The structure of joint leisure trips: analyzing two-person leisure trips of Dutch students

This paper investigates the spatial structure of students’ leisure trips and to what extent locational characteristics of social network partners influence decisions about their joint leisure activities and travel. To this end a survey was held among university students asking them about details of their last leisure trip made with a friend. Cluster analysis suggests that four typical leisure trip patterns can be derived. Three clusters seem to be determined by the residential locations of ego and alter. Depending on the residential distance between ego and alter, leisure trip distances are either short for ego and alter (if residential distance is very short), somewhat longer for both (if residential distance is slightly longer) or long for at least one partner (if residential distance is large). A fourth cluster includes cases with long leisure trips for both partners, independent of residential distance, representing cases where specific destinations are visited. A more detailed analysis of travel distance suggests that travel distance depends on size of the residential municipality, residential distance and objective and perceived quality of leisure facilities such as cafés, bars and restaurants. Overall, our study provides support for the idea that leisure trip decisions should be understood on the level of social network ties (i.e. ego and alter) rather than based on individual characteristics only. Also, it provides support for the idea that a focus on single ties, rather than on the structure of social networks as a whole, increases our insight in leisure trip decision making.Keywords: Social network links, Social-recreational travel, Spatial context, Students     

New spatial patterns of mobility within the metropolitan area of Madrid: Towards more complex and dispersed flow networks

Changing processes in metropolitan areas are producing more extended, discontinuous, dispersed and fragmented spaces. As a result, spatial patterns of mobility are changing too. By using data from daily mobility surveys, this paper analyses the processes of peripherisation of mobility (more suburban trips), dispersion of flows and increase of trip length and travel time in the metropolitan area of Madrid.     

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.     

Do rural older adults take fewer trips than their urban counterparts for lack of a ride?

Aging and the presence of one or more illnesses result in limited travel for many adults age 65 and over. Yet, the need to get to essential, social, and non-emergency medical destinations endures. At some point in their life, older adults become dependent on family/friends, or rely on for-profit/not-for-profit transportation services for their mobility needs, while some do not go on certain trips. Researchers have studied out-of-home activity and mobility of older adults using data on trips taken. There is a gap, however, in understanding trips not taken in the older adult population in rural versus urban locations. Our objectives in this paper are: (i) to investigate unmet travel needs of older adults by relying on responses for trips not taken; (ii) to examine how personal abilities, living situation, and socio-demographic factors are associated with trips not taken to various destinations; and (iii) to compare the likelihood of trips not taken due to lack of a ride in urban versus rural locations across the age and income spectrum. Our data come from a phone survey conducted across the province of Alberta, Canada, in 2017–18 (n = 1390). We specify ordinal logistic models where the dependent variable is how often a respondent did not undertake a trip due to not having a ride to various trip destinations. We find that rural seniors are more likely to not take trips compared to older adults in cities, holding all else equal including driving cessation, worsening health, and disability. Rural seniors who live alone or in low-density housing are also more likely to not take trips compared to urban older adults. Household income, however, tempers these location preferences. Our findings suggest that rural older adults can be supported through income transfers, community-based low-cost travel, and moving to higher-density residential locations.     

Revealing intra-urban travel patterns and service ranges from taxi trajectories

As an important transport tool, taxi plays a significant role to meet travel demand in urban city. Understanding the travel patterns of taxis is important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of taxi trips to characterize travel patterns, while it may be more appropriate to explore taxi service strategies on seasonal, weekly or daily time scale. Therefore, intra-urban taxi mobility is investigated by examining taxi trajectory data that were collected in Harbin, China, while 12-week corresponding to 12-month is chosen as the sampling period in our study. The multivariate spatial point pattern analysis is firstly adopted to characterize and model the spatial dependence, and infer significant positive spatial relationships between the picked up points (PUPs) and the dropped off points (DOPs). Secondly, the points of interest (POIs) are identified from DOPs using the emerging hot spot detection technique, then the taxi services and movement patterns surrounding POIs are further examined in details. Moreover, our study builds on and extends the existing work to examine the statistical regularities of trip distances, and we also validate and quantify the impacts posed by airport trips on the distance distributions. Finally, the movement-based kernel density estimation (MKDE) method is proposed to estimate taxis' service ranges within three isopleth levels (50, 75 and 95%) between summer/weekday and winter/weekend from taxi driver's perspective, and season as well as temperature factors are identified as the significant effect within certain service range levels. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.     

The equity impacts of bus rapid transit: A review of the evidence and implications for sustainable transport

The paper offers an analysis of empirical evidence on the equity impacts of operational Bus Rapid Transit (BRT) systems in the Global South. The focus is on vertical equity, i.e. whether BRT systems achieve progressive benefits for poorer segments of the population. Findings from Africa, Asia, and Latin America all suggest that BRT does offer significant benefits to low-income groups, in terms of travel time and cost savings, access enhancement, and safety and health benefits. However benefits are often skewed toward medium-income users and thus less progressive than they might be. Two primary reasons for this are insufficient spatial coverage and inappropriate fare policies. While many features of BRT potentially allow it to deliver pro-poor outcomes, such outcomes only materialize if BRT implementers pay specific and sustained attention to equity. The paper identifies key issues that need to be addressed to steer BRT implementation toward more socially sustainable outcomes—including better integration with other transit, paratransit, and nonmotorized transport services, and with the housing sector.     

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Traffic-related carbon dioxide (CO₂) emissions have become a major problem in cities. Especially, the CO₂ emissions induced by taxis account for a high proportion in total CO₂ emissions. The availability of taxi trajectory data presents new opportunities for addressing CO₂ emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO₂ emissions. This paper investigates trip-related CO₂ emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO₂ emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO₂ emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.     

Residential location and daily travel distances: the influence of trip purpose

This paper investigates the extent to which residential location influences daily distance travelled if travel purposes are differentiated. Statistical multilevel models are applied to Swedish National Travel Survey data from 2005–2006. Travel purposes are categorized by considering time–spatial constraints and hypothesized factors of personal freedom of choice. Results indicate that the influence of residential location on daily distance travelled is highly conditional on trip purpose in a nationwide Swedish context. Although statistically significant proportions of the variation in daily distance travelled to work, on service errands, and on weekdays were dependent on residential location, daily travel distances for leisure activities and on weekends varied greatly among people living in the same neighbourhood. From a policy perspective, these results suggest that measures intended to alter the built environment to reduce the volume of travel will be most efficient as regards work trips, while trips taken during free time are unlikely to be much affected. In addition, the multilevel models applied reveal several important interactions between the variation in travel distances across residential locations and individual characteristics of which researchers should be aware, especially when examining service trips.     

Factors associated with disability paratransit's travel time reliability

This paper identifies some of the characteristics of trips and pick-up and drop-off locations that are associated with paratransit's travel time reliability. Following convention, reliability has been defined as the inverse of variability. Four measures of travel time variability have been used to examine reliability: Standard Deviation, Percent Variation, Misery Index, and Buffer Index. Regression models have been used to estimate these four variables with trip data from Access Link, the paratransit service provided by NJ TRANSIT pursuant to the Americans with Disabilities Act (ADA). A number of characteristics of the pick-up and drop-off locations as well as selected characteristics of the trips were used as independent variables of the models. The statistical significance of the independent variables varied depending on which measure of reliability was estimated, but a few variables were consistently associated with reliability in all four models. These variables were trip distance, booking type, winter season, density of motor vehicle crashes in pick-up and drop-off locations, and whether pick-ups occurred in suburban bus corridors or urban core areas. Because of the significance of the variables on motor vehicle crash density in pick-up and drop-off locations, an additional regression model was used to examine the effect of crash incidents on trip duration by considering drop-offs that occurred in locations immediately after a crash. The model showed that trips take 4 to 5% longer when crashes occur in locations prior to a drop off. Planning implications of the findings are discussed.     

Determinants and spatial variability of after-school travel by teenagers

This research work targeted teenagers to investigate after-school travel patterns and factors affecting their modal choice characteristics in Okinawa, Japan, where teenagers are not allowed to drive. The analysis is based on the cross-sectional data collected for discrete choice modeling of students’ travel in the prefecture. In this study, high school students’ trips from school to home have been analysed using a multinomial logit model. This has revealed the impact of individual, modal, and spatial variables on the mode choice decisions and return trip patterns. A comparative analysis made of downtown and suburb schools exposed differences in travel patterns induced by spatial factors and some existing constraints in modal choice options.     

An exploration of the environmental and rider characteristics associated with disability paratransit trip delay

Over the past two decades, a number of studies have been published on the efficiency of paratransit service for persons with disabilities. Although speed, delay, and pick-up duration can significantly affect overall efficiency of paratransit service, studies that have focused on these performance measures have been rare. This study examines how these performance measures are associated with local environmental characteristics such as density and the characteristics of the trip makers. It uses a dataset containing detailed information on a large volume of trips made by the registered clients of Access Link, a paratransit service operated by NJ TRANSIT for persons with disabilities. To measure speed and delay, network distances for 1.91 million trips were estimated by the ArcGIS Network Analyst extension. Analysis of variance and regression models were used to examine the associations between the performance measures and a set of variables pertaining to trips, passengers, and characteristics of pick-up and drop-off locations. Models for the entire study area as well as specific Access Link regions show that there is a significant association between local environmental and personal characteristics of passengers and the performance measures. Evidence was found that higher density of population, employment, and intersections at the local level may have a significant adverse effect on service efficiency because of lower speed and higher delay. Planning implications of the findings are discussed.     

Do satisfying walking and cycling trips result in more future trips with active travel modes? An exploratory study

Previous studies have indicated that travel satisfaction - the experienced emotions during, and cognitive evaluation of, a trip - can be affected by travel mode choice and other trip characteristics. However, as satisfactory trips might improve a person's attitude towards the used mode, persons may be more likely to use that same mode for future trips of the same kind. Hence, a cyclical process between travel mode choice and travel satisfaction might occur. In this paper we begin to analyse this process—using a structural equation modelling approach on cross-sectional data—for people who engage in walking and cycling for leisure trips in the Belgian city of Ghent. The focus on walking and cycling reflects recent studies indicating that active travel is often associated with the highest levels of travel satisfaction. Results of this exploratory analysis offer tentative support for the idea of a cyclical process: the evaluation of walking and cycling trips positively affects the respondents' attitude towards the respective mode, which in turn has a positive effect on choosing that mode.     

What do trip data reveal about bike-sharing system users?

Bike-sharing systems (BSS) have rapidly been established in many cities worldwide. The benefits that systems potentially provide are increasingly debated with concerns that the BSS may mostly benefit limited groups of citizens. Understanding how, when and by whom these systems are used may help to plan the system to be widely employed and inclusive.Trip data generated by the BSS are among the more analysed data types in the BSS literature, as they are often readily available for scholarly use. Most often, trip data are used to study the origins and destinations of the trips and their spatial patterns. In this paper, we focus on analysing how well trip data can be used to understand the demographic characteristics and usage profiles of BSS users.We first analysed the use of BSS trip data in the recent scholarly literature. We then used data from the Helsinki BSS from 2017 (~1.5 million trips) as a case to study the potential of trip data for future BSS studies. The Helsinki BSS, launched in 2016, is considered to have been a success, as it exhibits one of the highest use rates in the world. We aimed to understand how this popular system has served different user groups.We demonstrate the value of BSS trip data in understanding user characteristics and usage profiles and show that trip data have not yet been fully used for these purposes in the scholarly literature. Even considering its limitations, trip data can provide information that it is important for BSS managers and urban planners when understanding and developing the system inclusiveness. In Helsinki, we show that the BSS use is largely contributed by a limited group of people whose home area and daily travel needs likely align well with the system network. These findings point to challenges in system inclusiveness despite the internationally high use rates.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Locational dynamics of logistics facilities: Evidence from Tokyo

This study uses data from a large-scale freight survey conducted in the Tokyo Metropolitan Area to jointly analyze the spatial distribution of logistics facilities and their proximities to the locations of shipment origins and destinations. The aim of the study is to examine in detail the argument that logistics sprawls increase truck trip distances, and thus would incur negative impacts to the society. We found that between 1980 and 2003, logistics facilities in the Tokyo Metropolitan Area have migrated outward, albeit in a much smaller scale than the cases documented in some U.S. and European cities. Our analysis of the shipment data confirms that logistics sprawl increases truck travel. Furthermore, we found that, regardless of their age, logistics facilities tend to increase shipping distances as their distances to the urban center increase, due to the spatial mismatch between the locations of the facilities and the shipment origins and destinations. The findings underscore the importance of comprehensive efforts to coordinate land use, not only for logistics facilities but also other businesses that generate freight movements.     

Gendered key events in the life course: effects on changes in travel mode choice over time

This paper studies changes in travel mode specific trip rates after life course (and accessibility) related key events from a gender perspective. It is theoretically informed by the mobility biographies approach, and by gender/travel studies. The data used is the German Mobility Panel (GMP) 1994 to 2010 in which households and their members are asked three times in three subsequent years to report the trips they made over a week. The changes reported are regressed to key events over the life course, cohort effects and period effects, while sociodemographics and spatial context attributes are controlled. A cluster–robust regression approach is used to account for the non-independent character of panel observations. Significant effects were found for some key events, including the birth of a child, entry into the labour market, and changes in spatial context, accessibility and mobility. Some effects differed distinctly between men and women, suggesting that men and women are differently affected by life course events. However, taken together the associations found, as well as their gender differences are rather limited. Hence, key events over the life course seem to be only loosely associated with travel mode specific trip rates.     

Network-based spatial interpolation of commuting trajectories: application of a university commuting management project in Kyoto,Japan

This study presents an application of network-based spatial interpolation of student commuting trajectories from a series of origin–destination trip datasets. In particular, we incorporated multimodal public transportation networks, including bus networks, to estimate the student commuting routes. The student samples for this study were collected from an online travel diary survey conducted by Ritsumeikan University in Kyoto, Japan. The ArcGIS Network Analyst was used to construct spatial network datasets and reconstruct trajectories from the origin–destination trip dataset. In addition, line densities of estimated trajectories were calculated and displayed on maps for geovisualization. These maps helped us understand the precise locations of congestion and spatial patterns of student commuting, unlike linear representations of people’s movements that connect origins and destinations. Our study also showed that estimated trajectories can simulate quantitative impacts on travel time by promoting walking or the use of public transportation.     

Analysing the spatial-temporal characteristics of bus travel demand using the heat map

As the basic travel service for urban transit, bus services carry the majority of urban passengers. The characterisation of urban residents' transit trips can provide a first-hand reference for the evaluation, management and planning of public transport. Over the past two decades, data from smart cards have become a new source of travel survey data, providing more comprehensive spatial-temporal information about urban public transport trips. In this paper, a multi-step methodology for mining smart card data is developed to analyse the spatial-temporal characteristics of bus travel demand. Using the bus network in Guangzhou, China, as a case study, a smart card dataset is first processed to quantitatively estimate the travel demand at the bus stop level. The term ‘bus service coverage’ is introduced to map the bus travel demand from bus stops to regions. This dataset is used to create heat maps that visualise the regional distribution of bus travel demand. To identify the distribution patterns of bus travel demand, two-dimensional principal component analysis and principal component analysis are applied to extract the features of the heat maps, and the Gaussian mixture model is used for the feature clustering. The proposed methodology visually reveals the spatial-temporal patterns of bus travel demand and provides a practical set of visual analytics for transit trip characterisation.     

Expanding the positive utility of travel through weeklong tracking: Within-person and multi-environment variability of ideal travel time

Travel has been found to have a positive utility—often measured as a desire for non-zero travel time. Although past studies have found that desired travel time varies by trip attributes, they often focused on a single trip purpose (e.g., commute) or single time point (e.g., peak period of one day) and rarely captured variation within individuals and across multiple travel environments. To address these limitations, we employed a smartphone-based travel behavior survey of 186 users making 4397 trips in the Washington, DC, and Blacksburg, VA metropolitan areas. For each user, multiple trips were recorded and the user was asked to repeatedly report on the ideal travel time for each trip. We found that desired travel time varied across different trip environmental characteristics and purposes. Ideal travel time was longer for active travel trips, leisure trips, weekend trips, and when the user conducted activities during trips (e.g., talking, using the phone, looking at the landscape) and traveled with companions. Our study suggests the need for more realistic estimation of the value of travel time savings and the need for quantifying the effect of multitasking during travel on people's willingness to reduce travel time. Practitioners should also consider providing better urban infrastructure for pedestrians and bicyclists to fulfill their trips, as their ideal travel times closely match actual travel times.     

Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach

Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a state-of-the-art machine learning method (i.e., extreme gradient boosting) to explore the non-linear associations between built environment and active travel for working and shopping. Our findings are as follows. (1) For both purposes, trip characteristics contribute the greatest, and the built environment is also quite important and has larger collective contributions for active travel than does socioeconomics. (2) The relative importance of built environment on active travel for shopping is evidently larger than that for working. (3) All built-environment variables have non-linear associations with active travel, and associations with active travel for working are generally in inverted U or V shapes, while those with shopping trips have much more complex patterns. (4) Differences in the threshold value and gradient exist between built-environment associations with active travel for working and shopping and between variables at origins and destinations. Decision makers are recommended to meticulously disentangle the complex influences of built environment on active travel and distinguish between diverse purposes to make informed and targeted interventions.