 1996): 55–62. http://dx.doi.org/10.1177/0361198196153700108.
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The I-95 Corridor Coalition, comprising 28 transportation agencies and state and local departments of transportation and supported by a number of federal and quasipublic organizations, has embarked on an aggressive program to study and implement intelligent transportation system services along this priority corridor from Maine to Virginia. A key element to the success of this effort is that the travel information needs and desires of the I-95 travelers be satisfied. A comprehensive multimodal study was initiated by the I-95 Corridor Coalition to assess the advanced traveler information system needs of I-95 corridor users and to determine the market potential for such information. The stakeholders include user groups, providers, and producers. The target groups include local and long-distance (business and recreation) automobile travelers, transit users, intercity air travelers, and intercity rail travelers. The provider groups include state, regional, and local transportation agencies, whereas the producer groups include telecommunications companies, automobile manufacturers, and traveler information service providers. Several survey mechanisms were used to assess the user and provider information needs and will be used to assess the market potential. These include focus groups, telephone surveys, on-site surveys, and individual interviews. Thus far in the study users have ranked roadway safety, personal security, and traffic information high in importance. Users have also indicated a willingness to pay for multimodal traveler information services both for their pretrip planning and while en route.
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Du, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, and Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion." Journal of Advanced Transportation 2021 (July 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.
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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
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Rouhieh, Behzad, and Ciprian Alecsandru. "Adaptive route choice model for public transit systems: an application of Markov decision processes." Canadian Journal of Civil Engineering 39, no. 8 (August 2012): 915–24. http://dx.doi.org/10.1139/l2012-080.
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Over the past couple of decades the advancements in the areas of information and computational technology allowed for a variety of intelligent transportation systems developments and deployments. This study investigates an advanced traveler information system (ATIS) and (or) an advanced public transit system (APTS) adaptive and real-time transit routing component. The proposed methodology is applied to bus routes with fixed, predefined bus line alignments. It is shown that routing buses on such systems can be modeled in real-time by employing an associated Markov chain with reward model to minimize the impact of congested traffic conditions on the travelers and the overall operation cost of the transit system. A case study using a traffic and transit data from a real-world bus line was used to apply the proposed bus routing approach. It was found that under certain traffic congestion conditions buses should be re-routed to minimize their travel time and the associated system costs. The hypothetical congestion scenarios investigated show that individual bus travel time delays range between 50 and 740 s when the proposed adaptive routing is employed. The proposed methodology is also suitable for application to transit systems that run on a demand-adaptive basis (the bus line alignment changes with the travelers demand). Additional calibration and future integration of the system into specific ATIS and (or) APTS user services will be investigated.
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Mori, Usue, Alexander Mendiburu, Maite Álvarez, and Jose A. Lozano. "A review of travel time estimation and forecasting for Advanced Traveller Information Systems." Transportmetrica A: Transport Science 11, no. 2 (July 16, 2014): 119–57. http://dx.doi.org/10.1080/23249935.2014.932469.
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Yang, Hang, Yajie Zou, Zhongyu Wang, and Bing Wu. "A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain." Canadian Journal of Civil Engineering 45, no. 2 (February 2018): 77–86. http://dx.doi.org/10.1139/cjce-2017-0231.
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Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.
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Runyoro, Angela-Aida K., Irina Zlotnikova, and Jesuk Ko. "Towards automated road information framework a case study of Tanzania." Transport and Telecommunication Journal 15, no. 1 (March 1, 2014): 12–19. http://dx.doi.org/10.2478/ttj-2014-0002.
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Abstract Traffic congestion problem has been noticed to have a serious impact on the economy of the country in terms of time wastage, energy consumption costs, human loss and environmental effects. Different strategies have been used so far all over the world as shown in the literature review. Intelligent Transportation System (ITS) is a multi-technology approach that can help to handle the issues and create a complete congestion reduction framework. This paper presents a case study for implementing automated road management system using networks in Tanzania, where three cities highly affected by traffic congestion have been studied. Study results show that these cities have not yet implemented new technologies in road traffic management; instead the traffic is controlled using traffic police officers and traffic lights only. The traffic lights use an old technology that cannot manage traffic in relation to the real-time situations. This study proposes components for a framework, which will assist automation in road traffic management. From the review of various existing ITS of which Advanced Traffic Management System (ATMS) and Advanced Travel Information System (ATIS) are subsystems, we have identified the possibility to integrate the two sub-systems within the framework. Three-phase traffic theory has been referred, FOTO and ASDA models are applied to the automatic recognition and tracking of congested spatiotemporal traffic patterns on roads.
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Polydoropoulou, Amalia, Dinesh A. Gopinath, and Moshe Ben-Akiva. "Willingness To Pay for Advanced Traveler Information Systems: SmarTraveler Case Study." Transportation Research Record: Journal of the Transportation Research Board 1588, no. 1 (January 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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Fox, Jean E., and Deborah A. Boehm-Davis. "Effects of Age and Congestion Information Accuracy of Advanced Traveler Information Systems on User Trust and Compliance." Transportation Research Record: Journal of the Transportation Research Board 1621, no. 1 (January 1998): 43–49. http://dx.doi.org/10.3141/1621-06.
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Advanced traveler information systems (ATIS) are computer-based in-vehicle systems that provide travel-related information to automobile drivers. It is important for user acceptance to occur quickly because ATIS performance is optimized when many automobiles are equipped with the systems. One important factor in user acceptance is how much users trust the technology. Previous studies on this topic revealed that inaccurate congestion information reduced the users’ trust in ATIS and limited compliance with ATIS advice. The previous research is extended by the evaluation of both trust in ATIS and compliance with ATIS advice in a high-fidelity driving simulator. Participants drove through a simulation with the goal of avoiding congestion. A simulated ATIS recommended which way to go to avoid congestion. Participants drove through four trials of ten intersections each. In one trial, ATIS provided accurate information at 100 percent of the intersections. In the other three, the information was 80, 60, or 40 percent accurate. The four trials were presented to the participants in one of three orders. Dependent measures included ( a) how much participants trusted ATIS advice, ( b) how many times they complied with ATIS, and ( c) how accurate they thought the system was. The results show that 40 percent accuracy would not support user trust and compliance, but that 60 percent accuracy probably would. Also, the order in which participants experienced the trials significantly affected both trust and compliance; the effects of accuracy order were similar for both trust and compliance.
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Yang, Zhi Yong, and Gui Yun Yan. "Dynamic Model Research of Departure Time and Route Choice Based on Prospect Theory." Advanced Materials Research 243-249 (May 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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Aultman-Hall, Lisa, Sarah Bowling, and Jill Clemons Asher. "ARTIMIS Telephone Travel Information Service: Current Use Patterns and User Satisfaction." Transportation Research Record: Journal of the Transportation Research Board 1739, no. 1 (January 2000): 9–14. http://dx.doi.org/10.3141/1739-02.
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In February and March 1999, an ARTIMIS (advanced regional traffic interactive management and information system) traffic advisory telephone service (TATS) user satisfaction survey was conducted. Evaluation of the Greater Cincinnati and northern Kentucky service had the following objectives: (a) to assess overall satisfaction; (b) to determine the preference of access with an N11 dialing code; and (c) to determine the influence on travel behavior. A total of 65 percent of users indicated they would be willing to pay for the service. On average, the maximum amount people would pay per call is $0.25. Users were asked what portion of the time the traffic information affected their travel behavior: route taken (average, 71 percent of the time), departure time (average, 34 percent), and destination (average, 25 percent). The information rarely affected mode used; however, users indicated that, on average, they postponed their trips 14 percent of the time. About 80 percent of the users believed they would personally benefit from a nationwide N11 dialing code. The study results suggest that a nationwide three-digit access code for traffic information would be a benefit to overall system performance. According to the analysis, the respondents indicated they make an average of 19 trips per year to other areas where they could use a service like ARTIMIS TATS. Users change their travel behavior as a result of the traffic information. They were very satisfied with the service. There was a preference, and good recall, for the 211 number currently provided by the Kentucky Transportation Cabinet and the Ohio Department of Transportation in northern Kentucky and Cincinnati, which replaced a seven-digit number.
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Deakin, Ann K. "Potential of Procedural Knowledge to Enhance Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1573, no. 1 (January 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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Fan, Qi, Wei Wang, Xiaojian Hu, Xuedong Hua, and Zhuyun Liu. "Space-Time Hybrid Model for Short-Time Travel Speed Prediction." Discrete Dynamics in Nature and Society 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7696592.
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Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction.
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Liu, Shixu, Lidan Guo, Said M. Easa, Hao Yan, Heng Wei, and Yingnuo Tang. "Experimental Study of Day-to-Day Route-Choice Behavior: Evaluating the Effect of ATIS Market Penetration." Journal of Advanced Transportation 2020 (July 11, 2020): 1–21. http://dx.doi.org/10.1155/2020/8393724.
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This paper examines the travelers’ day-to-day route-choice behavior with Advanced Traveler Information Systems (ATIS) through laboratory-like experimental method. Five groups of route-choice behavior experiments are designed to simulate actual daily behavior of travelers. In the experiment, subjects are provided with different levels of the complete road network information to simulate the proportion of subjects equipped with ATIS equipment (i.e., ATIS market penetration) and choose the routes repeatedly. The subject’s route-choice behavior under different proportions of complete road network information is analyzed, and the strategy of releasing such complete information is determined when the performance of road network system is the best. The Braess network which consists of three routes was used in the experiment and analysis. The results show that the fluctuation of traffic flow runs through the entire experiments, but it tends to converge to user equilibrium. When the market penetration is 75%, both the fluctuation of traffic flow and the tendency of subjects to change routes are the smallest, so the road network system is the most stable. This interesting result indicates that releasing traffic information to all travelers is not the best. Other results show that the travel times of the three routes in the five groups of experiments tend to converge to and finally fluctuate around user-equilibrium travel time. With the increase in ATIS market penetration, the average travel time of subjects in each round tends to increase. The overall trend of the five groups of experiments is that as the number of route switches increases, the average travel time increases. The results also indicate that releasing traffic information to all travelers cannot weaken the Braess Paradox. On the contrary, the more travelers are provided with traffic information, the less likely it will weaken the Braess Paradox.
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Kraan, Mariëtte, Hani S. Mahmassani, and 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, no. 1 (January 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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Kwon, Jaimyoung, Benjamin Coifman, and Peter Bickel. "Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data." Transportation Research Record: Journal of the Transportation Research Board 1717, no. 1 (January 2000): 120–29. http://dx.doi.org/10.3141/1717-15.
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An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.
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SZETO, W. Y., and HONG K. LO. "The Impact of Advanced Traveler Information Services on Travel Time and Schedule Delay Costs." Journal of Intelligent Transportation Systems 9, no. 1 (March 9, 2005): 47–55. http://dx.doi.org/10.1080/15472450590916840.
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Chen, Wan-Hui, and Paul P. Jovanis. "Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment." Transportation Research Record: Journal of the Transportation Research Board 1843, no. 1 (January 2003): 81–88. http://dx.doi.org/10.3141/1843-10.
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If real-time driver en route guidance advice does not meet driver preferences (e.g., preference for taking the freeway) or the advice is not correct, drivers are very likely to ignore the information, and the guidance system becomes ineffective in their route choice no matter how advanced the system. There is a need to investigate the factors affecting driver compliance with en route guidance advice. A travel simulation experiment was used to investigate significant factors affecting driver route choice behavior. A linear mixed model was developed for describing the factors affecting driver compliance with guidance advice using the compliance rate over several simulated trips as a dependent variable. The issue of repeated observations is addressed. The system accuracy and subjects’ learning experience in their spatial experience at the same intersection and temporal experience in the same day are also taken into account. The model results show that significant factors are involved: freeway advice, turning advice, congestion occurrence, incident occurrence, subjects’ spatial experience, subjects’ temporal experience, and subjects’ education level; there are several important interactions as well.
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COMI, Antonio, Berta BUTTARAZZI, Massimiliano SCHIRALDI, Rosy INNARELLA, Martina VARISCO, and Paolo TRAINI. "An advanced planner for urban freight delivering." Archives of Transport 4, no. 48 (December 31, 2018): 27–40. http://dx.doi.org/10.5604/01.3001.0012.8363.
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The paper aims at introducing an advanced delivery tour planner to support operators in urban delivery operations through a combined approach which chooses delivery bays and delivery time windows while optimizing the delivery routes. After a literature review on tools for the management and the control of the delivery system implemented for optimizing the usage of on-street delivery bays, a prototypical tour delivery planner is described. The tool allows transport and logistics operators to book the delivery bays and to have real-time suggestions on the delivery tour to follow, through the minimization of the total delivery time. Currently, at development phase, the tool has been tested in a target zone, considering the road network and time/city delivering constraints and real-time data about vehicles location, traffic and delivery bay availability. The tool identifies the possible tours based on the delivery preferences, ranks the possible solutions according to the total route time based on information on the road network (i.e. travel time forecasts), performs a further optimization to reduce the total travel times and presents the user the best alternative along with the indications of which delivery bay to use in each delivery stop. The developed prototype is composed by two main parts: a web application that manages communication between the database and the road network simulation, and, an Android mobile App that supports transport and logistic operators in managing their delivering, pre trip and en route, showing and updating routing based on real-time information.
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Jou, Rong-Chang, Ta-Yin Hu, and 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, no. 1 (January 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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Darmawan, Viery, Rengga Asmara, and Ira Prasetyaningrum. "IMPLEMENTASI CONTEXT AWARE PADA SISTEM TRAVEL PLANNER BERBASIS MOBILE." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 5, no. 1 (April 30, 2021): 23–27. http://dx.doi.org/10.46880/jmika.vol5no1.pp23-27.
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In the era of technological advances, tourists will first seek information about the tourist object to be addressed, even tourists often don't have a destination, so they have to search one by one via the internet. In determining travel plans, it is often to see one by one the review of tourist attractions and conclude the results will take a long time, while tourists need actual and fast information to determine the travel plans. In this study, the authors take a new approach, namely by creating a mobile-based travel planner system that compiles travel plans automatically by considering contextual information related to tourist location points, whether of tourist locations during travel days, travel opening and closing hours, so that it will increase travel efficiency without having to do the research manually which takes a long time. The system can also provide travel recommendations based on visitor comments sentiment on Google Places and is equipped with a trip route that will be generated automatically. This research is useful for helping tourists plan their trip actually because of the consideration of contextual information so that it will make it easier and save tourists time.
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Masri, Ni Wayan, Jun-Jer You, Athapol Ruangkanjanases, Shih-Chih Chen, and Chia-I. Pan. "Assessing the Effects of Information System Quality and Relationship Quality on Continuance Intention in E-Tourism." International Journal of Environmental Research and Public Health 17, no. 1 (December 25, 2019): 174. http://dx.doi.org/10.3390/ijerph17010174.
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The advance of electronic commerce has resulted in successful e-travel services. Through the development of e-travel information, consumers can plan their trip without time and space limitations. This study proposes a model regarding the formation of the relationship quality (customer satisfaction and trust), information system quality, perceived value, and customers’ intention to continue in the e-tourism environment. The study is based on 351 e-travel users in Taiwan. The result shows that customer satisfaction has a positive effect on continuance intention. Information system quality has a positive relationship with customer satisfaction, trust, and customer continuance intention. Furthermore, the perceived value has an effect on customer satisfaction and trust. However, the perceived value is partially related to customer continuance intention through customer satisfaction. The managerial implications of this study are discussed.