Provision Of Travel Information Research Articles

Factors affecting paratransit travel times at route and segment levels

Paratransit users have reportedly been unsatisfied with the quality of service that they receive. Efforts at replacing the service or formalizing operations to meet users’ mobility needs have faced challenges or outrightly resisted. Approaches such as providing travel information and deploying interventions along the roadway infrastructure where the government has authority have been suggested. Deploying any of these approaches will require insights from empirical data. The study considered a key measure of service quality to users and operators alike – Travel Time. It investigated factors affecting travel times of paratransit at the route and segment levels. A travel time survey that employed a mobile app (Trands) onboard paratransit vehicle was used to collect travel time, stop, and other related information on a selected route. The Backward Stepwise Regression technique was used to determine factors affecting paratransit travel times. Dwell time, signal delay, recurrent congestion index (RCI), non-trip stops, and deviation from route were significant variables at the route level. All the factors affecting segment travel times were also part of those involving route travel times except the segment length. Interestingly, deviation from the route increased overall travel time, which is against its logic. Insights gained from the study were used in suggesting proposals that can reduce travel time and improve the service quality of paratransit.

Travel information provision and commuter behavior changes: Evidence from a french metropolis

This study aimed at assessing the role of travel information provision on behavioral changes in daily mobility in the metropolis of Montpellier in France. We focused on commuter behavior using data collected through an online survey on mobility behavior during the summer of 2015. We found that while 73% of commuters respondents use travel information sources, only 31% of them declared any mobility behavior change. This study explores the impact of specific factors such as socio-demographic variables (e.g., age and gender) and transportation habits (e.g., public transportation pass, travel time, and safety margin). As an operational measure of the prudent behavior, safety margin highlights the threshold effect of travel information provision on behavioral changes. Indeed, three commuter profiles can be distinguished according to their prudence levels: chronically non-prudent, reasonably prudent, and excessively prudent. Finally, the study highlights that travel information provision alone may not be enough to induce a shift in behavioral changes among commuters toward more environment-friendly modes of transportation.

여행 유튜브 콘텐츠 속성이 이용자의 정보 만족, 관광지 태도와 방문 의도에 미치는 영향

As Youtube takes root in consumers' daily lives, travel content on Youtube has become a major information channel for a travel destination. Travel content on Youtube provides information to help users relive their memories and plan new trips. The purpose of this study is to propose an effective content marketing strategy by examining the effects of the attributes of travel content on Youtube on information satisfaction, attitude, and visit intention. To this end, a survey was conducted on Youtube users who had watched within the last 6 months, and 279 were used for analysis. As a result of the analysis, it was confirmed that attributes such as enjoyment, reliability, timeliness, vividness, and usefulness affect information satisfaction. Also, information satisfaction was found to have a significant effect on attitudes toward a destination. Finally, it was confirmed that there was a significant influence relationship between attitude and intention to visit. These results are significant in that the influential relationship on information satisfaction was confirmed by encompassing content attributes fragmented in previous studies, and the scope of the study was expanded by adding an attitude variable to predict visit intention. In particular, it was found that the attribute that had the greatest influence on information satisfaction was enjoyment, and that reliability and usefulness had low effects. These results suggest that emotional rather than informational attributes should be emphasized when providing travel information to Youtube users.

The Effects of Travelers’ Price Sensitivity on Information Search Behaviors

In a remarkably heterogeneous tourism market, marketers apply a wide range of strategies which help them ward off competitors and attract customers. The openness of travel information such as product and service quality and price is essential but still a challenge for marketers since traveler characteristics are often multi-dimensional. This study devotes special attention to travelers’ price sensitivity, and aims to investigate whether price sensitivity can segment travelers and the effects on information search behavior. For this purpose, the research study conducted Analysis of Variance (ANOVA) and regression analysis using survey data of 310 respondents. The results confirm the existence of heterogeneity in price sensitivity and there is a clear difference in the use of information by travelers resulting from this variable. Marketers should therefore utilize different communication strategies for travelers with different price sensitivities. For example, to obtain price-sensitive travelers it is more efficient to provide travel information with a clear difference in price between products and services that will reduce their search efforts. On the other hand, to target price-insensitive travelers, marketers should provide sufficient information about product attributes through online personal information sources including organizations such as Trip Advisor, Twitter, Facebook, and Instagram.

The implementation of social customer relationship management for tourism information system

Semambu island village, Ogan Ilir regency, south Sumatra has been used as an agricultural and livestock education tour destination since November 2017 and there has been no customer data management since then. The use of social media as a promotional tool has not been done to its maximum potential. This can be seen from 189 people who liked its Facebook page or 11.05% out of the reached users, 192 followers or 11.23% and those who interacted as many as 114 people or 6.67% from the total users. Meanwhile, there were 709 followers on its Instagram which consisted of 48% men and 52% women at the time of the study. This research applied social customer relationship management (social CRM) in a website-based system. The waterfall model development method supported the customer relations management by utilizing Facebook and Instagram to improve customer relationships in providing travel information, knowing interest and listening to complains as well as their suggestions based on interactions with the social media users as existing and prospective customers.

How Attractive are Public Transport Interchanges? A Cross Comparison of Two European Terminals

As the world becomes more urbanized, there is a strong need for urban public transport to provide sustainable alternative solutions against private-vehicle usage. However, the opportunities for seamless journeys through public transport are still limited and the need for properly designed and operated transport interchanges is vital. The present paper investigates the perceptions and the users' level of satisfaction when using the New Railway Station of Thessaloniki in Greece and the Riga International Coach Terminal in Latvia, in terms of services provision and station's operation. In total, 36 indicators were tested, grouped in eight quality factors, namely travel information, wayfinding information, time and movement, access, comfort and convenience, station attractiveness, safety and security and emergency situation handling. Attitudinal surveys were implemented to determine key performance factors that affect travelers' satisfaction when using the two terminals. Data were collected through on-line questionnaires and were elaborated through descriptive and inferential statistics, including Mann-Whitney two-sample U-testing to assess differences between the samples in variables measured on a 5-point Likert scale, Spearman bivariate correlations to measure the strength of association between the quality indicators and multiple regression analyses to examine the effect of selected attributes on the general satisfaction level of travelers. Results showed that both interchanges perform better in physical quality attributes, like access, travel and wayfinding information provision, but they do not satisfy users' aesthetics expectations in the internal and external area of the interchanges and the surrounding area and they do not cover adequately their feeling of security and safety in the transfer or waiting areas. These results highlighted the users' preferences and concerns which contribute into a satisfactory overall design of the interchanges. In a nutshell, transport interchange design should satisfy both providing a hub for seamless mobility, but also integrating the station as a part of the public realm.

The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions

Uncertainty in transportation systems can incur additional travel costs, but this adverse effect can be mitigated by providing travel information (Lindsey et al., 2014). It is not clear, however, if providing information always reduces system travel costs, particularly when the information provided is not one hundred percent accurate. This paper studies the welfare effects resulting from providing pre-trip information to morning commuters in a two-route network with bottlenecks, where the bottleneck capacity and free-flow travel time on each route are stochastic. We derive the expected travel costs at user equilibrium (UE) under stochastic conditions without and with pre-trip information first on one route, and later on two routes. Under this model, we examine how accurate and inaccurate information affect commuting costs. We find that full and accurate information is welfare-improving under stochastic bottleneck capacity and deterministic free-flow travel time. However, when bottleneck capacity and free-flow travel time are both stochastic, full and accurate information can be welfare-reducing for the two-route scenario. We show that the degree of correlation between routes in traffic conditions, the frequency and severity of bottleneck capacity drops, and the relationship between free-flow travel time and bottleneck capacity significantly affect the welfare effects of pre-trip information. Furthermore, when bottleneck capacity experiences severe drops, full information is more likely to be welfare-improving, even if the provided information is not completely accurate. These theoretical results are supplemented by numerical examples that show the welfare effects of providing pre-trip information to morning commuters.

Dynamic Path Flow Estimation Using Automatic Vehicle Identification and Probe Vehicle Trajectory Data: A 3D Convolutional Neural Network Model

Dynamic path flows, referring to the number of vehicles that choose each path in a network over time, are generally estimated with the partial observations as the input. The automatic vehicle identification (AVI) system and probe vehicle trajectories are now popular and can provide rich and complementary trip information, but the data fusion was rarely explored. Therefore, in this paper, the dynamic path flow estimation is based on these two data sources and transformed into a feature learning problem. To fuse the two data sources belonging to different detection ways at the data level, the virtual AVI points, analogous to the real AVI points (turning movements at nodes with AVI detectors), are defined and selected to statically observe the dynamic movement of the probe vehicles. The corresponding selection principles and a programming model considering the distribution of real AVI points are first established. The selected virtual AVI points are used to construct the input tensor, and the turning movement-based observations from both the data sources can be extracted and fused. Then, a three-dimensional (3D) convolutional neural network (CNN) model is designed to exploit the hidden patterns from the tensor and establish the high-dimensional correlations with path flows. As the path flow labels commonly with noises, the bootstrapping method is adopted for model training and the corresponding relabeling principle is defined to purify the noisy labels. The entire model is extensively tested based on a realistic road network, and the results show that the designed CNN model with the presented data fusion method can perform well in training time and estimation accuracy. The robustness of a model to noisy labels is also improved through the bootstrapping method. The dynamic path flows estimated by the trained model can be applied to travel information provision, proactive route guidance, and signal control with high real-time requirements.

Od "samo gledam" do rezervacije avio karte

The online booking behaviour of tourist website visitors shows that there is a transition from browsing to the purchasing process. Potential travellers are expected to benefit from a broad range of market opportunities made available by the Internet, the most revolutionary of which is the opportunity for price reductions as a direct result of the elimination of intermediaries in the travel industry distribution chain. Customers have to deal with a variety of challenges and limits when provided with various alternatives for even simple and low-cost travel bookings, and word-of-mouth from online reviews acts as a vital source of information for customers. A significant component in influencing customer decision-making is eWOM and it is also a key factor in the individual booking of services and products. Customers' perception of the credibility of reviews significantly influences their behavioural intentions and the role of eWOM in providing travel information and tourists' behavioural intent can produce "myths " on the opportunities for travel savings and air tickets cost.

Evaluating the influence of information provision (when and how) on route choice preferences of road users in Greater Orlando: Application of a regret minimization approach

With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. While earlier research has examined route choice preferences in relation to travel time and travel cost (or toll), there is little guidance on the influence of information provision mechanisms. By accommodating for information provision attributes, the proposed research contributes to our understanding of the design of an active traffic management (ATM) system by quantitatively estimating the inherent trade-offs across the various attributes affecting route choice. Specifically, the research designs and elicits data using a web-based stated preference (SP) survey to understand road users’ preferencesin the Greater Orlando Region, USA. In the empirical analysis, the data compiled is utilized to develop random utility maximization and random regret minimization based panel mixed multinomial logit models. Route choice behavior is modeled using a comprehensive set of exogenous variables including trip characteristics, roadway characteristics and traffic information characteristics. The model results are utilized to conduct a comprehensive trade-off analysis across various attributes for the two model frameworks. In this research effort, we also customize the trade-off computation for regret minimization model for accommodating variable interactions. The trade-offs results provide useful insights on travel information provision (when and how).

The effect of travel time information on day-to-day route choice behaviour: evidence from a real-world experiment

Route choice behaviour in response to travel information receives increasing attention within travel behaviour research. This study contributes to the literature by generating insights into the effect of travel information on day-to-day route choice behaviour based on largely explorative analyses using route choice data obtained from a real-world experiment. As such, our study complements confirmatory stated preference and laboratory experiments. We find that the provision of travel information leads to a decline in switching propensity and a higher probability that the shortest route is chosen. Furthermore, we identified six behavioural profiles, varying from switch-averse to switch-prone. Travel time information seems to influence travellers’ propensity to shift from one profile to another across different OD-pairs. Our results contribute to understanding of the effect of travel information on route choice behaviour, and as such help inform the design of effective information-based demand management measures.

Measuring Travellers’ and Stakeholders’ Satisfaction on Perceived Sustainability of a Transport Interchange

Abstract The present paper aims at measuring the satisfaction of travellers and stakeholders on perceived quality of service provided at the Latvian interchange “Riga International Coach Terminal” and understand whether there are any gaps between the two groups of involved parties, based on their perceptions and expectations. To this end, a travellers’ attitudinal survey was organized and interviews with representative stakeholders were conducted, in order to capture and analyse their attitudes and preferences and extract those attributes that affect their satisfaction. Results showed that the interchange performs well in physical quality attributes, such as travel and wayfinding information provision, but in terms of access and aesthetics expectations in the internal and external design, some contradictory findings were revealed between travellers and stakeholders, validating the fact that understanding users’ perceptions can work as vital input to policy makers’ perceptions of an integrated sustainable public transport system.

Quantifying traveler information provision in dynamic heterogeneous traffic networks

ABSTRACTInformation is effectively the same as a change in uncertainty perceived by an observer. This paper adopts the strict definition of information from Shannon’s Information Theory and provides procedures for quantifying effective provision of traveler information, considering it to be equivalent to the change of perceived uncertainty. The proposed method combines a cognitive grouping theory and an information learning scheme at an individual’s level to evaluate the dynamic information provision in the unit of a bit. Such numerical quantification can be meaningful in evaluating alternatives with more fine-grained information provision strategies and understanding their equity impact. Quantifying information in a manner consistent with Information Theory also provides a ‘shared language’ that facilitates more constructive discussion among stakeholders from different backgrounds. The case study is conducted on a heterogeneous dynamic traffic network near Downtown Los Angeles for evaluating different alternatives of a proposed dynamic message board in terms of its location and dynamic content.

Optimal Information Provision at Bottleneck Equilibrium with Risk-Averse Travelers

This paper aims to study the effects of travel information provision on risk-averse travelers when travel time is uncertain. A stochastic bottleneck model is examined with risk-averse commuters, in which the free-flow travel time is assumed to be uncertain and follows a uniform distribution. A mean-variance approach is adopted to measure the travel cost under risk. It is proven that the individual travel cost at bottleneck equilibrium monotonically increases with the risk-aversion level. With a higher risk-aversion level, the morning peak hour starts earlier, but the duration of the peak hour remains constant regardless of the risk-aversion level. If improvement in information quality can reduce the travel time uncertainty, risk-averse commuters will benefit from the higher quality of information. Nevertheless, when the cost of information provision is also considered, the optimal information provision strategy is derived to minimize the total system cost. The numerical examples demonstrate the information efficiency and provision strategy. The findings reveal the congestion patterns of a stochastic bottleneck with risk-averse travelers and will guide us to provide appropriate travel information.

An agent-based model of the emergence of cooperation and a fair and stable system optimum using ATIS on a simple road network

Traffic congestion threats the growth and vitality of cities. Policy measures like punishments or rewards often fail to create a long term remedy. The rise of Information and Communication Technologies (ICT) enable provision of travel information through advanced traveler information systems (ATIS). Current ATIS based on shortest path routing might expedite traffic to converge towards the suboptimal User Equilibrium (UE) state. We consider that ATIS can persuade drivers to cooperate, pushing the road network in the long run towards the System Optimum (SO) instead. We develop an agent based model that simulates day-to-day evolution of road traffic on a simple binary road network, where the behavior of agents is reinforced by their previous experiences. Scenarios are generated based on various network designs, information recommendation allocations and incentive mechanisms and tested regarding efficiency, stability and equity criteria. Results show that agents learn to cooperate without incentives, but this is highly sensitive to the type of recommendation allocation and network-specific design. Punishment or rewards are useful incentives, especially when cooperation between agents requires them to change behavior against their natural tendencies. The resulting system optimal states are to most parts efficient, stable and not least equitable. The implications for future ATIS design and operations are further discussed.

Optimal travel information provision strategies: an agent-based approach under uncertainty

ABSTRACTInformation influences travel behavior a great deal. This paper applies and further develops an agent-based approach to modeling travel behavior under uncertainty and different information provision strategies. Artificially intelligent agents, with the capability of learning, information acquisition, searching, and decision, are constructed instead of the typical representative agents formulated by utility maximization theory. Meanwhile, the presumption of sequential behavior process is relaxed. Agents are flexible to adjust travel mode, departure time, and route in response to a stimulus. A traffic simulator is also integrated in order to simulate agents' travel experiences on a transportation network. The agent-based model is then integrated into a simulation-based optimization (SBO) framework to analyze the effects of different information provision strategies. It is found that providing real-time traffic information to agents does not always result in improved network traffic condition or higher network reliability. Thus, we employ SBO technique to identify the optimal information provision strategies to support policy/planning decision-making.

Dynamic route choice behaviour and simulation-based dynamic traffic assignment model for mixed traffic flows

Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of advanced computers, electronics, and communication technologies. In order to perform advanced traffic management and provide travel information, dynamic traffic assignment models need to be developed to provide time-dependent estimates of traffic flows on networks in order to efficiently utilize possible advanced traffic information as well as traffic control measures. Traffic assignment distributes Origin-Destination (OD) trips in a network and determines the flow patterns in a traffic network. This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations. Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck. Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure. The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times. In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages. Numerical experiments are conducted in a test network and a real city network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user class’s conditions, including multiple user behavior rules and multiple physical vehicle classes.

Traffic state and emission estimation for urban expressways based on heterogeneous data

Urban expressways, as the backbone of a city's transportation network, are critical for reducing traffic congestion and improving transportation efficiency of the whole network. The estimation of traffic states and emissions for urban expressways supports traveler information provision and system-wide traffic management. This paper aims to modify the extended generalized Treiber-Helbing filter (EGTF) to fuse GPS data (probe vehicles) and traditional traffic data (loop detectors), so as to enhance more accurate estimations of traffic states and emissions on urban expressways. The speed field is first reconstructed based on heterogeneous data, and then travel time and emissions are estimated using a virtual trajectory method and the VT-Micro model, respectively. The algorithm is applied to a real-world case study for an urban expressway in Beijing, China. After the parameter tuning, the proposed algorithm is compared with existing algorithms from the literature. Numerical results show that data fusion using the proposed algorithm could make better use of heterogeneous data and increase the accuracy of travel time and emissions estimations.

Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions

In this study, to incorporate realistic discrete stochastic capacity distribution over a large number of sampling days or scenarios (say 30–100days), we propose a multi-scenario based optimization model with different types of traveler knowledge in an advanced traveler information provision environment. The proposed method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions (and related reliability measure) across a large number of different sampling days. Using a gap function framework or describing the mixed user equilibrium under different information availability over a long-term steady state, a nonlinear programming model is formulated to describe the route choice behavior of the perfect information (PI) and expected travel time (ETT) user classes under stochastic day-dependent travel time. Driven by a computationally efficient algorithm suitable for large-scale networks, the model was implemented in a standard optimization solver and an open-source simulation package and further applied to medium-scale networks to examine the effectiveness of dynamic traveler information under realistic stochastic capacity conditions.

Breaking the habit: Does fracturing your wrist change your travel and driver behaviour?

When someone breaks their wrist it presents a disruption to everyday routine. Some of this is as a result of having to change travel patterns. This paper investigates the changes people make to their travel behaviour in the light of an unexpected change in their situation caused by fracturing their wrist and wearing a forearm plaster cast. One hundred and eleven participants, approached as they were having their plaster cast removed, completed a questionnaire addressing travel behaviour change, driver safety and information provision covering their time in a plaster cast (typically an average of 5–6weeks). Eighteen percent of participants drove during the time they had a forearm plaster cast on. All reported they felt safe in doing so and that wearing the plaster cast did not compromise safety, though it was uncomfortable and compensatory behaviours took place. Risk and affective scales did not predict whether participants drove in a cast, suggesting that practical and utilitarian, rather than psychosocial, reasons were the motivation for driving in a plaster cast. Eighty-two percent found other ways of travelling without using their car. Participant’s use of buses and trains, walking and taking lifts were all increased and overall, across all modes of transport, participants travelled fewer miles but made more journeys. There was a reduction in cycling, especially for those who drove in a cast, suggesting cyclists who broke their wrist changed to driving while their arm was in a cast. Information provision did not affect whether someone drove or not. Implications for providing travel information to help people avoid car use while their forearm is in a cast and maintaining behaviour change afterwards are discussed.