Top PDF The impact of travel information's accuracy on route choice

The impact of travel information's accuracy on route choice

The impact of travel information's accuracy on route choice

feedback information about the chosen alternative. They demonstrated that informed respondents were more risk averse and preferred a more reliable route compared to the control group that could only learn through past experience. Their result illustrates indirectly how the effect of information accuracy might be explained by the perceived difference between the provided expected ‘average’ and the experienced outcomes. Conversely, Ben-Elia et al. (2008), in a different repeated choice experiment, internalised inaccuracy by providing respondents with dynamic en-route information describing the ranges of travel times. They show that compared to non- informed respondents, informed ones learn faster and exhibit risk seeking behaviour (i.e. prefer a shorter and riskier route) in the short run which dissipates in the long run as experiential information becomes more dominant. Since the actual travel times experienced were always drawn within the descriptive range, no apparent discrepancy should have been perceived here between the information provided by the system and the outcome of choice. The two aforementioned studies illustrate the possible associations that could exist between the accuracy of information on one hand and changes in travellers risk attitudes on the other hand. Regarding feedback information, Bogers et al. (2005) showed that respondents who were provided with foregone payoffs (i.e. feedback on chosen and non-chosen alternatives alike) performed better in terms of travel time savings, though these benefits decreased over time as more experience was accumulated.
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Modelling the impacts of ATIS accuracy on travellers' route choice behaviour and risk perception

Modelling the impacts of ATIS accuracy on travellers' route choice behaviour and risk perception

In terms of methods, different theoretical frameworks have been applied to model route-choice behaviour and the impacts of information. The most fundamental approach is the Expected Utility (Von-Neumann & Morgenstern, 1944) where the main assumption is that the „rational‟ traveller seeks to maximize the perceived expected utility by choosing the alternative with the least expected costs (e.g. travel time). A detailed review of such EU-based route-choice modelling applications is provided by Prashker & Bekhor, (2004). Discrete choice (random utility) models have widely adopted this approach allowing for systemically estimating the choice probabilities. Several modelling frameworks have been developed applying repeated (daily) choices, in particular with the aim to explain the learning updating mechanism (Watling & van Vuren, 1993; Mahmassani & Liu, 1999; Srinivasan & Mahamassani, 2003) and several researchers also studied the impact of information in such cases (e.g. Nakayama et al. 2001; Nakayama & Kitamura 2000; Nakayama et al. 1999; Bonsall et al. 1997). In most cases, the modelled behaviour is reactive in nature i.e. the traveller-agent uses the provided information to update knowledge on the available alternatives. However, in reality, travellers might continue to acquire information as long as there is a reasonable gain from it (Bonsall, 2004). In this sense, the fact that a link of a network has an installed VMS makes it more attractive as demonstrated by Razo & Gao (2010) and Tian et al, (2010). Chorus et al., (2009), developed a theoretical framework based on Bayesian utilities i.e. the updating of travel time probabilities conditional on information provided. Using numerical simulations they demonstrates that compliance is negatively associated with the perceived reliability of the information and the uncertainty regarding the travel time differences between (two) alternatives. However, this still has to be tested empirically.
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Information impacts on route choice and learning behavior in a congested network

Information impacts on route choice and learning behavior in a congested network

Such incident information does in fact affect the individual behavior pattern, where more participants tend to choose the stochastic branch at the origin node and the stochastic shortcut[r]

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Tambula nange : relations between perceived walkability, built environment and pedestrian route choice in a developing country

Tambula nange : relations between perceived walkability, built environment and pedestrian route choice in a developing country

Finally, walking comfort was positively impacted by the state of the road surface (+0.999 for decent road, +0.958 for a good road and +1.443 for an excellent road). Apparently, this is considered more important than the condition of the pavement, which only had a -0.683 for absent sidewalk and +1.529 for one that is separated but not paved. As expected, though, walking comfort was positively associated with low (+1.465) or medium (+1.124) crowdedness. All of the above relationships are interesting in themselves, but even more important is whether these subjective and objective factors influenced the travel behaviour of participants (sub-question 4). After all, giving an opinion is one thing, but actually acting on it is another and this is what the walkability index will be based on. Of course, length of the path is by far the most important factor and receives parameter weight -0.02. Apparently, our participants were half as likely to use a path 35 meters longer that the shortest one. Given the prevalence of mostly shorter routes in the dataset, this is not too surprising; in fact the value is very close to some other studies as discussed in the previous section. To improve model fit a Link Constant was added as in Zimmerman et al. (2016). This parameter gives a flat penalty, around -1.4, to each link used in a path, which serves primarily to control some statistical properties of the model relating to network density. In very dense networks, many different paths can be used and this increases utility of moving from A to B, even though the existence of many not-very-different alternatives can be quite irrelevant.
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Assessing travel time reliability implications due to roadworks on private vehicles and public transport services in urban road networks

Assessing travel time reliability implications due to roadworks on private vehicles and public transport services in urban road networks

Roadworks are perhaps the most controversial topic in transport professional field. On one hand, they are a necessity to assure the current and future functionality of the traffic network, while on the other, they are seen as a major disturbance by road users with concerns for excessive travel time delays. The impact of roadworks is usually analysed at a local level however the network-wide effects are crucial to ensure reliable travel times. Moreover the analysis usually focusses on private cars and the reliability impact on public transport services are too important to ignore. This paper investigates the impact of roadworks undertaken on a given road link over wider parts of the network and assesses travel time reliability for both cars and buses. This research involves setting up of a con- ventional network assignment model to arrive at the route choice of drivers as a result of the roadworks and then integrates the outcomes with a microsimulation model to generate space-time trajectories to arrive at travel times of individual vehicles. We adopted a reli- ability measure from the literature to compute travel time reliability of a given type of vehicle by unique origin-destination (O-D) pair combinations and also more generally to provide a wider picture at an aggregated network level. The method was tested on a real life network in England, and travel time reliability results were analysed both at the network scale and significant O-D pair level for private cars and bus routes.
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The combined effect of information and experience on drivers' route choice behavior

The combined effect of information and experience on drivers' route choice behavior

approach to previous discrete choice studies (those without information effects) these studies enabled perception and processing mechanisms based on principles of bounded rationality (Simon, 1982), simple heuristics and limited sets of choice rules. In addition other studies applied fuzzy logic principles like “if-then” rules (Peeta & Yu, 2005; Lotan, 1993) or dynamic assignment and micro simulation using artificial agents (Nakayama et, al., 2001; Nakayama & Kitamura, 2000; Nakayama et. al., 1999; Bonsall et. al., 1997). A few studies attempted to investigate information impacts using revealed preference field studies of real locations or ATIS systems (Fujii & Kitamura, 2000, Kraan et. al., 1999; Deakin, 1997; Wardman et.al., 1997; Khattak & Khattak., 1996). The current research is designed to contribute to this line of empirical research by building on recent basic behavioral research.
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Which road do I take? A learning based model of route choice behavior with real time information

Which road do I take? A learning based model of route choice behavior with real time information

Under these assumptions which is the most probable choice Alice will make in each scenario? Answering this question requires formulating a choice model that explains her choices. However, estimating the impact of information within the framework of a choice model is hardly a simple task. Since information is assumed to change the level of uncertainty in the choice situation, the main challenge is to model travelers' response to information. Thus, the assumption of perfect information generally embedded in random utility-based route-choice models, i.e. without information effects, is discarded. These models assume drivers’ cognition is governed by rational choice. They do not explicitly abstract behavior under uncertainty; rather they apply different sets of assumptions on the distributions of the unobservable factors (Watling & van Vuren, 1993). In this context, flexible error terms as applied in mixed discrete choice models such as Mixed Logit (Srinivasan & Mahmassani, 1999) and Mixed Probit (Mahmassani & Liu, 1999) have proved of added value. Using dynamic specifications is another approach to reduce uncertainty by updating the level of utility over time (Horowitz, 1984). It is also possible to assume that travelers' cognition reflects bounded rationality (Simon, 1982) whereby choices are made based on simple decision rules or heuristics in the form of thresholds of accepting possible outcomes. Srinivasan & Mahamassani, (2003) applied this approach by using a Mixed Logit
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Analysing the impacts of the traveler information provision services and the speed limit control on vehicular emissions and congestion

Analysing the impacts of the traveler information provision services and the speed limit control on vehicular emissions and congestion

With the implementation of traveler information provision services, travelers with better information about a transport network may change their route choice behavior and this may affect[r]

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Evaluating the effects of information reliability on travellers’ route choice

Evaluating the effects of information reliability on travellers’ route choice

The aim of this paper is to ascertain whether and to what extent the way data are collected and used can affect the interpretation of experimental results obtained by stated preference (SP) surveys based on the use of travel simulators and applied through repeated choices. The final goal here is to assess the impacts of Advanced Traveller Information Systems (ATIS) on both travellers’ compliance with information and route choice. In recent decades ATIS applications have been a popular research topic for transportation analysts and many models have been proposed and discussed due to the widespread need to solve traffic oversaturation problems with ever-diminishing infrastruc- tural investments. Moreover, from a commercial point of view, traffic information is also a valuable content for modern applica- tions in the field of telecommunications. Indeed, some studies have shown that travellers exhibit a considerable willingness to pay for reliable advanced traffic information [27]. ATIS are intrinsically integrated with advanced communication platforms and devices, and aim to enhance (or integrate) the information level on network conditions that most travellers already have from their own traffic estimation process (experience). Information contents are gathered, elaborated and delivered by traffic control centres which are able to increase the reliability and effectiveness of (real-time) monitored traffic data.
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Testing the Theoretical Accuracy of Travel Choice Models
Using Monte Carlo Simulation.

Testing the Theoretical Accuracy of Travel Choice Models Using Monte Carlo Simulation.

This is an ITS Working Paper produced and published by the University of Leeds. ITS Working Papers are intended to provide information and encourage discussion on a topic in advance of formal publication. They represent only the views of the authors, and do not necessarily reflect the views or approval of the sponsors.

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Impact of travel destination factors on travel destination choice: An empirical investigation of Indian Travelers

Impact of travel destination factors on travel destination choice: An empirical investigation of Indian Travelers

Destination branding appears to be emerging as one of the most compelling tools available to destination marketers seeking a competitive advantage (Murphy, Benckendorff and Moscardo, 2007) Brand image is an important concept in consumer behavior (Dobni and Zinkhan, 1990). The most common and widely accepted definition of brand image is “the perceptions about a brand reflected as associations existing in the memory of the consumer” (Keller, 1993). Much empirical research support the premise that destination image consists primarily of two components: cognitive and affective (Crompton, 1979). It’s not necessary the destination image always has to be about only based on the word of mouth from other people. Today Social Media has made everything highly accessible to help us get information and also build up destination images in our mind. Destination image is generally known as an essential perspective in fruitful tourism advancement and showcasing the destinations because of its effect on both supply-and demand side parts of showcasing. (Tasci and Gartner, 2007). The focal hypothesizes of the destination image is considered has a significant part in a person's travel purchase which is related to basic leadership and a person’s voyager's fulfillment/disappointment. To a great extent travel purchase relies upon an examination of his assumption about the destination, or a formerly held destination image, and his anticipated performance of the destination (Steven and pike, 2002)
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ROUTE CHOICE MODELLING FOR BICYCLE TRIPS

ROUTE CHOICE MODELLING FOR BICYCLE TRIPS

The first group of cycle planners are ones which are covering travelling around the city. An example which could be used is the Vancouver cycle planner (Cycling Route Planner, 2007). The user has a possibility to set his preferences, such as priority usage of cyclist facilities instead of major roads, shortest path, least elevation gain path, least traffic pollution or mostly vegetated path. Then the planner finds ideal path which is based on these preferences and display the path on the map with overall information about it along with directions. Another even more detail approach is used in the OPT for health – route planner for San Francisco (OPT for Health, 2010). Cyclists are not the only target group but possibility to adjust the searching for ideal route is here even more complex. The user is allowed to set values for each attribute which is considered (e.g. most bicycle friendly, major road, traffic access restricted…). This setting is supposed to lead to improvement of searched path. On the other hand there are other types of cycle planners which are trying to be still complex even with limited amount of adjustable preferences such as San Francisco Bicycle trip planner (2009). In this planner the user can choose only from three types of paths – shortest, balanced or biker friendly and setting of maximum grade.
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Optimal Travel Route Discovery based on Topic Interest and Image Attributes

Optimal Travel Route Discovery based on Topic Interest and Image Attributes

A. Y.-T. Wen, K.-J. Cho, W.-C. Peng, J. Yeo and S.-W. Hwang [2] proposed a keyword-aware travel route recommendation system. Given the huge number of registered data and photos in social media, it intends to discover travel experiences to promote travel plans. The system observes that when planning a trip, the user may have some keywords about his/her preferences in his/her travel. In addition, a variety of travel routes are also needed. In order to provide a variety of tourist routes, the system claims to extract features of more attractions (POIs). Therefore, proposed system is a keyword-aware Skyline Travel Path (KSTR) framework that uses knowledge extracted from historical flow information and users social activity in social sites. Explicitly, the system analyse the "where, when, who" problem by characterizing the geographical movement patterns, time effects, and social impact. Then a keyword capturing step (phase) is proposed to automatically classify the POI related tags into different types so as to effectively match the query keywords. The route reconstruction algorithm is further designed to build a route candidate that matches the user provided input. Provide diversified search results and explore the Skyline concept to arrange routes. In order to find the accuracy of given algorithm, extensive experiments were conducted on location-based real social network datasets. B. T. Kurashima, T. Iwata, G. Irie, and K. Fujimura [3] suggest using geo-tags to recommend travel routes in photo-sharing sites.
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Travel time predictions: should one model speeds or travel times?

Travel time predictions: should one model speeds or travel times?

This result is of importance for routing devices as they typically optimize routes by minimizing the sum of the expected link travel times. More accurate predicted link travel times will lead to better routes and hence save time and costs. Our results indicate that the user should provide information on the preferred accuracy measure in order to choose the optimal method for pre- dicting link travel times as the input to the routing: If one is interested in minimizing the mean squared error (as is relevant for logistics applications), then the direct method should be selected. However, if one is interested in obtaining a small mean absolute percentage error (as is arguably of more interest for individual travel time predictions), then the indirect method achieves better results.
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Stochastic Capacity at Freeway Bottlenecks with Application to Travel Time Prediction.

Stochastic Capacity at Freeway Bottlenecks with Application to Travel Time Prediction.

Mesoscopic models are at the intermediate level between micro and macro models. Mesoscopic models represent individual vehicle motion based on macroscopic traffic relationships (ex., speed-density function), but not their interactions (2). Since mesoscopic models keep recording the travel experience of each individual vehicle, they are normally used as an evaluation tool for traveler information systems. Mesoscopic models integrated with Dynamic Traffic Assignment (DTA) (3, 4) have become popular tools for traffic analysts to perform such applications (i.e., capacity enhancement strategy assessment) on large-scale transportation networks because of thier ability to allocate individual vehicle within the network to their destination based on factors that affect their route choice. Over the last three decades, the focus of DTA research has been on the demand characterization side, such as incorporating dynamic time-dependent demand, stochastic departure times, and multiple user classes.
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Route Choice and the Value of Motorists’ Travel Time:
Empirical Findings

Route Choice and the Value of Motorists’ Travel Time: Empirical Findings

This is an ITS Working Paper produced and published by the University of Leeds. ITS Working Papers are intended to provide information and encourage discussion on a topic in advance of formal publication. They represent only the views of the authors, and do not necessarily reflect the views or approval of the sponsors.

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Route Choice and the Value of Motorists’ Travel Time:
Theoretical and Methodological Issues

Route Choice and the Value of Motorists’ Travel Time: Theoretical and Methodological Issues

This is an ITS Working Paper produced and published by the University of Leeds. ITS Working Papers are intended to provide information and encourage discussion on a topic in advance of formal publication. They represent only the views of the authors, and do not necessarily reflect the views or approval of the sponsors.

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Information Bottleneck for Gaussian Variables

Information Bottleneck for Gaussian Variables

Lossy reconstruction can be considered by introducing distortion measures as done for source coding of Gaussians with side information by Wyner (1978) and by Berger and Zamir (1999) (see also Pradhan, 1998), but these focus on the region of achievable rates under constrained distortion and are not relevant for the question of finding the representations which capture the information between the variables. Among these, the formalism closest to ours is that of Berger and Zamir (1999) where the distortion in reconstructing X is assumed to be small (high-resolution scenario). However, their results refer to encoding rates and as such go to infinity as the distortion goes to zero. They also analyze the problem for scalar Gaussian variables, but the one-dimensional setting does not reveal the interesting spectral properties and phase transitions which appear only in the multidimensional case discussed here.
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Measurement of Mature Spring/Spa Travellers‟ Beliefs in Taiwan

Measurement of Mature Spring/Spa Travellers‟ Beliefs in Taiwan

B. Mature Travellers’ Beliefs about Visiting Springs/Spas Many Taiwanese mature people have experienced bathing in a frigid climate in mineral spring waters, which has been common since the late 1800s. Even spring tourism has been acknowledged for a century in Taiwan‟s travel market. People continue today to search for the reason for the re-birth of mineral springs and the boom in health spas in Taiwan in the first decade of the 21 st century. Why were spring/spa activities so popular both during Japanese times and in the present day? And did people really believe the water could cure illness? Some spring/spa destinations are rather old or in faraway locations; even so, travellers were willing to believe they had medicinal effects for their ill-health. In answer to those questions, people may have realised that different types of natural springs have diverse and unique medicinal effects for improving health. That is to say, the benefits of soaking their body in the mineral springs or health spas are multi-functional. This perception is similar to those people who believe in drinking bottled water and taking vitamins in their daily lives.
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Modelling Preferences for Smart Modes and Services: A Case Study in Lisbon

Modelling Preferences for Smart Modes and Services: A Case Study in Lisbon

Choice models of smart travel options have primarily focused on modelling preferences for advanced travel information systems (ATIS)- both for cars and public transport (e.g. see 11 for a comprehensive review), shared mobility (e.g. 12, 13), demand responsive services (e.g. 14) and more recently smart/autonomous vehicles (e.g. 15). The majority of these choice models have relied on the Stated Preference (SP) surveys, particularly the studies which have been conducted in the pre-deployment stage. The scope of these models has however been limited to choices among similar modes (e.g. choice of traditional car vs. smart car, car-share vs. solo driving, etc.) as opposed to comprehensive choice experiments covering the full range of possible options including multi- modal alternatives. This is primarily due to the complexity associated with the SP survey design in presence of large choice sets Alternate sources of data include variants of SP survey like travel simulator exper iments (e.g. 17) and the combination of SP and Revealed Preference (RP) data (e.g. 18). These have concentrated on smaller subsets of alternatives at a time rather than the full range of options.
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