Understanding Of Travel Behavior Research Articles

The influence of travel attitudes on perceived walking accessibility and walking behaviour

People are driven by their perceptions. Perceptions of the environment shape mobility decisions and represent the “lived reality” of accessibility but are mostly disregarded in common accessibility analysis. This paper explores the role of perceived accessibility. By using a structural equation modelling (SEM) approach, we investigate multiple relations between travel attitudes, socio-demographic factors, objective walking accessibility, perceived walking accessibility and realised walking behaviour in three steps. Firstly, we analyse the influence of objective walking accessibility and travel attitudes on perceived walking accessibility. Secondly, we look at perceived walking accessibility as a mediator variable and, finally, we investigate the association between perceived walking accessibility and walking behaviour. Key results imply that travel attitudes are important in understanding variations in perceptions, in terms of direct effects and as mediator variables. Perceived walking accessibility directly affects the probability of walking and plays, therefore, an important role in understanding travel behaviour. The implications of our results are discussed with regard to future research gaps and policy.

EXPLORING THE RELATIONSHIPS BETWEEN SATISFACTION, PLACE ATTACHMENT AND LOYALTY IN NIGERIAN ZOOS

Surveys exploring satisfaction, place attachment and loyalty in a zoo context are scarce despite the prevalence of such studies vis a vis other tourism destinations, and their valued importance in understanding travel behaviour and contributions to destinations’ success. This study explores the relationships between visitors’ satisfaction, place attachment (place identity, place dependence, place affect and place social bonding) and loyalty in one of Nigeria’s prominent zoos. This study adoped a qunatitative research approach, with data obtained from a sample of 395 visitors through a structured questionnaire. Using structural equation modeling, we found that the most important causal factor of loyalty is satisfaction, either directly or indirectly through some dimensions of place attachment. In addition, it was revealed that place social bonding can act as either a predictor or an outcome of visitors’ satisfaction. Satisfaction was found to be a significant and positive mediator between place attachment (place identity and place social bonding) while place attachment was found not to mediate the relationship between satisfaction and loyalty. The findings were discussed and practical applications were drawn, particularly aimed at optimizing and providing satisfying experiences which enhances place attachment as well as loyalty.

How does Dockless bike sharing serve users in Nanjing, China? User surveys vs. trip records

Dockless bike sharing (DBS), as an emerging bike sharing scheme, effectively promotes active travel and improves the mobility of users. Travel behavior analysis of DBS users is a key basis of service improvement, and the data sources include user surveys and trip records. User surveys are widely applied but have the drawback of perceptual errors. Thus, there is a compelling need for exploring travel behavior using automatically-collected trip records. This study examines DBS users' travel frequency and purpose with five semi-annual user surveys and trip record data in Nanjing, China. User surveys reflect user perception of travel behavior, while trip records can be used to calculate actual travel frequency and infer travel purpose. The results show that perceptual travel frequency based on user surveys has a central tendency, and actual travel frequency based on trip records follows a heavy-tailed distribution. Older people tend to underestimate their travel frequency, but other age groups tend to overestimate their frequency. In addition, a dockless bike sharing's travel purpose inferring (DBS-TPI) model is proposed to infer the travel purpose based on trip record data and points of interest data. The DBS-TPI model is reliable and transferable under different scenarios, which could complement user surveys. This study provides insights into improving data collection for better understanding of travel behaviors among DBS users.

Spatial and Temporal Distribution of Elderly Public Transport Mode Preference

The elderly population is increasing rapidly. Understanding travel behaviour for this group of commuters (in terms of the trip purpose and travel time) is necessary for future transport planning. Many researchers are working on travel’s spatial and temporal analysis to provide operational decision making and transport network planning. This research study’s primary purpose is to identify the influence of trip duration (using public transport), time of the day (usage of public transport), and public transport (PT) accessibility over public transport mode preference by elderly (over 65 years of age) commuters. The methodology of this study is divided into two parts as spatial analysis and temporal analysis. The research identified the dependency of trip duration, time of the day, geographical areas, and PT access over transport mode preference of elderly. The temporal study shows that transport mode preference can vary depending on trip purposes. However, for specific trip durations and times of the day, the elderly sometimes choose PT as a mobility mode. For instance, on shopping trips between 10:00 and 11:00 a.m., the elderly have a greater possibility of choosing public transport over private vehicles. Moreover, the results show the public transport mode preference based on different times of the day and trip purposes. Urban and transport planner can use the results to modify/plan public transport schedule, which can be easily accessible by the elderly population.

Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior

This study proposes an approach for modeling travel behavior under uncertainty coupling Cumulative Prospect Theory (CPT) with Multi-attribute Decision Making (MADM) theory. CPT is utilized to depict travelers’ evaluations of each attribute, and MADM describes the process of making tradeoffs among multiple conflicting criteria. Divergent perception principles for different attributes are considered in the proposed framework. The proposed approach is utilized for an empirical analysis concerning mode shift behavior for commuting in Shanghai of China, based on data collected by stated preference surveys. Results show that the proposed approach outperforms conventional methods in terms of model performances and behavioral revelations. Empirical results demonstrate that sensitivity to gains and losses in cost and travel time are divergent in mode shift behavior. More importantly, it is found that travelers underestimate the occurrence chances of low-probability travel time and overestimate the occurrence changes of high-probability travel time in mode shift behavior, which is contrary to the findings from economics. Travelers show substantial loss aversion features as well. The heterogeneity in the value functions of CPT is investigated to shed light on differences in the evaluation process among individuals. Results reveal quite different empirical CPT parameters and behavioral mechanisms in mode shift behavior as compared to monetary experiments in economics. It highlights the importance of empirical estimations in various travel choice contexts to essentially understand travel behavior mechanisms, rather than arbitrary usage of findings from economics.

Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective

The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups, such as tourists, remain underexplored. In this study, we introduce an analytical framework to gain insights into tourist mobility patterns. By analyzing mobile phone trajectories of international travelers to three different cities in South Korea, we introduce nine mobility indicators to capture different facets of tourist travel behavior (e.g., duration of stay in a city, spatial extent of activities, location visited and trips conducted, and mobility diversity), and examine their statistical properties across cities. An eigendecomposition approach is then introduced to better understand the interdependency of these mobility indicators and inherent variations among individual travelers. Based on the eigendecomposition results, we further employ a dimension reduction technique to describe the key characteristics of each traveler. Since the mobile phone dataset captures the nationality of tourists, we use such information to quantify the behavioral heterogeneity of travelers across countries and regions. Finally, we select a few traveler groups with distinctive mobility patterns in each city and examine the spatial patterns of their activities. Substantial differences are observed among traveler groups in their spatial preferences. The implications for location recommendation and deployment of tourism services (e.g., transportation) are discussed. We hope the study brings a synergy between classic human mobility analysis and the emerging field of tourism big data. The framework can be applied or extended to compatible datasets to understand travel behavior of tourists, residents, and special population groups in cities.

Impact of Weather, Activities, and Service Disruptions on Transportation Demand

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

Kültür ve İnanç Turizmi Kapsamında Ziyaretçilerin Seyahat Profillerinin İncelenmesi: Sille Örneği

Understanding travel behavior of visitors in a certain region is important in terms of revealing profile of those who come to that region. New tourism plans can be created according to determined travel profiles, and it will be possible to host more visitors in region with applications. Purpose of study; To analyze electronic comments of visitors to Sille, which is an important of culture and belief, and to contribute to managers by offering suggestions to develop regional tourism. In this context, answers to six questions were sought in study. The data used in study consists of 618 electronic comments covering years 2014- 2020 on website named TripAdvisor. The comments were analyzed in a data analysis program called MAXQDA. As a result of analysis, it was seen that visitors were mostly of wise type, and visits were mostly made in August and with family. While Sille's proximity to center was prominent positive opinion, the restorations were bad was leading negative opinion. The most striking location has been Aya Elenia Church and Sille has been mostly compared to Cappadocia region.

Explaining the Railheading Travel Behaviour with Home Location, Park ‘N’ Ride Characteristics, and the Built Environment to Strengthen Multimodalism

Urban planning is transitioning away from the ‘Predict and Provide’ approach that accommodates automobility and towards the ‘Demand Management’ approach that prioritises alternatives that include active, shared, and public transport and restricts the convenience of automobility. While this transition could prove a sustainable solution for urban mobility, individuals already residing within auto-dependent settings may be unwilling or unable to relocate to high urban density where the alternatives are more viable. As such, restricting the automobility of these individuals potentially leaves them stranded throughout the urban form. The ‘Multimodalism’ approach is a pragmatic alternative that provides Park `n’ Rides, Kiss `n’ Rides, and feeder transit services that ensure everyone has access to rapid public transport yet the approach receives relatively little research attention. As such, researchers, policy makers, and planners are poorly equipped to influence intended multimodal travel behaviours or discourage the unintended such as ‘railheading’ towards more distantly located PnR. In this study, the transport planning and social psychology literature is examined to develop a conceptual model of travel behaviour, and for the first time, railheading behaviour is examined at the metropolitan-scale and explained using the conceptual model. The conceptual model and research findings strengthen the theoretical and empirical foundations for understanding travel behaviour, which in turn supports planning authorities and practitioners in promoting more sustainable transport behaviour, and in preparing for an urban future where Mobility-as-a-Service, ride-hailing, ride-sharing, eScooters, and autonomous vehicles become more integrated and commonplace.

Combining GPS tracking and survey data to better understand travel behavior of out-of-bounds skiers

Our research seeks to expand existing knowledge of travel behavior and decision-making in avalanche terrain. We have done this by using GPS tracking to observe the travel behavior of out-of-bounds (OB) skiers and collecting survey data to investigate their terrain choices. We sampled participants in the field by distributing hand-held GPS units and surveys along the southern boundary of Bridger Bowl Ski Area, Southwest Montana, USA. In total, we used data from 136 participants that volunteered over the course of 19 field days, from February 2017 to February 2018. We analyzed the resulting GPS data using a GIS, and we derived terrain metrics from elevation and land cover data. We fit a multiple linear regression model using GPS track downhill starting distance from the ski area boundary as the response variable and survey responses, interaction with complex avalanche terrain (as defined using the Avalanche Terrain Exposure Scale), weather conditions, and avalanche hazard level as explanatory variables. This approach evaluates travel behavior as a function of human factors, terrain, weather, and snowpack, providing a holistic perspective on decision-making drivers. Our results show that gender (female), formal avalanche education, and perception of avalanche mitigation are statistically significant (p < 0.05) survey responses which indicate that participants travel further from the ski area boundary before descending Saddle Peak, which effects individuals avalanche terrain exposure. Downhill starting distance is also significantly correlated with time and distance in complex avalanche terrain (p < 0.05). Our results provide a case study of the terrain preferences and avalanche awareness of OB skiers and highlight specific “human factors” that are correlated with terrain selection. Two practical applications of this research are: 1) tailoring of targeted avalanche education outreach based on our results specific to the OB setting, and 2) designing new signage to illustrate the avalanche terrain near the ski area boundary for skiers who are inexperienced in the backcountry or unfamiliar with the specific area.

Understanding travel behavior: A study of school excursion motivations, constraints and behavior

A more detailed understanding of actual travel behavior is crucial to better understand travel patterns and determine the attractiveness of target markets. Educational tourism, and school excursion tourism in particular, is a relatively under researched and poorly understood segment of the tourism industry. This paper examines the influence of socio-demographics, motivations and constraints on planned and actual school excursion behavior. A total of 1,314 schools from across Australia participated in an online survey. Analysis revealed the importance of interpersonal constraints at explaining both planned and actual travel. Socio-demographics including the number of staff and students at the school, and a dedicated school excursion planner helped to explain the average length of stay of school excursions. The push motive of ‘new experiences’ positively influenced the number of overnight excursions undertaken, while the push/pull motive of ‘a reward’ helped explain longer length of stay and distance travelled, alongside socio-demographic factors. Practical implications are outlined for both destinations and attractions interested in targeting school excursions. Future research avenues are also outlined based on the research findings and limitations.

Children commute to schools in Bangalore urban and rural districts: travel pattern, behaviour and perceptions

Background: Children form vulnerable road users during their regular school commute. School children safety is not taken into consideration before construction of road network. The total number of persons injured in India during 2015 near schools or colleges or educational institutions due to road traffic accidents are 13,270 in urban areas according to National Crime Records Bureau which is quite alarming. Current study is the first attempt in both rural and urban Karnataka towards understanding travel pattern, behaviour and perceptions among school children during commute to schools. The objectives of the present study were to assess travel pattern, behaviour and perception of school children in Bangalore urban and rural districts and to assess factors associated with travel pattern.Methods: A cross sectional study was conducted using a representative sample of schools selected by simple random sampling. Data was collected using a pre-tested, semi-structured questionnaire which was analysed using SPSS version 20.Results: Nearly 86% of school children travelled less than 5 kms to reach their schools. Nearly 39% of school children travelled alone to school. Most common mode of travel to school was by walk. Nearly 27.4% did not cross the main roads safely. Adherence to road safety behaviour was insufficient. Vehicular traffic was perceived as a major apprehension by children during school commute.Conclusions: Commute of children to schools in both urban and rural part of Bangalore was unsafe with least adherence to safety measures, which might increase their vulnerability to road traffic injuries.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

Travel Probability Fields – An approach to understand travel behavior: Case study of slum dwellers in Kolkata, India

Transport is an important contributor to the well-being of individuals and communities. Lack of suitable and affordable transportation can be a significant barrier to participation of different groups of population in a developing society. The current approach for planning for informal population in developing countries such as India is sadly not based on informed policy decisions backed up by scientific investigation of travel behavior of such communities. As a result, the transport investment decisions are more political in nature, which do not adequately address the needs of the informal community in their day-to-day mobility requirements. In this context, Travel Probability Fields offer a potential analytical tool to understand the travel behavior of population under study and evolve need-based transport investment decisions particularly when the resources are scarce in the context of developing countries. The present paper is an attempt to assess the mobility needs of slum dwellers in the city of Kolkata in east India to identify their travel behavior using Travel Probability Fields (TPF) in order to arrive at scientific based transport policy intervention for urban poor. The paper based on mobility patterns of slum dwellers located in different locations in the city of Kolkata develops Travel Probability Fields for various trip purposes and travel modes in order to assess the impact of location of slums and accessibility to public transport on travel patterns.

Demand and Driver Supply Implications of Regular Public Transportation System for Policy Makers: A Case Study of Kandahar City, Afghanistan

Kandahar city of Afghanistan being one of the largest cities of the country does not have any regular system of public transportation. Unfortunately, at the moment the entire picture of the current services and future plans is unclear due to the lack of travel behavioral data including trip rate, travel time distribution, modal share and so on. More serious is that mechanism behind the behavior has not been figured out, so that no comprehensive transportation planning was established from theoretical basis. As the unprecedented study in Kandahar city, this paper attempted to carry out a travel survey with Revealed and Stated Preference (RP/SP) approach to understand travel behavior for the first time, by focusing on determinant factors of demand for regular public transportation modes and supply of drivers using discrete choice modelling approach. The results revealed that travel time and travel cost are among the most influential factors for forecasting the demand of public transport. Other socio-demographic factors such as gender, age, monthly income, and vehicle ownership are also demand determinant factors. Furthermore, consideration of women in all processes of planning and operating public transit is very essential. On the other hand, due to low income, higher working loads and some other job related factors, the drivers of paratransit system are not satisfied with their current jobs and have shown strong willingness to become bus drivers in future. Their willingness to change their jobs is effected by their personal income levels, availability of other income sources, education and some other social factors.

Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals

Rapid growth of the older population worldwide, coupled with their overreliance on automobile and its negative consequences for the environment and for their wellbeing, has encouraged research on travel behavior of this age group. This study contributes to the literature by providing an integrated analysis of the effects of sociodemographic, built environmental, psycho-social, trip, and activity space attributes on shopping trip mode choice of older adults in Helsinki Metropolitan Area. Data was collected using an online map-based survey. Two person-based activity space models were developed, in addition to the commonly used 500-m buffer, to measure activity space and built environmental attributes. Integrated Choice and Latent Variable (ICLV) models were utilized to explore modal choice. Although the use of activity space models did not significantly increase the fit of ICLV models, it provided different information. Walkability index showed a positive significant effect on walking trips in individualized residential exposure model. A positive effect on transit use or biking was found in individual home range and 500-m buffer. The shape and dispersion of activity spaces affected mode choice as well. Green space influenced the goal of being physically active which in turn affected mode choice. Three personal goals of being physically active, having cultural and social affairs, and caring for others influenced mode choice. Results indicate the priority of the use of activity space and hybrid choice models in understanding travel behavior. Findings of this study can guide policies aiming to increase the use of more sustainable modes among this age group.

A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization

In studies of human mobility, there is a need for a holistic system for collection of sensing data, management of data flows, fusion of multiple data sources, and visualization of integrated data to better understand travel behavior. We have designed and implemented a generic Future Mobility Sensing (FMS) system to serve these purposes. FMS harnesses various sensing technologies, heterogeneous multi-source data and analytical functionalities with three dedicated platforms, namely 1) the FMS Data Collection Platform, which intertwines sensing objects, machine learning algorithms and user verifications to collect high resolution, multi-day travel data; 2) the FMS Data Management Platform, which provides standardized APIs to access data stored in an interconnected data model; and 3) the FMS Data Fusion and Visualization Platform, which consolidates multi-source data to be interpreted and presented. Together, the three platforms form a mobility sensing flow to facilitate data-driven analysis and decision-making. With FMS, heterogeneous multi-source data are suitably integrated for analysis, and multi-dimensional knowledge is extracted and presented in intuitive and interactive analytical dashboards. The system is intended to be generic to support different requirements of mobility studies, such as travel surveys, as its function modules are reusable and can be customized to support a unified data collection, management, fusion and visualization process. This paper introduces the overall architecture of the FMS system and summarizes various applications that it can support. Specifically, we present a study of commercial vehicle parking in Singapore to demonstrate the capability of the system.

Measuring density and diversity to model travel behavior in Indian context

We propose Total Activity Density (TAD) and Weighted Index of Dissimilarity (WID) as indicators of density and diversity against the conventional indicators of population density and non-residential to residential ratio, respectively. The proposed indicators account for complementary land uses i.e. residential and non-residential activities that serve as origins and destinations in the home-based trip, respectively.Both the proposed and conventional indicators are measured for the city of Vishakhapatnam at NMT-TAZ level. We have validated the application of the proposed indicators by estimating linear regression models for trip length using both the proposed and conventional indicators. The analysis shows that the trip length models with TAD and WID resulted in modest or negligible improvement over the model with conventional indicators. However, the models estimated using the proposed indicators have resulted in a significant reduction in the sum of squared errors as compared to the models estimated using the conventional indicators. We, therefore, interpret that the proposed indicators of density and diversity provide a better understanding of travel behavior in the Indian context. Our study shows the inverse and significant impact of TAD and WID on trip lengths. Therefore, more dense and diverse zones are likely to result in short distance trips and encourage non-motorized modes of transport.