Travel Patterns Research Articles

Impact of land use on bike-sharing travel patterns: Evidence from large scale data analysis in China

With bike sharing flourishing around the world, researches focus on the optimisation of station layouts, but seldom explore the dynamical features of residents’ bike-sharing travel habits. Land use factors probably indicate the activities and reflect the reasons behind residents’ travel habits; however, few studies provide empirical evidences, especially with big data analysis technology. Thus, we conduct a big data-driven empirical research to clarify residents’ travel patterns and the effects of land use thereon, which is based on the full system records of one typical bike-sharing city in China. K-modes clustering method is used to identify residents’ single and daily bike-sharing travel patterns. Three multinomial logit regression models are applied to exploring the selection mechanisms of the travel patterns. Eventually, we find that with the reference of ST pattern, the TPT-ENW pattern selection is most affected by the number of accommodation service places around travel destinations and one standard deviation increase will increase relative risk of this pattern by 1.57467. The MNT-SC pattern is most affected by the number of corporations and the other five single travel patterns are all significantly affected by the number of catering service places, which indicates commuting and dining are the travel purposes of these patterns. In addition, we find a multinomial logit model with individual fixed effects can explain residents’ daily travel pattern selection better than a pooled multinomial logit model. The analysis of residents’ travel routines explains the distribution of bike-sharing mobility and provides basis for bike-sharing system policy formulation.

Exploring non-mandatory travel behavior in Jakarta City: Travel time, trip frequency, and socio-demographic influences

This study investigates non-mandatory trips in three regions of Jakarta city, namely Central, North, and West. Specifically, the focus is on examining mode choice, distance traveled, and trip frequency. The dataset used for analysis consists of 1,187 complete survey responses obtained from an on-site survey of residents conducted between December 2019 and March 2020. The results reveal distinct travel patterns across the surveyed areas. For instance, Central Jakarta exhibits a high reliance on ride-hailing services, while shorter-distance trips are more common in West Jakarta. These variations can potentially be attributed to socio-demographic factors. To gain deeper insights into travel behavior, a multinomial transport mode choice model is employed to explore the relationship between travel modes, trip characteristics, and personal/household variables. Interestingly, despite the growth of motorcycles in Jakarta, our findings suggest that older individuals are more inclined to choose walking over motorcycle travel. Additionally, trip frequency plays a role in mode choice, as frequent trips are associated with motorcycle-based travel. As anticipated, motorcycle modal share is influenced by vehicle ownership; however, our results indicate a negative impact of ownership on the likelihood of switching from motorcycle mode to alternative modes. To facilitate a shift from motorcycle use to active and public transportation, our findings emphasize the need for an effective transportation strategy that includes reducing motor vehicle ownership, creating walkable environments, and developing age-friendly public transport options.

Examining the effect of moderating variables on autonomous public van acceptance model (APVAM).

Autonomous vehicles (AVs) will soon become the primary means of transportation for millions of people. However, the introduction of these vehicles may lead to increased traffic and changes in people's travel patterns. To address this issue, one solution is to promote the use of AVs in a shared and public manner. However, the success of this approach depends on public acceptance of public AVs. If the promoters of public AVs are unaware of how people perceive this technology, it is possible that these vehicles will face failure in the market. Therefore, this paper aims to identify the factors that influence the willingness to use public AVs. To achieve this, an autonomous public van acceptance model (APVAM) was developed by adapting the unified theory of acceptance and use of technology (UTAUT2) through structural equations modeling. In this study, 824 citizens of Tehran participated in a field study and completed an online questionnaire. The research results indicated that the variable of effort expectancy indirectly affects the use of autonomous public vans (APVs), while the variables of performance expectancy, facilitating condition, hedonic motivation, and perceived PUnTrust directly affect their use. The research also found that various factors such as gender, level of education, individualism/collectivism, travel purpose, the dominant travel mode, marital status, occupation, age, household wealth, number of vehicles owned, and the price of the current vehicle of the household have a moderating effect on the APVAM. Furthermore, it was revealed that individuals who primarily use personal or public transport for their daily trips are more influenced by the perceived enjoyment of APVs in increasing their willingness to use these vehicles, compared to those whose predominant mode of travel is walking or cycling.

Does the information provider choose to promote public transport use or protect public health? A case study in China

BackgroundHealth risks from air pollution require cutting motorized private vehicle use. Providing new public travel patterns, along with appropriate smartphone-based multimodal travel information services (SMTIS) is considered an effective way to alleviate this stress. However, air pollution is not likely to be solved overnight. There is a growing demand for more detailed air pollution information accessible through mobile apps to avoid exposure to pollution, which may increase the use of private cars. So far, little is known about this contradictory behavior. MethodsBased on 1926 observations obtained from a stated preference experiment conducted in Shanghai, a mixed logit model was developed to investigate the effect of SMTIS on travelers’ mode choice behavior. Specifically, this study aims to reveal the impact of progressively providing typical multimodal information, social influence information, and air pollution information expressed in cigarette equivalents on commuters’ choice of travel modes, including private car, park-and-ride (P&R), and customized bus. ResultsInformation offered by SMTIS, including travel time, travel cost, available parking spaces, comfort level, and air pollution information, exerts significant influences on travel mode choice. Also, there is observed taste heterogeneity for such information across the population. Respondents placed less emphasis on air pollution information during commute trips than on travel time. Providing information on modal pollution exposure prevents people from using public transport. Even when air quality is good, we still observe an increase in the proportion of people choosing cars. With regard to social influence information, people were more likely to choose P&R when online word-of-mouth about P&R displayed a positive propensity, while the percentage of other travelers who had selected P&R did not exhibit a significant effect. ImplicationsThe findings provide valuable implications for the design of low-carbon traffic management policies and the deployment of multimodal information services.

Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data

Station-free bike sharing is one of the most important short-distance means of transportation. However, with the surge in the number of shared bikes, an imbalance of supply and demand in time and space caused by the disorderly parking of shared bikes has also emerged. This research combined massive spatiotemporal trajectory data of shared bikes and user travel demands to propose a feasible multi-objective optimal scheduling method. Specifically, this research presented a model that utilizes extensive order data to analyze user travel patterns, divides shared bike operation areas into internally connected communities by Geohash coding, and analyzes the shared bike dispatch hotspots in each community segment. Then, a novel model for multi-objective optimal scheduling of shared bikes was proposed based on NSGA-II. The model takes the number of transport vehicles participating in shared bike dispatching and the actual number of dispatch points as the decision variables, and its optimization goal is to reduce the cost of dispatching shared bikes and improve the utilization rate of shared bikes. The optimization effect of the model before and after the improvement of the genetic algorithm was analyzed, and the proposed optimized Pareto front solution set and optimal scheduling routes for shared bikes were given. Some of the results are noteworthy. First, the factors that affect the scheduling cost and utilization rate of shared bikes vary, so setting the variables reasonably is helpful for improving the optimization ability of the scheduling optimization model. Second, exploring the connections between shared bikes can effectively improve the scheduling efficiency of shared bikes. The research results are of great significance for optimizing dispatching routes and formulating low-carbon shared bike dispatching strategies.

Mining motif periodic frequent travel patterns of individual metro passengers considering uncertain disturbances

Periodic pattern mining is of great significance for understanding passenger travel behavior, but the previous works mainly focused on the trajectory data and the dimension of the spot/point. Besides, many uncertain factors (severe weather, traffic accident, etc.) may interfere with discovering original and accurate periodic travel patterns. This paper proposes a novel type of travel pattern called motif periodic frequent pattern (MPFP), which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics, considering, uncertain disturbances. We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data (SCD), and apply the real long-time-span experimental data from a large-scale metro system is applied. Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of “week”. Only the top 10 types of all 4 624 types account for about 95% of all motifs and the top 5 types constitute about 90%, and the MPFP of the top 3 types of motifs account for nearly 80% of all periodic patterns, in which Mono-MPFP and 2-MPFP are the main ones. The relatively stable time range of MPFP is three months, and the threshold for the optimal uncertain disturbance factor should be set at 5%. Additionally, several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.

Does the travel career pattern model work for repeat tourists?

ABSTRACT Tourism researchers and practitioners have an interest in understanding tourists’ travel patterns, and one of the most used models applied to reach this goal is the seminal Travel Career Pattern (TCP) model. However, repeat tourism is underexplored and in the present research, the authors provide an initial investigation of repeat tourists using the TCP model. Based on survey data from 500 international tourists in India the present research found that the TCP model is valuable for exploring repeat tourists’ travel patterns and motives. Within the TCP model results show that middle level motives were found to be most important for tourists with more diverse travel experience which include nature, self-development and self-actualisation. Significant relationships were also found among the origin of tourists, their travel experiences and their motivational pattern.

Stereotypes and the public acceptability of shared micromobility

Understanding the public's views and acceptability of shared micromobility (SMM) is crucial for uncovering barriers to incorporating SMM into sustainable urban mobility. This research studies stereotypes of SMM and their impacts on SMM's public acceptability. We focus on shared e-scooters and shared bikes/e-bikes in England, the Netherlands, and Sweden. We conceptualise stereotypes of SMM, developing a scale for their measurement. Factor analysis reveals that stereotypes of SMM are structured around two cognitive dimensions, namely, warmth and competence, which aligns with the stereotype content model. Warmth captures individuals' stereotyped SMM user images regarding how the users interact with other road users; competence reflects individuals' stereotyped SMM's capabilities to improve existing transport systems. Overall, stereotypes of SMM, characterised by low levels of warmth and high levels of competence, are ambivalent. Yet, they are also variable. Analysis of covariance unveils variations in SMM stereotypes across countries, sociodemographics, and travel patterns. Swedish residents, older adults, individuals from households without children, SMM users, and car-oriented individuals hold lower levels of warmth (for both types of SMM) and competence (for shared e-scooters) than their counterparts. Finally, the public acceptability of shared e-scooters is neutral, whilst that of shared bikes/e-bikes is mildly positive. Multivariate analyses show that stereotypes' warmth and competence are positively associated with individuals' acceptability of SMM, cumulatively accounting for over 50% of the variation in acceptability. Our findings help inform policies and planning on SMM. The developed scale holds the potential for evaluating stereotypes of SMM in particular and emerging transport services in general.

Policy responses and travellers’ preferences in pandemics: Evidence from Europe

We exploit daily information on flight traffic between 37 European countries from 2019 to 2022 to identify the drivers of air travel demand during and after the pandemic. By applying two-way fixed-effects regressions on daily data at the route-level, we find that the strict lockdown orders reduced bilateral flights by about 17% on average. Our results suggest that the fall in flight traffic from the first and second lockdowns was about 10% and 12%, respectively, while the third lockdown led to a massive reduction corresponding to 22%. The results also reveal that a one-unit change in the aggregate stringency index led to 0.2% decrease in flight frequency, while reducing the number of passengers by 0.7%. The empirical evidence suggests that a substantial change in travel patterns was explained by the spread of the virus itself, as well as voluntary actions based on individual preferences and fear of infection. The main findings of this paper have important policy implications for a more coherent response to any future virus outbreak as it affects airlines and the general economy.

15-min Cities: the Potential of a Medium-Sized Polycentric Latin American City.

Urban sustainability and sustainable mobility have become the central focus of sustainable development initiatives. The city of 15 models seeks to ensure that urban development is sustainable. This paper evaluates the current state of mobility and the use of sustainable transport in the specific context of the city of Antofagasta, which, due to its characteristics of urban expansion and its mining activity, has been considered an international reference case. In particular, we study how the current urban structure shapes the city's travel patterns and identify opportunities for public policy actions to focus their investment and urban intervention efforts on the essential aspects that make it possible to achieve 15- or 20-min territories. The results reveal the need for urban development initiatives to focus on improving the supply of goods and services in areas with low installed capacity, promoting the culture of micro-neighbourhoods, and encouraging cycling. In particular, only 10% of trips, for all purposes and all modes of transport, are completed in less than 15min. And about 58% of trips, for all purposes and all modes, are completed in less than 30min. In the marginal effects of the multinomial logit model, a one-unit increase in travel time increases the probability of choosing to travel by public transport by 63% and decreases the probability of walking by 41%.

Prevalence and influential factors of adopting electric motorcycles: The case of students in Hanoi, Vietnam

The development of electric motorcycles depends on the motorcycle usage rate in each country, which is crucial in forming sustainable urban development. In this study, we investigated determinants of using e-motorcycles among students in Hanoi, Vietnam. Students are the research subject due to their critical role in shaping future travel patterns. The used data were collected by surveying 290 students from three universities. We found that the prevalence of adopting electric motorcycles among students is 11%. The results of binary logit regression show that female students in Hanoi living within distances between home and school from 3 to 5 km are more inclined to utilize electric motorcycles. The perception of usefulness and environmental concern are facilitators of the adoption. However, perceived ease of use is not a significant predictor. This study plays a role as an exploratory analysis of the actual use of electric motorcycles. However, in order to have a better understanding of the motives behind the choice of electric vehicles for students and other population segments, more research in different settings is needed.

Integration of ridesharing and activity travel pattern generation

This paper proposes a mobility system serving as an innovative platform that synchronises a ridesharing model and an Activity Travel Pattern (ATP) generator, enabling a synergistic enhancement of participants’ mobility experiences. Through system-level integration, it can significantly improve the performance of ridesharing systems while also exploring the intricate relationship between ridesharing and participants’ ATPs. Numerical examples demonstrate the advantages of ridesharing systems when armed with flexible/variable announcements from the ATPs-based participants. The results show that incorporating ATP-based announcements in the system increases the chances of finding a match, and the attractiveness of the ridesharing mode for participants is improved. Despite their lower number, ATP-based participants contribute significantly to finding matches and obtaining distance savings. The study also highlights the key role of tour gyration in the performance of the ridesharing system. It incentivises participants with distant activity locations to join the ridesharing system as ATP-based participants.

Exploring intra-urban human mobility and daily activity patterns from the lens of dockless bike-sharing: A case study of Beijing, China

In recent years, the emergence of dockless bike-sharing has brought new ways of transportation, while also providing support for observing urban dynamics at a finer granularity. This study uses dockless bike-sharing data, combined with point of interest (POI) data, to explore intra-urban human mobility and daily activity patterns, using Beijing as a case study. Here, we employ spatial statistics and graph network to quantify the characteristics of travel behavior. Firstly, spatiotemporal statistics analysis was conducted to investigate the travel patterns and spatiotemporal differences of bike-sharing in Beijing. It was found that bike trips have clear peak hours and highly overlap with the subway lines, indicating that the bike-sharing system effectively completes the ‘last mile’ of travel in Beijing. Secondly, graph networks were used to reveal the core patterns and community structures of human mobility in Beijing and their connections to daily activities. Our findings indicate that there are different core structures of human mobility in Beijing, some dominated by a single core node, while others are formed by the aggregation of multiple cores. Finally, POI data was used to analyze human daily activity patterns. We analyzed the temporal characteristics and the hotspot areas of each daily activity in Beijing and found that transferring to other transportation modes is the most popular activity for people riding bikes, followed by shopping, working, and eating out. These findings provide a reference for understanding urban human dynamics and urban structure from the perspective of dockless bike-sharing.

K-prototypes 군집분석 알고리즘을 적용한 코로나19 팬데믹 전후의 국내 여행행태 비교 연구

The purpose of this study was to apply the k-prototypes algorithm, capable of handling both continuous and categorical variables, to gain insights into the changes in travel patterns between the pre- and peri-COVID-19 pandemic periods. For the empirical analysis, data from the National Travel Survey conducted from 2018 to 2021 were extracted, specifically focusing on respondents in Seoul, Gyeonggi, and Incheon (n=21,161). A comparison between the pre- and peri-COVID-19 pandemic periods revealed significant changes, including an increase in visits to short-distance destinations, a decrease in the number of travel companions and travel spending, an increased reliance on personal experiences as a source of travel information, and a decrease in travel among the elderly population. Furthermore, a comparative analysis of travel pattern clusters demonstrated that six travel pattern clusters, namely ‘long-distance travel group’, ‘high-frequency travel group’, ‘friend/couple group’, ‘travel group with children’, ‘high-spending group’, and ‘group traveler’, remained consistent during the peri-COVID-19 period, except for the ‘group traveler’ cluster. The implications of the research findings are discussed, along with suggestions for future research directions.

Teleworking behavior pre-, during, and expected post-COVID: Identification and empirical description of trajectory types

Teleworking is a major lifestyle change whose adoption expanded substantially during the COVID-19 pandemic, and which might permanently change the working habits of some. To understand how much during-COVID teleworking behavior will “stick” in the post-pandemic world, this study explores pre-, during-, and expected post-COVID teleworking frequency trajectories based on during-COVID survey responses, which were weighted to represent the population shares of non–/non-usual/usual teleworkers (TWers). Taking respondents’ self-reported expectations at face value, post-COVID teleworking frequency will be 2.6–3.3 times that of pre-COVID, but we suspect that those expectations are over-optimistic. We developed a typology based on the teleworking trajectories, and identified eight mutually exclusive and collectively exhaustive worker segments: Climbers (13.6%), Plateauers (8.4%), Cappers (12.6%), Stable unables (43.1%), Stable non-choosers (13.5%), Stable non-usual TWers (1.5%), Stable usual TWers (3.2%), and Reducers (4.0%). We conducted three exploratory factor analyses and identified latent attitudinal constructs including 11 general, 7 work-related, and 7 telework-related attitudes. We profiled the eight segments regarding various attitudes and sociodemographic traits to reveal their potential connections to teleworking behavior, and to suggest further explorations with respect to residential location, vehicle ownership, and other travel patterns. Specifically, we see research needs pertaining to residential relocation and household vehicle ownership changes for segments expecting net increases in teleworking frequency between pre- and post-COVID. We also observed heterogeneity of teleworking adoption/frequency choices, attitudes, and travel behaviors among (tele)worker segments. Overall, this study deepens our understanding of post-COVID teleworking behavior changes, and provides a starting point for understanding the heterogeneity among (tele)workers.

Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.

Exploring Melbourne Metro Train Passengers’ Pre-Boarding Behaviors and Perceptions

The focus on sustainable transportation has increased interest in promoting sustainable modes of transport, such as rail. Understanding train passengers’ behaviors and perceptions is essential to enhance their travel experience and increase train ridership. Pre-boarding behaviors and perceptions are crucial in shaping the overall train travel experience. However, there are limited studies that have developed a systematic framework for investigating train passengers’ pre-boarding behaviors and perceptions. This paper examines the train passenger’s pre-boarding behaviors and perceptions about the station and platform. The study adopts a mixed-methods approach by developing a pre-boarding decision framework and combining it with questionnaire surveys to explore passengers’ behaviors and perceptions on the platform before boarding. A total of 429 valid responses from Melbourne metro train users were used for analysis. Descriptive statistics and correlation techniques were applied to identify patterns and relationships. The findings reveal common pre-boarding behaviors and perceptions. Furthermore, the study uncovers factors influencing these behaviors and perceptions, such as passenger demographics, travel patterns, and specific trip characteristics. For example, carrying large items and travel frequency significantly impact passengers’ travel experience in the pre-boarding phase. Waiting time, group travel, carrying small items, gender, and age group also significantly impact some pre-boarding behavior variables. Travel time, on the other hand, makes no significant impact on any of the pre-boarding variables that we examined. This research provides valuable insights for rail service operators and policymakers to enhance the pre-boarding experience, optimize station design, and improve passenger satisfaction.

Understanding changes in travel patterns during the COVID-19 outbreak in the three major metropolitan areas of Japan

Unlike the lockdown measures taken in some countries or cities during the COVID-19 outbreak, the Japanese government declared a “State of Emergency” (SOE) under which people were only requested to reduce their contact with other people by at least 70%, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three major metropolitan areas of Japan: (I) weekends and holidays prior to the COVID-19 outbreak, (II) weekdays prior to the COVID-19 outbreak, (III) weekends and holidays before and after the SOE, (IV) weekdays before and after the SOE, (V) weekends and holidays during the SOE, and (VI) weekdays during the SOE. It was also found that travel patterns might have started to change from March 2020, when most schools were closed, and that the mobility patterns after the SOE returned to those prior to the SOE. Interestingly, we found that after the lifting of the SOE, travel patterns remained similar to those during the SOE for a few days, suggesting the possibility that self-restraint continued after the lifting of the SOE. Moreover, in the case of the Nagoya metropolitan area, we found that people voluntarily changed their travel patterns when the number of cases increased.

A Comparative Analysis of Museum Accessibility in High-Density Asian Cities: Case Studies from Seoul and Tokyo

We investigated the relationship between urban accessibility of museums in the urban spaces of Tokyo and Seoul within limited travel distances. Similarities and differences were identified in the museum accessibility between the two cities. The urban accessibility of museums was set as the dependent variable, calculated via space syntax. For the spatial accessibility of museums, five walking ranges (1000–2000 m) were set as independent variables, with a distance of 250 m as the basic unit. Data normality and independence of the derived data were checked, and polynomial curve fitting was performed to interpret the accessibility of museums in each city. A comparative analysis was conducted on museum accessibility. The results show areas with a high concentration of museums in Tokyo and Seoul partially deviated from the center of the urban hierarchy. The urban and spatial accessibilities of museums in both cities quantitatively correlated with limited travel distances. Museum visitors in Tokyo were more likely to have relatively free-flowing routes in the city. The museums in Seoul had a lower overall accessibility than those in Tokyo, and travel patterns and routes to these museums were likely to be restricted when located in urban areas and consequently resembled a forced movement pattern.

Investigating occasional travel patterns based on smartcard transactions

Public transportation (PT) studies often neglect non-routine trips focusing predominantly on commuting. However, recent research revealed that occasional trips make up a substantial portion of public transport journeys, and traveler preferences for non-routine trips diverge from their preferences for regular commuting. We study non-routine trips based on a database of 63 million smartcard (SC) records of PT boardings made in Israel during June 2019. The characteristics of these trips are revealed by clustering PT users’ boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority in Israel and constitute less than 15% of the riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified as occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of day, and land use composition around the location of trip origin. We discuss the potential applications of our approach in the landscape of diverse flexible PT.