Influence Travel Behavior Research Articles

Assessing Non-linear Influences of Urban Layout on Driving Travel Distance: A Mumbai Case Study

Introduction This study investigates the impact of built environment factors on travel behavior in Mumbai, India, focusing on the non-linear effects on driving distances. The rapid urbanization and complex urban of Mumbai constitute present challenges for sustainable transportation, necessitating a deeper understanding of how urban planning influences travel behavior. While demographic factors have often been highlighted in travel studies, this research prioritizes the role of built environment factors. Materials and Methods The study utilized Gradient Boosting Decision Trees (GBDT) to analyze household travel survey data from Mumbai, capturing the non-linear relationships between built environment variables and driving distances. Partial dependence plots were used to visualize these effects, and the relative importance of each variable was assessed to identify key determinants of travel behavior. Results The analysis identified trip time as the most influential factor in determining driving distances, followed by built environment characteristics, such as proximity to commercial areas and intersection density. Socio-demographic factors were found to have a comparatively low impact. Non-linear relationships were observed, such as the stabilization of driving distances beyond certain thresholds of block density and proximity to bus stops. Discussion The findings challenge the traditional emphasis on demographic factors in explaining travel behavior, highlighting the significant role of urban form. The study reveals that specific built environment factors, such as accessibility and connectivity, play a crucial role in shaping driving behavior in a rapidly urbanizing city like Mumbai. These results suggest that urban planning strategies should prioritize these factors to reduce car dependency. Conclusion This study underscores the importance of the built environment in influencing travel behavior in Mumbai, particularly in reducing driving distances. The insights gained offer valuable guidance for urban planners and policymakers aiming to promote sustainable mobility in rapidly developing cities. Further research is recommended to validate and expand upon these findings in other urban contexts.

Coupled Impact of Points of Interest and Thermal Environment on Outdoor Human Behavior Using Visual Intelligence

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points of interest (POI) and thermal environments on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering the driving factors behind human behavior in outdoor spaces. First, Yolo v5 and questionnaires were employed to obtain crowd activity intensity and preference levels. Subsequently, target detection and clustering algorithms were used to derive variables such as POI attractiveness and POI distance, while a validated environmental simulator was utilized to simulate outdoor thermal comfort distributions across different times. Finally, multiple classification models were compared to establish the mapping relationships between POI, thermal environment variables, and crowd preferences, with SHAP analysis used to examine the contribution of each variable. The results indicate that XGBoost achieved the best predictive performance (accuracy = 0.95), with shadow proportion (|SHAP| = 0.24) and POI distance (|SHAP| = 0.12) identified as the most significant factors influencing crowd preferences. By extrapolation, this classification model can provide valuable insights for optimizing community environments and enhancing vitality in areas with similar climatic and cultural contexts.

Modelling Passengers’ Travel Behaviour for Shared Autonomous Vehicle and Bus Considering Heterogeneity

The popularisation of autonomous vehicles will give rise to a new business model called shared autonomous vehicle (SAV). SAVs may attract a large number of passengers and lead to a decline in the share of buses, which can be interpreted by exploring passengers’ travel behaviour when confronting the SAV and bus modes. Thus, this paper addresses the SAV and bus passengers’ travel behaviour, aiming to examine the factors influencing travel behaviour and revealing the characteristics of SAV passengers. We classified passengers using latent class cluster analysis and modelled passengers’ travel behaviour based on confirmatory factor analysis and mixed logit model. The findings indicate a variation in travel preferences among different classes of travellers. Short-distance travellers pay less attention to travel time. Non-short-distance PT travellers are most likely to be affected by service attributes (waiting time, travel time and travel costs). Non-short-distance private car travellers are more likely to become early SAV adopters. Passengers travelling for short distances may be more likely to choose SAV, which reveals the potential of SAVs to become a first and last mile connection for public transport. Passengers lack trust in SAVs, which will affect their promotion.

The Impacts of Remote Work and Attitudinal Shifts on Commuting Reductions in Post-COVID Melbourne, Australia

This paper analyses the commuting frequencies and modal choices of travellers in Melbourne, using a dataset reflecting travel behaviour before and after COVID-19. A factor analysis of 63 latent variables identified seven key factors, which were used in cluster analysis to examine the relationships between latent constructs, land use, and socio-demographic variables, as well as commuting behaviours. The analysis categorised white-collar employees into four groups based on their remote work engagement, with socio-demographics and industry type as key factors. The analysis shows that female clerical and administrative workers who worked from home during the pandemic are now returning to the office, raising gender equality concerns within society. Meanwhile, the education and training sector mandates office attendance despite the feasibility of remote work, as universities prioritise in-person attendance to attract more international students, impacting societal norms around telecommuting. The analysis revealed that saving on commute costs, reducing travel time, and spending more time with family are the among the primary factors influencing travel behaviour among white-collar employee’s post-pandemic. The study found that the decrease in public transport trips is associated with increased telecommuting rather than service dissatisfaction, especially among Central Business District (CBD) employees who still rely on public transport. This trend suggests that the CBD sector’s growing acceptance of remote work is reducing daily commutes, which puts additional pressure on public transport providers to sustain and improve their services. A decline in service quality could further reduce ridership, highlighting the need for consistent, high-quality public transport. Furthermore, the study found that increased telecommuting is likely to reduce car trips in the future, especially among healthcare and social workers who prefer driving due to public transport’s unreliability for their demanding schedules. By examining variables like the advantages and disadvantages of working from home, convenience, accessibility, and the efficiency of public transport, this study enhances the understanding of transport behaviour and underscores the need to improve public transport reliability to support sustainable cities as remote work grows.

Understanding changing patterns in travel behaviour to support domestic tourism recovery and resilience

ABSTRACT The COVID-19 pandemic has severely impacted the tourism sector, disrupting travel and centralising a shift towards domestic tourism. This study examines the changing preferences and demands of travellers to promote the recovery and sustainability of domestic tourism. The research conducted 1 034 online surveys with the general public to analyse their travel behaviours and preferences concerning domestic tourism activities willing to participate in. The findings indicate that despite limited disposable income, a significant portion of the South African population possesses discretionary income and is interested in participating in tourism activities. Travel preferences are influenced by various socio-economic and demographic factors. Changing patterns in destination choices and activities are also noted, which influence travel behaviour and related tourism consumption/ demand for products and experiences, marketing and impacts. Respondents are knowledgeable of and attracted to many domestic tourism products in South Africa, with social and nature-based tourism being prominent. Monitoring domestic tourism trends can inform marketing strategies and product development. Additionally, awareness and communication strategies should be improved, and incentives should be provided to encourage local travel.

Associations between the built environment and travel to higher-order centers in Chinese rural areas

Encouraging travel to higher-order centers is important for enhancing the quality of life of rural residents and promoting rural development. Although the literature shows that the built environment influences travel behavior, few studies have explored the relationship between the built environment and the frequency of travel to higher-order centers, particularly in rural contexts. Even less attention has been given to the synergetic effects that occur across built environment elements and county heterogeneity. Using data from 659 respondents in three Chinese counties collected between 2019 and 2020, we applied multilevel ordered logistic models to explore the associations between the rural built environment and travel frequency to higher-order centers (including townships, counties, and city centers). The results show that rural residents' travel frequency to higher-order centers decreases with increasing distance from such centers. The relationships between built environment elements and travel frequency to higher-order centers differ according to the level of such centers. Moreover, built environment elements exert synergetic impacts on travel frequency to higher-order centers. The association of the built environment with travel frequency to higher-order centers is moderated by the developmental levels of counties. These findings suggest that interventions in the rural built environment are important for ensuring the enhancement of rural resident travel behavior and access to better services, which, in turn, benefits rural revitalization.

Navigating active Transit: How built environments shape commuting and leisure journeys

This study offers a nuanced investigation into the complexities of active travel behavior in low and middle-income countries (LMICs), with a specific focus on the Mumbai Metropolitan Region (MMR) in India. Utilizing a dual-method approach, the research employs Poisson regression models to analyze the frequency of active commuting and Cox proportional hazards models to explore the duration of such travel. The study reveals that urban design elements, socio-economic factors, and individual demographics collectively shape travel behavior, often in unexpected ways. Contrary to prevailing assumptions, walkable neighborhoods and enhanced public transit accessibility do not universally encourage active commuting. For example, while increased bus stop availability boosts the frequency of work-related active trips, its impact on leisure travel is minimal. Similarly, higher intersection density, generally associated with greater walkability, unexpectedly leads to longer travel durations. The study also finds that proximity to urban centers like the Central Business District exhibits a dual influence—promoting active commuting for work but slightly reducing it for leisure activities. These multifaceted findings underscore the need for more context-specific, multidimensional approaches in urban planning and policy formulation. Future research aims to expand upon these insights through quasi-experimental methods to better understand the socio-cultural, economic, and environmental intricacies influencing travel behavior in LMICs.

Revisiting built environment and travel behavior: A natural experiment accounting for residential self-selection

Numerous studies have attempted to quantify the genuine impact of the built environment factors on travel behavior which is not through residential self-selection (RSS). Nevertheless, the interactions among built environment variables have rarely been included in investigations. To address this gap, this study utilizes a natural experiment formed by the unique urban housing context in Beijing, China. It specifically focuses on resettlement housing families, whose place of residence is largely exogenous, effectively mitigating the bias caused by RSS. By using a gradient boosting decision trees model, this study investigates the combined influence of the built environment on household vehicle kilometers traveled (VKT). The results show that the net built environment contributes about 64% to household VKT, and RSS plays a trivial role (less than 2%) in the relationship between the built environment and household VKT in Beijing. However, the built environment variables influence household VKT in different ways for resettlement households and commercial housing households. These results increase our understanding of how the built environment influences travel behavior.

Traffic pollution as a privilege: An intersectional approach to environmental justice and transport emissions

The growing concern over air pollution and community health, demands a comprehensive understanding of the political nature of transport-related emissions. This paper approaches how structures of power influence travel behaviour and, consequently, how they shape emissions from mobility in Barcelona. To comprehend how the intersection of gender, age, and migration background influences air pollution, we use a travel survey to build a set of models that are known to theoretically engage with intersectionality studies. Results show that identities tied to structures of power promote a greater contribution to air pollution than other mechanisms. That is, middle-aged European-born men have the most NOx-intensive mobility practices. In contrast, people that embody intersectional experiences of oppression such as misogyny, racism, or ageism, have lower emission levels. Considering these results, we argue that policies which acknowledge the unequal distribution of responsibilities present an opportunity to reduce emissions from transportation, while guaranteeing fairness and advancing environmental justice.

How do adolescents experience the decision-making process on travel mode choice?

Active travel can contribute to multiple health benefits in youth. Previous research has identified several factors influencing travel behavior. This study investigates how adolescents process these factors during their decision-making process on travel mode choice. Semi-structured interviews were conducted with 13 adolescents (11-14 years) and analysed using deductive-inductive thematic analysis. Four themes were generated from which the decision-making process on mode choice was conceptualized according to adolescents. The step-by-step process in which travel mode options were gradually reduced, was dependent on the context (Theme 1), the perceived availability of travel mode choice options, which was influenced by factors beyond the individual's perceived control (Theme 2), and on adolescents' pros/cons assessment to identify the most convenient option. Adolescents' habitually used travel mode influenced the decision at several stages throughout the process (Theme 4). To promote healthy travel behaviors, interventions should consider contextual circumstances, balance between enabling autonomous choices and promoting advantages of active travel modes, address individual travel modes and include practices that foster conscious processing of decision-making.

RETRACTED ARTICLE: Sustainable regional rail system pricing using a machine learning-based optimization approach

AbstractRegional transport pricing is indeed very vital in urban settings where the transportation network is spread out across large areas and can influence travel behavior and the sustainability of cities. Therefore, in addition to existing pricing systems, such as flat fare, distance-based fare, and zonal pricing, this study proposes a sustainable approach to regional rail system pricing using rent prices and a transportation affordability index. The proposed model aims to reduce commuters' overall travel distance in order to reduce air pollution and maintenance costs for public transportation vehicles. Rent-based pricing encourages people to rent houses in regions that shorten their travel distances and fill a gap in the literature on regional rail system pricing by dealing with the decentralization of the cities. A two-step clustering and non-linear optimization modeling approach are proposed based on face-to-face surveys with regional rail system passengers. For various clusters of stations, rent per income rates and rental-based ticket prices were obtained. Furthermore, a sensitivity analysis is conducted to evaluate different conditions of the affordability index and rent prices in the studied regions. Compared to the current pricing system, ticket revenues increased by 3.88% and 1.68% in rent-based pricing.

Dynamics of Campus Travel Behavior under the COVID-19 Pandemic

The COVID-19 pandemic has shown to be a global challenge that, in addition to other effects, has influenced travel behavior. This study examines factors affecting academic travelers’ mode choice before and during the pandemic and factors contributing to sustainable transportation on campus. By examining their travel patterns and behaviors, we contribute to understanding transportation preferences and identifying opportunities for sustainable transportation on university campuses. Studying academic travelers is crucial as they are significant daily travelers with a substantial impact on transportation systems and the environment. Understanding their mode choices helps transportation planners and policymakers promote sustainable transportation options. The literature has identified influential factors in making trips to university campuses, including age, gender, accommodation, cost, and travel time. However, cross-sectional studies involving comprehensive variables are lacking and the influence of the COVID-19 pandemic on transportation has not been thoroughly evaluated. To address this gap, the current study aims to evaluate novel variables, including intra-transport modes, entry permits, accessibility, parking availability, occupations, level of study, travel purpose, and visit frequency. The University of Isfahan, accessible by all modes of transport, was selected as the study area. After analyzing the questionnaire and variables using SPSS software (IBM SPSS Statistics for Windows, Version 22.0 Released 2013), travel behavior was studied by discrete choice models and the models’ coefficients were estimated using NLOGIT. The finding demonstrated that using private modes (taxi, private vehicle, and active modes) increased in response to the pandemic, while using public modes (bus or subway) represented a decline. Before and during the pandemic, most people who had the same trip purpose shifted from taking the bus to using private vehicles and active transportation. Generally, people became more inclined to walk on campus during the pandemic. This study aimed to examine the travel behavior of academic travelers, who possess diverse travel choices compared with typical commuters, thus providing valuable insights into how the broader population might respond to different transportation options. The findings offer a novel perspective for university and city planners, enabling more informed decisions regarding sustainable development in campus areas.

Factors Affecting Travel Behaviour Change towards Active Mobility: A Case Study in a Thai University

This study investigates the factors influencing travel behaviour change towards active mobility (AM) in the context of a suburban university in Thailand. The research framework involves developing a qualitative questionnaire for a SWOT analysis. The outcomes of this analysis inform the creation of a quantitative questionnaire called the Stated Preference Survey. This survey collects opinions from 400 randomly selected individuals representing various demographics in the study area. The collected data are then analysed using a binary logistic regression model to explore the relationship between independent variables (such as demographics, travel characteristics, and perceptions of infrastructure and amenities) and the likelihood of individuals adopting AM. The results indicate that the demographic variables, such as gender and income, played a significant role, with males and higher-income individuals showing lower likelihood of adopting AM. The presence of well-designed infrastructure with aesthetic features and rest areas along pedestrian and bicycle paths positively influenced behaviour change. Safety and security measures, including protective measures against motorcycles on pedestrian paths, installing CCTV cameras, and safe crossings, also played a crucial role. However, promotional efforts through media and applications did not significantly contribute to behaviour change. Policymakers and urban planners can use these insights to effectively encourage AM.

Exploring attitudinal factors influencing modal shift: a latent class analysis of Danish commuters

Governments advocate for a modal shift from motorized transport modes to active modes. Various political approaches can be adopted to affect travel behavior and patterns. However, interventions spread across the entire population offer limited opportunities to achieve behavioral change. Furthermore, attitude has been shown to cut across demographic characteristics and strongly influence the conducted travel behavior. Therefore, a latent class analysis including significant sociodemographic variables and value-based attitudes concerning factors influencing transport, settlement, and additional priorities was performed. The study objectively identified five classes of Danish commuters with the same preconditions in terms of commuting distance but with clear differences in attitude and transport modes. Each latent class represents a unique combination of characteristics, which indicates the need to design target group-specific interventions to optimize the chances of influencing travel behavior. In particular, a group of malcontented motorists demonstrating a high intention to change exhibit negative feelings toward car travel and thus appear to act in contravention of their attitudes. In contrast, a class of immovable motorists was found, a class of beneficial commuters and finally two cycling dominated classes of passionate cyclists and environmentalist cyclists. Finally, this study has emphasized that similar attitudes can lead to dissimilar behaviors and that the same behavior can be exhibited for various reasons. We deduced how transport mode choice is influenced by various factors, with habit as one of the strongest, as those with strong habits seem disinclined to information about alternatives and call for “harder” policy interventions. The findings emphasize the importance of targeted interventions tailored to specific commuter groups to encourage modal shifts towards sustainable transportation.

Analyzing the effects of congestion on planning time index – Grey models vs. random forest regression

Travel time and its predictability level are important factors influencing travel behavior and the role of traffic congestion as an important factor in the unreliability of travel time is undeniable. Traffic congestion is divided into two categories: Recurring and Non-recurring. In this paper, the effect of recurring congestion, defined as the ratio of the traffic speed over one hour to the free flow speed, will be investigated on the reliability of travel time in terms of the planning time index (PTI) on a 1.467-mile segment along the IS-64 freeway in Chesapeake, Virginia. To do so, two methods have been analyzed in this study: the grey models (GM) and the random forest regression (RFR). By using mean absolute percentage error (MAPE) as a criterion to judge, RFR could show a better and more satisfying performance in predicting PTI values when congestion changes. In the following, to make prediction results of RFR more understanding and easier to use, bagging and bootstrapping are used to improve the model results and more accurately predict the PTI. Then, the outputs were drawn using scatter plots for both peaks separately. Analyzing graphs has shown that for congestion values in the range of 1 to 0.9, PTI is reliable in both peaks. When congestion starts to decrease from 0.9 and reaches 0.7 or 0.75, depending on peak type, PTI is moving in the unreliable area, but it isn't extreme. Finally, when the congestion value becomes smaller, the rate of change in PTI in both peaks increases.

Travel Demand Management in an Auto Dominated City: Can Travel Behaviour Be Nudged in the Kingdom of Saudi Arabia?

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city.

Iterative urban design and transport simulation using Sketch MATSim

ABSTRACT New transport technologies have historically influenced how cities are shaped, while urban form is known to influence travel behaviour. But this reciprocal relationship is seldom operationalized in practice due to the disciplinary gap between urban design and transport planning. The two are often linked through a ‘predict and provide’ workflow, which is problematic in the context of emerging technologies such as AVs. This paper argues for a more iterative design and transport simulation workflow, through design experiments. One design experiment is illustrated using Sketch MATSim, to investigate the impact of network design on the performance of shared automated vehicles.

Estimating Travel Choice Probability of Link-Based Congestion Charging Scheme for Car Commuter Trips in Jakarta

The likely effects on car commuters of enforcing congestion charging using the link-based corridor method include that they may shift to public transport, divert their route of travel, or decide not to travel to the related area. However, most recent research has focused mainly on the choice of modes. This paper examined the travel choices of private car commuters resulting from the congestion charging scheme set to be implemented in Jakarta, Indonesia. The scheme is intended to replace the current odd-even strategy. It is imperative to study all possible mutually exclusive alternatives faced by car commuters. A web-based e-survey was used for data collection, employing the stated preference method. The discrete choice multinomial logit model was chosen to analyze the data. A total of 401 of the 2125 respondents to the e-survey questionnaire link, evenly distributed to all areas of Greater Jakarta, were sampled in this study. The sample respondents who traveled by car, passing through the eight designated corridors, were analyzed. NLOGIT6 software was used to analyze the parameter of attributes, the probability of alternatives chosen, and the marginal effects of congestion charging on such corridors, employing the multinomial logit model (MNLM). One surprising finding was that the load factor and taxi fares were not significant, indicating that the level of in-vehicle overcrowding is not a concern of respondents, and taxi services are not a substitute for car travel. Another surprising finding was that income variables and job type do not significantly influence travel behavior. In terms of the probability of commuters to continue to travel by car when link-based congestion charging is imposed, only around half of the car travelers were willing to pay the congestion levy and pass through congestion charging corridors. The probability of car travelers diverting onto alternative roads is high, i.e., around 16.82% to 22.88%, while the probability of car travelers shifting to mass transportation is 17.69%. When interpreting direct marginal effects, there is a change in the probability of all travelers choosing to use private cars through the congestion charging corridor of −0.0338, or a decrease of −3.38% for every IDR 1000 increase in the congestion charging levy rate, ceteris paribus.

Motivation and Basic Psychological Needs Satisfaction in Active Travel to Different Destinations: A Cluster Analysis with Adolescents Living in Germany.

Active travel in adolescence contributes to improved health outcomes. Self-Determination Theory suggests that motivation and basic psychological needs influence travel behavior. Person-centered approaches can examine interrelationships of these constructs underlying travel behavior. The aim of this study was to investigate (i) which clusters can be identified in adolescents, (ii) whether clusters explain overall active travel behavior, (iii) whether clusters were associated with travel mode to various destinations or distance, and (iv) whether differences across clusters appear regarding sex/gender, age, and weight status. The sample included 517 (263 male, 254 female) adolescents from Germany, aged 11-15. Self-organizing maps analysis identified six clusters from nine input variables: intrinsic motivation, integrated regulation, identified regulation, introjected regulation, external regulation, amotivation, autonomy satisfaction, competence satisfaction, and relatedness satisfaction. The most beneficial cluster regarding active travel demonstrated highest basic psychological needs satisfaction and autonomous motivation with low controlled motivation and amotivation. The most vulnerable cluster was characterized by generally low levels of motivation except for external regulation and amotivation. Clusters were not associated with distance to school, friends/relatives, shopping facilities, or leisure facilities. The findings support the importance of high quality and high quantity of motivation for active travel in adolescents.

Sustainable Mobility and the Environment: How our transportation choices shape our future

This article is an overview of how travel behavior and urban mobility are shaping and influencing each other. The paper is structured in two parts. The first one presents the factors that influence travel behavior and in the second one, a few examples of good practices and unsuccessful projects are analyzed in order to determine how community collaboration affects the planning and decision-making process. The behavior patterns are influenced by socio-demographic factors, land use, transportation, or travel costs. Lifestyle represents one of the important factors in choosing transportation services. Thus, understanding the user’s mobility patterns is a complex process and varies depending on the context and the needs.