Travel Behavior Patterns Research Articles

ENVIRONMENTAL PLANNING AND DESIGN: EXPLORING URBAN RESILIENCE THROUGH E-HAILING

Sustainable cities strive for balance in environmental health, economic vitality and social equity through efficient and resilient urban planning. Crucial to this balance, sustainable transportation systems reduce carbon emissions, ease traffic congestion and promote clean energy. Among various sustainable transportation options, e-hailing has gained global popularity, offering convenient rides through smartphone apps. This quantitative research focuses on exploring the usage patterns of e-hailing services among university students at the main campus of Universiti Sains Malaysia. A total of 392 university students were surveyed using stratified random sampling, and SPSS analysis revealed a moderate positive correlation between attitudes towards e-hailing and satisfaction with safety, price, convenience and availability. Furthermore, regression analysis confirmed significant relationships between attitude and satisfaction levels. By investigating how travel behaviour patterns and attitudes towards e-hailing influence student satisfaction, the study aims to understand e-hailing’s role as an alternative mode of transportation in enhancing overall transportation service satisfaction. These insights offer valuable guidance for transport planners, e-hailing companies and university administrations seeking to improve transportation options and student satisfaction.

Unraveling urban dynamics: Scaling laws of human mobility across the cityscape divide

The scaling laws of human mobility are one of the key theories of human geography. They also have important policy implications for urban-rural integration and regional economic growth. Although scaling laws of human mobility have been widely discussed, the allometric growth between urban and rural areas is overlooked. This study aims to fill the gap by investigating scaling laws and power-law distribution across urban and rural areas during urban growth with individual people's trajectory information of 13 million mobile phone users. The analysis identified a turning point at a population of 1.8 million in the Greater Bay Area, where the growth rate of aggregate travel demand stabilizes. The results show that human mobility exhibits segmented allometric growth, with superlinear growth transitioning to sublinear beyond this turning point. The disparities of travel behaviour patterns between urban and rural populations increase as cities grow. These findings would enhance the existing knowledge of the allometric scaling of human mobility.

A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data

A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data

Exploring the Factors Accelerating the Electric Motorcycle Adoptions: Insights from Theory of Planned Behavior and Travel Behavior

Indonesia is a densely populated country where most people use motorcycles for mobility. With increasing carbon emissions, Indonesia plans to migrate conventional motorcycles toward electric ones by 2040. However, the adoption process of electric motorcycles is relatively slow, considering that the number of electric motorcycles is still far from the government's target. This study aims to investigate what factors influence the adoption process of electric motorcycles in Indonesia. Based on 906 samples, an analysis was conducted using a hybrid choice model on willingness to pay more, which considered three components: socio-demographics, Theory of Planned Behavior (TPB), and travel behavior patterns. The results showed that all three components significantly affect the willingness to pay more. Individuals who are older, highly educated, high-income, use public or environmentally friendly transportation, and have a low frequency of mobility for work purposes are more likely to purchase an electric motorcycle. The results of this study provide a new perspective in the unique context of electric motorcycle adoption in Indonesia and conditions that still need improvement when related to the government's long-term targets. This research will be helpful for governments and manufacturers by providing the characteristics of people who are more likely to purchase an electric motorcycle.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

Characterising travel behaviour patterns of transport hub station area users using mobile phone data

Understanding the travel behaviour of transport hub users is vital for improving transport services. Although previous research has explored passenger behaviour in and around transport hubs, there is a lack of comprehensive studies on travel patterns within hub station areas. To this end, this study harnessed mobile phone data to analyse the travel behaviour within a hub station area, using Beijing South Railway Station as a representative case. A series of recognition criteria was set up based on spatio-temporal information to categorize users within the station area. We categorised station area users into transport hub users (THU) and non-transport-hub users (NTHU) and examined their distinct travel patterns. The results reveal distinct travel patterns for THU and NTHU. NTHU are predominantly concentrated in and around the station area. THU, however, display longer trip distances and more widely distributed endpoints. In terms of travel time distribution, NTHU show evident morning and evening peak phenomena, while the travel time distribution of THU fluctuates throughout the day. The study also employed association rule analysis to illustrate strong connections between the station area, Beijing's core, central areas, and even urban fringe areas. The results reveal that the station area is most closely connected to the core and central areas of Beijing, from where many users are attracted to the station area. In addition, some urban fringe areas also have strong connections with the station area. These findings inform urban transport planning and personalised travel services.

How far are cities from the x-minute city vision? Examining current local travel behavior and land use patterns in Montreal, Canada

In the past three years the 15-minute city planning concept has captured the attention of policy makers and the public worldwide. Some regions have included it in their planning goals and others modified it to 30 min to make it more attainable in their local context. The goal of this research is to measure whether the 15- or 30-minute city goals align with current travel behaviour and land use patterns in the North American context using Montreal, Canada as the case study. In doing so, we look at which destinations are cultivating local travel patterns consistent with the 15- and 30-minute city concepts and identify any unique qualities of these areas using the 2018 Montreal origin-destination travel diaries. Our bivariate analysis finds very few destinations in Montreal where a high number of trips and high percentage of trips are ending using a sustainable mode of transport and below the 15- or 30-minute travel time threshold. We further investigate the land use patterns that align with the 15- and 30-minute city to recommend realistic planning goals and policy interventions that match the North American context. The findings from this research can be of interest to transport professionals and policy makers trying to implement the 15- or 30- minute city concepts to their regions.

Which factors influence the use of shared and privately-owned e-scooters in the city of Madrid? Implications for urban mobility

Micromobility using fully-electric two-wheeled vehicles is increasing in cities worldwide. E-scooters, whether shared or privately-owned, provide short door-to-door trips by facilitating the first/last mile stage of the journey. They are expected to improve livability in cities by reducing harmful emissions and space occupation. In this respect, understanding travel behavior and usage patterns is essential to regulate them appropriately. The purpose of this study is to determine individuals' sociodemographic variables, mobility-related attributes, and latent constructs influencing e-scooter usage. To that end, an individual-level model is estimated to explain the adoption and frequency of use of both shared and private e-scooters based on survey data. The research takes the city of Madrid as a case study, and contributes to a deeper understanding of the differences in the use of privately-owned and shared e-scooters, with a particular focus on the influence of mobility habits and attitudinal variables. The study is complemented with some insights on shared e-scooter usage at the trip-level, which shows the substitution caused on walking trips, and their limited ability to promote modal shifts from the private car. Finally, the research provides valuable implications for urban dynamics and feedback for policymakers and transport planners.

Perspectives on e-scooters use: A multi-year cross-sectional approach to understanding e-scooter travel behavior in Portland, Oregon

Unique travel behavior patterns are observed as shared electric scooters (e-scooters) provided by private operators expand into U.S. cities. Three separate years of e-scooter ridership survey data from the Portland Bureau of Transportation’s E-scooter Pilot Programs were analyzed to ascertain the multi-year cross-sectional and demographic characteristics of e-scooter riders. A binary logistic regression model, descriptive statistics, and multiple regression model are used to analyze e-scooter mode substitution, trip purposes, and travel distance from 2018 to 2020 in Portland, Oregon. Since the introduction of e-scooter in 2018, respondents have been less likely to use their previous transportation, and especially vehicle trips were consistently replaced with e-scooter trips during three different periods of analysis. In 2020, utilitarian trips, work/school trips, and trips for accessing transit stops replaced recreation trips as the primary trip purpose. The travel distance model shows that e-scooters can help to reduce vehicle miles traveled (VMT) by replacing car and ride-hail/taxis trips. With the result of this research, this study supports shared e-scooters as a viable transportation mode in the future that can achieve several policy goals, such as climate change, congestion, first/last mile connector to transit, and equity.

ASSESSING THE IMPACT OF TRANSIT-ORIENTED DEVELOPMENT ON RESIDENTS’ QUALITY OF LIFE IN NORTHERN MALAYSIA

Transit-Oriented Development (TOD) is gaining wide acceptance by many state’s governments in Malaysia due to its potential to create a liveable neighbourhood with enhanced mobility. Therefore, this study aims to assess the impact of TOD on the residents’ quality of life in Malaysia northern states. The data for this study were gathered from a survey on 360 residents who used the Northern KTM commuter train service. Descriptive and inferential statistics including chi-square test and PLS-SEM technique was performed to analyse the data and produce the findings. The findings of this study shown that there were significant differences in travel behaviour patterns (companions, frequencies, and walking durations) with respect to respondents’ travel purposes. Moreover, it was revealed that land-use diversity and walkable design as important TOD principles that contribute to their quality of life. The findings of this research would serve as a base but critical information to direct future National Estate Development Plan.

Attributes of modal choice in an industrial-based urban area: A case study on Savar Paurashava

Road transportation does an excellent job of connecting Savar's inner and outer areas, but it might be difficult for commuters to decide which mode of transportation to employ because residents of Savar come from a wide range of socioeconomic backgrounds. It is necessary to do in-depth research on the characteristics of people in the study region who choose different modes of transportation to achieve the goal of selecting a certain mode for their travel. Where a person lives can have a significant impact on their typical patterns of travel behavior. People who live closer to the center of things have access to a wider variety of amenities than those who live further away, and as a consequence, their travel patterns are distinct from one another. The study's findings indicate that several factors, including income, age, gender, and vocational qualities and goals, influence a commuter's preferred method of transportation. When it comes to selecting a mode of transportation, the most predictable factors to take into account are trip time and cost. After conducting research, it was found that most excursions are made from this place to Motijheel, Tejgaon, and Savar Export Processing Zone for their services, businesses, and jobs, respectively.

Comparing the Travel Behavior of Older Users Between Station-Based and Dockless Bike Sharing Systems

As the population worldwide continues to age, providing improved mobility services for older people becomes increasingly crucial. In recent years, the maturation of bike sharing has led older people to progressively incorporate it into their everyday lives. In this study, station-based bike sharing (SBBS) and dockless bike sharing (DBS) are investigated in relation to older population mobility. This research relies on the fusion of multiple data sources, including SBBS and DBS trip records, point of interest (POI), and meteorological conditions in Nanjing, China. A zero-inflated negative binomial regression model is used to compare the travel behaviors among various age groups with the exposure variable. SBBS outperforms DBS in relation to age equity, while diverse travel behavior patterns are exhibited between the age groups. Although younger users are the majority of users, older users travel more frequently than younger users in bike sharing services. Also, older users are more likely to use shared bikes near the religious and health POIs, as well as in higher population density areas. This study provides beneficial insights into the transport equity and service improvement of bike sharing systems.

Heterogeneity in mode choice behavior: A spatial latent class approach based on accessibility measures

We propose a method to estimate mode choice models, where preference parameters are sensitive to the spatial context of the trip origin, challenging traditional assumptions of spatial homogeneity in the relationship between travel modes and the built environment. The framework, called Spatial Latent Classes (SLC), is based on the integrated choice and latent class approach, although instead of defining classes for the decision maker, it estimates the probability of a location belonging to a class, as a function of spatial attributes. For each Spatial Latent Class, a different mode choice model is specified, and the resulting behavioral model for each location is a weighted average of all class-specific models, which is estimated to maximize the likelihood of reproducing observed travel behavior. We test our models with data from Portland, Oregon, specifying spatial class membership models as a function of local and regional accessibility measures. Results show the SLC increases model fit when compared with traditional methods and, more importantly, allows segmenting urban space into meaningful zones, where predominant travel behavior patterns can be easily identified. We believe this is a very intuitive way to spatially analyze travel behavior trends, allowing policymakers to identify target areas of the city and the accessibility levels required to attain desired modal splits.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ∼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020–20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the “post-COVID” era.

Promoting sustainable and personalised travel behaviours while preserving data privacy

Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters’ carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addresses these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype.

How do the socio-economic indicators influence the travel behaviour pattern?: Case study Amman-Jordan

Limited research has been conducted in developing countries on travel behavior and its prediction. This study aimed to investigate if socio-economic conditions affect travel behavior patterns in Amman, Jordan, and propose regression models for trips defining the contributory factors. A total of 681 interviews were conducted with households (210) and workplace employees (335), assessing behavioral travel styles in two neighborhoods with different characteristics. Compared to residents of high-income areas (HIA), residents of low-income areas (LIAs) travel more by all modes of transportation; in LIA and HIA, the trip rate per person was (2.2) and (2.0), respectively, while in low-income and high-income areas, the number of trips per household was 5.14 (153.6 minutes) and 3.7 (155 minutes). Most household trips in low-income neighborhoods, mainly for education and work, were made on foot, while private cars were more common in high-income areas. For trips related to the office and shops, the private car was the most common mode of transportation. In low-income neighborhoods, shared taxis were commonly used for household and shop trips, and buses were often used for commutes. School and university students, as well as household size, provided valid trip predictions. Employees can predict work trips to the office, customer visits, and shop-related trips.

Exploring the Spatial-temporal dynamics of travel patterns and air pollution exposure of E-scooters

Dockless mobility has been the biggest disruptive force in the shared mobility industry solving the “first-last” mile issue. With their high adoption levels combined with little to no regulation regarding their usage, these users have been driving along with motorized vehicles exposing them to major concerns. The users are exposed to high levels of traffic-related air pollution (TRAP) due to their direct exposure to vehicle exhaust. This study aims to understand the spatial and temporal dimensions of this emerging transportation mode in terms of travel behavior patterns, geographical aspects of travel, interactions between the travel route taken with the existing vehicle traffic, and resulting air pollution and exposure. The travel behavior patterns are evaluated through spatial-temporal analysis of a sample of e-scooter trip data collected in 2018 in the City of Austin and an online travel behavior survey. The analysis identified areas with peak usage, and peak ridership time. The survey results found the e-scooter user demographics to be mostly white males, in the 26–45 age range, with an undergraduate degree and working full-time. Secondly, key responded in influencing the use of an e-scooter are trip length, connectivity to transit, congestion and parking issues, and pollution reduction. Thirdly, e-scooter predominantly replaced personal vehicles and shared ridership in case of home-to-work trips and replaced walking for connecting to transit stops. The exposure to TRAP was obtained by integrating the spatial-temporal dynamics of e-scooter trips with spatial-temporal dynamics of pollutant concentrations modeled from traffic. Exposure analysis found peak exposure levels during midday and evening periods focused in the Central Austin area. This area houses the University of Texas campus and several neighborhoods with lots of shopping, restaurants, bars, and live music avenues. The findings are useful for policymakers and planners when planning for infrastructure changes air pollution control measures, incentive programs, and policies to motivate shared mobility.

Daily travel behaviors and transport mode choice of older adults in Mexico City

Our paper adds to the aging mobilities literature by addressing the need for more empirical studies in the Global South and utilizing the 2017 Household Origin-Destination Survey (HODS 2017) to broadly understand the mobility of older adults in Mexico City. We use descriptive statistics to reveal travel behavior patterns and construct a multinomial logit model to understand variations in mode choice of older adults in Mexico City. Our analysis does provide an emphasis on differences by income class, gender, and neighborhood level access to public transportation. Results uncovered transport-related inequalities as poorer older adults travel for longer periods of time compared to their wealthier peers. In terms of travel behaviors, we found that 40.5% of older adults reported using public and paid transit, 32% walked, 26.5% drove, and only 1.2% biked. Also, we learned that when older adults have access to good public transit infrastructure, they use it, regardless of car ownership status. Also, mobilities resulting from gender differences among older adults showed that older females are less likely to use structured public transit, walking, and biking in comparison to older males. Lastly we outline research limitations and recommendations for future transport policies.

Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars

The ever-increasing number of automobiles, especially private cars, has irrevocably altered social life, revolutionized travel experience, and reshaped the face of urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while human mobility and travel behavior by private cars are still little understood. To bridge this gap, we employ a private car trajectory data set collected from real-world urban environments. This article provides a new perspective on human mobility, with a focus on private cars, by investigating mobility patterns and capturing the spatiotemporal evolution of urban hot zones from people’s arrive–stay–leave (ASL) behavior. Understanding these hot zones and travel behavior patterns is necessary when considering changes or improvements in urban networks. Decisions impacting the safety and emissions impacts of urban travel, including, for example, design improvements at curves or to reduce left-turn conflicts, improvements to pedestrian facilities, and the placement of alternative energy refueling stations, will rely on similar analyses across many different urban areas. Our article provides a proof of concept, and we then outline new research opportunities and challenges involving private car trajectory data.Along with the increased process of urbanization and industrialization, recent years have witnessed an ever-increasing number of automobiles, especially private cars <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , also known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passenger cars</i> , those with less than seven seats and usually registered to individuals for personal use. Compared to floating cars <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , private cars constitute the vast majority of automobiles. For instance, the nationwide ownership of automobiles in China exceeded 263 million by the end of 2019 <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , and more than 88% of these vehicles (225 million) were registered to individuals. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(a)</xref> , ownership of private cars exhibited a growing trend from 2013 to 2019. As seen in Sivak’s technical report <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , per capita car ownership in the United States was 0.756 in 2015 and increased to 0.766 by 2016. According to <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> , in the last two decades, private cars accounted for more than 75% of passenger transport activity in the European Union. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(b)</xref> , even though a slightly decreasing trend is expected by 2030, it is still expected to be larger than 72%.

Study protocol to explore the social effects of environmental exposure and lifestyle behaviours on pregnancy outcome: an overview of cohort of pregnant women study

IntroductionA growing number of international studies have highlighted the adverse consequences of lived experience in the first thousand days of pregnancy and early life on the probability of stillbirth, child...