Travel Patterns Research Articles

Applying Data Analytics to Analyze Activity Sequences for an Assessment of Fragmentation in Daily Travel Patterns: A Case Study of the Metropolitan Region of Barcelona

Sequence analysis is a robust methodological approach that has gained popularity in various fields, including transportation research. It provides a comprehensive way to understand the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to mobility surveys offers valuable insights into the sequencing of travel activities and modes, shedding light on the complex interrelationship between individuals and their travel choices and the built environment. Sequence analysis allows us to examine travel behaviors as dynamic processes and reveal the underlying structure and evolution of travel patterns over a day. Here, we describe a data analytics approach that enables the identification of common travel patterns and the exploration of variations across demographic groups or geographical regions. We propose a method for discovering similarities in travel behavior segments from diaries included in travel surveys in order to refine transport policies for selected segments. Unfortunately, the data collected by the authorities in the analyzed travel surveys do not include family structure, which seems critical in explaining the segmentation of travel sequences.

How do young people move around in urban spaces?: Exploring trip patterns of generation-Z in urban areas by examining travel histories on Google Maps Timeline

Generation Zs have become dominant in the urban areas of various metropolitan cities worldwide, including in developing countries such as Indonesia. Their number has been estimated to be more than one-quarter of the population, and it is expected that they will become the largest generation in the coming years. Their contribution to the number of trips in urban areas has been significant, making it important to examine their travel patterns. The aim of this study had been to investigate the travel patterns of Generation Zs in the urban areas of developing countries such as Indonesia, where public transportation systems are not yet adequate and urban mobility is dominated by private vehicles (motorcycles and cars). This study had explored the travel behavior of Generation Zs, specifically their trip chains and mileage. This study had attempted to fill the research gap in travel pattern analysis using a travel diary approach, which is usually conducted over a relatively short period. Typically, a travel diary survey is only conducted over a few days or weeks since the approach requires significant time and is costly, while it is also susceptible to reporting and transcription errors. By utilizing travel history data from Google Maps Timeline, this study had observed the movement history of 434 Generation-Z individuals aged 17–24 years for 153 days (from August to December 2022). This approach is considered an alternative or complement to the travel diary survey, which had been commonly used. In the analysis, we had also considered gender, financial ability, home location, and workplace or campus location to discover how many trips had been made and how far people had traveled for their daily activities, both on weekdays and weekends. There had been a total of 213,094 trips observed, 63% of which had been made by motorcycle, and 35% of which had been made by car. (The remainder had been via walking, cycling, and public transportation, at only 2% overall.) The results had shown that men had made more daily trip chains and had had higher mileage than women, both on weekdays and weekends. Overall, the number of daily trips made on weekdays had been higher than on weekends. However, the research had also found that the higher the financial ability of the Generation-Z individual, the more daily trips they had made, especially on weekends. Even those with financial ability above IDR 2.4 million (>USD 156.37) had made more daily trips on weekends than on weekdays. The regression analysis results had shown that gender and financial ability had significantly influenced daily trips and mileage. Home location and workplace or campus location had also impacted daily trips and mileage. In addition, the results of this study also fill the gap in previous research on the travel behavior of Generation Z. With the characteristics of the built environment that still lack adequate public transportation systems and the dominance of private vehicles in urban mobility, the research findings show significant differences with developed countries that already have adequate public transportation systems.

ASO Visual Abstract: Variations in Travel Patterns Affect Regionalization of Complex Cancer Surgeryin California.

ASO Visual Abstract: Variations in Travel Patterns Affect Regionalization of Complex Cancer Surgeryin California.

A multi-country panel study of behaviour, perceptions and expectations during different stages of the COVID-19 pandemic

It is widely accepted that the COVID-19 pandemic has dramatically changed travel patterns since 2020, largely due to restrictions on people’s movement and work-fromhome practices. A large number of studies have been conducted to understand such changes from a trip maker’s perspective, using different types of mobility data collected across the world. This study uses survey panel data on travel behaviour and activity participation collected between May 2020 and November 2020 in the United Kingdom, Australia, Colombia and South Africa using a consistent survey approach. We identify a role for three key underlying latent constructs, namely 1) concerns about COVID-19, 2) approval of government interventions and 3) scepticism towards COVID-19 measures. Using a hybrid choice model, we study the role of these constructs in explaining stated travel choices in two hypothetical post-pandemic scenarios. The model results show significantly different perceptions towards COVID-19 concerns and government handling of the COVID-19 pandemic (including restrictions) across countries. The model estimates show a clear influence for the latent constructs in explaining stated behaviour in the hypothetical post-pandemic scenarios across the four countries, where this is also impacted by lockdown stringency levels as well as socio-demographics.

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.

O.5.1-7 Travel patterns and CO2 emissions of recreational league play in football and handball in Germany - an explorative study

Abstract Purpose Recreational sports are commonly recommended for their health-enhancing effects. But broadening the perspective on planetary health raises the question of how they impact the environment. While there is a body of evidence on the impacts of professional sports on planetary health, there is currently a lack of studies investigating the sustainability of grassroots level sports, in particular team sports in recreational leagues. This study set out to explore travel patterns in different recreational leagues in the sports of football and handball in Germany. Methods We derived data including club location and game schedules for amateur football and handball teams in the region of Erlangen, Germany, from official websites of sport federations. Using google maps, we calculated the travel distances for every match-up in season 2022/23 and highlighted the distribution of teams in divisions by mapping. Results Even on a low recreational level, significant travel distances are accrued by teams over a season. In general, playing in a more competitive league results in longer travel distances for football teams. On average, women teams cover longer travel distances compared to men’s teams. Playing handball results in more travel compared to playing football. The current set-up of divisions can be improved to limit travel distances. Conclusions Despite some limitations (e.g., number of included divisions or difficulty translating the distances into CO2 emissions), this investigation sheds light on the environmental impact of recreational league play. The sheer number of sports teams suggests the importance of investigating recreational league play and supporting sport federations to optimize game schedules to limit travel emissions. Funding Source The study was not funded.

Analysis of Disaster Risk on Expressways for Foreign Tourists to Japan Using Rental Cars: Study Using Central Nippon Expressway Pass

The travel patterns of foreign visitors to Japan are changing from group travel, which was the mainstream to date, to individual travel, and there has been an increase in the number of cars rented by visitors as a means of transportation. The use of rental cars improves access to rural areas and is being promoted as a means of transportation. However, if a natural disaster occurs while driving a rental car, then the individual may face difficulties owing to a lack of local support. Therefore, the traffic volume on the roads used by foreign visitors to Japan during certain periods needs to be estimated in advance as well as the natural disaster risks that exist on those roads. In this study, we utilized CEP data managed by the Central Nippon Expressway Company in Japan to identify the risk of disaster for foreign visitors to Japan who use expressways with rental cars. The CEP data has usage records of foreign visitors to Japan who used rental cars on expressways in the Chubu region of Japan during the period from 1 April 2018 to 31 March 2019. Using this data, we calculated the risk of earthquake and landslide disasters. Result of this analysis, it is clear that the risk of earthquake disasters is high on expressways in the Tokai region, and the risk of landslide disasters is high on expressways in mountainous areas.

Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks

Deep learning has achieved great successes in performing many visual recognition tasks, including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city travellers. Recently, estimating city-level travel patterns using street imagery has been shown to be a potentially valid way according to a case study with Google Street View (GSV), addressing a critical challenge in transport object detection. This paper presents a compressed deep network using tensor decomposition to detect transport objects in GSV images, which is sustainable and eco-friendly. In particular, a new dataset named Transport Mode Share-Tokyo (TMS-Tokyo) is created to serve the public for transport object detection. This is based on the selection and filtering of 32,555 acquired images that involve 50,827 visible transport objects (including cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles) from the GSV imagery of Tokyo. Then a compressed convolutional neural network (termed SVDet) is proposed for street view object detection via tensor train decomposition on a given baseline detector. The method proposed herein yields a mean average precision (mAP) of 77.6% on the newly introduced dataset, TMS-Tokyo, necessitating just 17.29 M parameters and a computational capacity of 16.52 G FLOPs. As such, it markedly surpasses the performance of existing state-of-the-art methods documented in the literature.

Access distance to e-scooters: Analysis of app use and trip data in Stockholm

Users’ access distance to shared micromobility services is an important component of travel patterns, a determinant of travel choices, and input to determining service catchment areas. Users’ willingness to walk to shared micromobility vehicles is increasingly relevant as policymakers regulate shared free-floating e-scooters to designated parking zones. This paper proposes a novel approach to analyze access distances of e-scooters users based on e-scooter app use and trip data for Stockholm, Sweden. Euclidean access and map-based walking distances are derived from the distances between the location where the users opens the app to search for an e-scooter and the trip’s origin. Variations in access and walking distances are analyzed based on time of day, day of week, proximity to public transportation, and geographical distribution. Users walk on average 185 m and have an active walking time of 2.3 min with a median value of 95 m and 2.1 min. Shorter walking distances are observed for trips during the morning and lunch hours compared to the afternoon and at night. Furthermore, users walk slightly longer during the weekend compared to weekdays. Access distances are shortest within a 0–100 m radius to the nearest public transportation station. The suggested catchment area radius for shared e-scooters ranges from 128 m to 203 m, based on the 75th percentile of access distances. A policy implication is the importance of planning parking zones for e-scooters very close to public transportation to encourage multimodal trips.

Variations in Travel Patterns Affect Regionalization of Complex Cancer Surgery in California.

Regionalization of complex surgical procedures may improve healthcare quality. We sought to define the impact of regionalization on access to high-volume hospitals for complex oncologic procedures in the state of California. The California Department of Health Care Access and Information Database (2012-2016) identified patients who underwent esophagectomy (ES), pneumonectomy (PN), pancreatectomy (PA), or proctectomy (PR). Geospatial analysis was conducted to determine travel patterns. Clustered multivariable regression was performed to assess the probability of receiving care at a high-volume center. Among 25,070 patients (ES: n = 1216, 4.9%; PN: n = 13,247, 52.8%; PD: n = 3559, 14.2%; PR: n = 7048, 28.1%), 6575 (26.2%) individuals resided within 30min, 11,046 (44.1%) resided within 30-60min, 7125 (28.4%) resided within 60-90min, and 324 (1.3%) resided beyond a 90-min travel window from a high-volume center. Median travel distance was 13.4 miles (interquartile range [IQR] 6.0-28.7). On multivariable regression, patients residing further away were more likely to bypass a low-volume center to undergo care at a high-volume hospital (odds ratio 1.32, 95% confidence interval 1.12-1.55) versus individuals residing closer to high-volume centers. Approximately one-third (29.7%) of patients lived beyond a 1-h travel window to the nearest high-volume hospital, of whom 5% traveled over 90min. While hospital mortality rates across different travel time windows did not differ, surgery at a high-volume center was associated with an overall 1.2% decrease in in-hospital mortality. Regionalization of complex cancer surgery may be associated with a significant travel burden for a large subset of patients with complex cancer.

Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport

AbstractIncentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

A Method of User Travel Mode Recognition Based on Convolutional Neural Network and Cell Phone Signaling Data

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application.

Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space.

Understanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal variations in England, this study aims to provide some insights into the most important risk parameters. We used spatial clusters developed in Jahanshahi and Jin (2021 Transportation 48, 1329-1359 (doi:10.1007/s11116-020-10098-9)) as geographical areas with distinct land use and travel patterns. We also segmented our data by time periods to control for changes in policies or development of the disease over the course of the pandemic. We then used multivariate linear regression to identify influences driving infections within the clusters and to compare the variations of those between the clusters. Our findings demonstrate the key roles that workplace and commuting modes have had on some of the sections of the working population after accounting for several interrelated influences including mobility and vaccination. We found communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries and those who rely more on public transport for commuting tend to carry a higher risk of infection across all residential area types and time periods.

ASO Author Reflections: Variations in Travel Patterns Affect Regionalization of Complex Cancer Surgery in California.

ASO Author Reflections: Variations in Travel Patterns Affect Regionalization of Complex Cancer Surgery in California.

Spatio-Temporal Network Analysis of the Impact of Mega Event-Based Development: The 2018 Winter Olympic Host City (Gangneung, South Korea) Case

Background and objective: Although mega-events are generally expected to have both short-and long-term lasting favorable effects on host cities, few quantitative studies have investigated this impact based on demand-oriented evidences and through a phased data collection. This study examines the case of Gangneung, a Winter Olympic host city, in order to identify the extent to which the effects of the Olympics are durable.Methods: City map survey was conducted 48 times for 527 visitors for a year and half for visitation pattern observation. In order to investigate the temporal impact of the Olympic event and seasonality, attraction visitation network for seven periods are compared based on using Network density comparison analysis. In addition, a regression analysis-a QAP (Quadratic Assignment Procedure) was followed to examine different factors affecting network formation.Results: As the Winter Olympics were taking place mainly around the Olympic Park, the visitation network results displayed very strong connections among the Olympic park, the Northern coast, and the Southern city’s downtown. However, except the major connections, all other connecting strengths were presented with noticeably weaker strengths when compared to the pre-Olympic period. After the Olympics, the edge strengths were restored to levels similar to those of the pre-Olympic period and, the most noticeable change was the reduced gap between the strongest and weakest attraction group, indicating that the edge strengths tend to distribute equally. Another change was that the strengths of connections to the Olympic Park became stronger, indicating that the Olympic legacy was included as one of the touristic attractions. The results of the following season indicated that the connections among the Northern and Eastern coasts, the Western historical sites, and the Southern downtown area remained strong and connections to the downtown became more prominent with new powerful attraction points.Conclusion: The longitudinal observations revealed that there is a noticeable durable impact of hosting the Olympics. To some extent, Olympic hosting and the KTX railway construction seem to have changed tourist visitation patterns. Although the results warn that the expectation of spillover and durable effect can be hasty, the following results imply that the redevelopment project induced by a mega event has brought the changes and reorganized the travel pattern in small city. This also provides evidence that mega-event impact sometimes lags behind the city government and local community’s schedule, which explains the need for a long-term evaluation.

Q-Learning Based Commercial Electric Vehicles Scheduling in a Renewable Energy Dominant Distribution Systems

The increasing prevalence of commercial electric vehicles (CEVs) for ferrying passengers has become popular in recent times. This scenario strains the distribution system (DS) during the system charging cycle. However, the design becomes beneficial if the CEVs support the utility based on the needs. This work aims to efficiently schedule the operational modes of CEVs to decrease power loss in the DS in the presence of distributed generation (DG). The CEVs are modeled by considering the uncertainty in their travel patterns. This work proposes a combined approach to reduce power loss and enhance voltage quality by adopting internet of Things (IoT) enabling technologies. This method combines Voltage Stability Index (VSI), Q-learning method, and enhanced moth flame optimization (EMFO) for efficient operation of DS. The DS considers IoT-enabled EV charging stations (EVCSs), DGs, and CEVs for 3-way communication. Various case studies are presented to show the efficacy of the proposed technique compared to existing methods in the literature.

Risk of imported malaria infections in Zanzibar: a cross-sectional study

BackgroundZanzibar has made substantial progress in malaria control with vector control, improved diagnosis, and artemisinin-based combination therapy. Parasite prevalence in the population has remained around 1% but imported infections from mainland Tanzania contribute to sustained local transmission. Understanding travel patterns between mainland Tanzania and Zanzibar, and the risk of malaria infection, may help to control malaria importation to Zanzibar.MethodsA rolling cross-sectional survey linked to routine reactive case detection of malaria was carried out in Zanzibar between May 2017 and October 2018. Households of patients diagnosed with malaria at health facilities were surveyed and household members were tested for malaria using rapid diagnostic tests and a sub-sample by quantitative PCR (qPCR). Interviews elicited a detailed travel history of all household members who had travelled within the past two months, including trips within and outside of Zanzibar. We estimated the association of malaria infection with travel destinations in pre-defined malaria endemicity categories, trip duration, and other co-variates using logistic regression.ResultsOf 17,891 survey participants, 1177 (7%) reported a recent trip, of which 769 (65%) visited mainland Tanzania. Among travellers to mainland Tanzania with travel destination details and a qPCR result available, 241/378 (64%) reported traveling to districts with a ‘high’ malaria endemicity and for 12% the highest endemicity category was ‘moderate’. Travelers to the mainland were more likely to be infected with malaria parasites (29%, 108/378) than those traveling within Zanzibar (8%, 16/206) or to other countries (6%, 2/17). Among travellers to mainland Tanzania, those visiting highly endemic districts had a higher odds of being qPCR-positive than those who travelled only to districts where malaria-endemicity was classified as low or very low (adjusted odd ratio = 7.0, 95% confidence interval: 1.9–25.5). Among travellers to the mainland, 110/378 (29%) never or only sometimes used a mosquito net during their travel.ConclusionsStrategies to reduce malaria importation to Zanzibar may benefit from identifying population groups traveling to highly endemic areas in mainland Tanzania. Targeted interventions to prevent and clear infections in these groups may be more feasible than attempting to screen and treat all travellers upon arrival in Zanzibar.

Identifying human mobility patterns using smart card data

ABSTRACT Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.

A data-driven travel demand model to predict electric vehicle energy consumption: focusing on the rural demographic in the UK

ABSTRACT This paper presents a 7-day Travel Demand Model (TDM) for UK rural areas to aid the Electric Vehicle (EV) transition in these regions. Utilising data from both the UK Census Survey and UK National Travel Survey (NTS), private passenger vehicle travel patterns for a rural village in the Peak District National Park (UK), were modelled. This model is adaptable to any rural community within the UK, requiring only publicly available information on households and vehicles for that community. Using a novel approach through the development of lifestyle scenarios to understand the required household activities, the TDM incorporates five different trip purposes as the building blocks for a vehicle’s activity. Over a period of one week, 13,520 miles were driven by 84 vehicles across 49 households, that shows an EV fleet serving this community would consume 3562 kWh energy per week.

Can I Stop? Considering Opportunities to Influence Multidisciplinary Factors That Result in Crashes

In previous decades significant gains were made in reducing the number of road traffic related fatalities in Victoria and Australia. However, in absolute terms this reduction started plateauing despite substantial investment in road safety countermeasures. This trend is not well aligned with the goal to achieve zero fatalities by 2050. Through practical traffic management experience, a study tour of nine countries and a literature review, we have gained valuable insights into the changing dynamics of roads. We have identified opportunities to improve road safety by adapting to larger/heavier vehicles, denser traffic, changing travel patterns and evolving demographics. These opportunities include: vehicle-centred crash avoidance in addition to crash consequence mitigation strategies; reduced traffic complexity; reduced cognitive workload; and targeted dynamic information of inclement weather and traffic conditions. Improved road surface friction, braking, tyres and vehicle stability can reduce crashes and their impacts. This article aims at stimulating broader discussions among experts for the development of next-generation road safety strategies.