An approach to imputing destination activities for inclusion in measures of bicycle accessibility

This paper proposes a new method to estimate bicycle accessibility for various trip purposes based on a massive dockless bike-sharing dataset in Shanghai, China. Specifically, a Dirichlet multinomial regression topic model (DMR model) is applied to identify bicycle trajectories' trip purposes, simultaneously considering arrival time and drop-off location. Based on obtained trip purposes, we estimate impedance functions using a negative exponential function. Finally, based on estimated impedance functions, two cases of bicycle accessibility for two different purposes - restaurant and hospital - are presented in Shanghais central area. The results show that almost 90% of bicycle trips are less than 30 min or 5 km. Although the difference between the impedance functions between various trip purposes is not significant, we find that trip purposes of “Work and School” have the highest travel impedance for bicyclists. Cyclists in Shanghai accept longer bicycle travel times for leisure (e.g., shopping) than for commuting (e.g., work or school).     

The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou,China

Improving residents' travel efficiency and reducing carbon emissions from travel are the key issues for sustainable development of urban transportation. This study first employed a circuity index to measure the path efficiency of residents' trips based on 2015 survey data in Guangzhou and developed a generalized additive model (GAM) to investigate the relationship between the path efficiency and travel distance for different purposes of trip and different travel modes. On this basis, it further evaluated the time efficiency of different travel modes for each trip. The results showed that there is a complex and nonlinear relationship between the path efficiency and travel distance, which differs between different purposes of trips and different travel modes. In general, trips by non-motorized transport have a lower circuity index and higher path efficiency than those by cars or public transport. Moreover, non-motorized transport is the time-efficiency optimal mode for almost half of the trips, especially for daily shopping trips. However, people prefer to choose public transport on their trips even though public transport is not the time-efficiency optimal mode for these trips. Generally, only about half of the residents chose the time-efficiency optimal mode for their trips. Those who did not choose the time-efficiency optimal mode tended to choose the modes with higher carbon-intensity. The conclusions of this study indicate that for improving travel efficiency and reducing carbon emissions from transport, more efforts should be focused on the non-motorized travel environment and developing relevant policies to encourage more walking and cycling.     

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.     

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.     

Estimating social travel demand of senior citizens in the Netherlands

Like many other countries, the Netherlands is experiencing a sharp rise in the ageing population. As age increases, people’s mobility may decrease. However, older people have more leisure time compared to their younger (working) counterparts, and potentially spend more time on social activities. Therefore, this group can possibly increase social travel demand. However, to date, the travel demand for social activities of senior citizens has received only little attention. This paper studies trip-making for social purposes, with a special focus on the demographic ageing factors. Using social activity diary data, models are estimated to predict the number of social trips, the travel distance and mode of transport for social trips. The results indicate that the elderly of today seem to be as mobile as their younger counterparts with respect to the number of social trips. High education and involvement in clubs on average result in more social trips and full time work is found to result in fewer social trips. With regard to trip distance the results show that the average travel distance does not decrease as people get older. Full time work is found to result in longer social trips. Shorter trips were found for people in urban as well as rural areas. Trips for the purpose of visiting or joint activities tend to be longer than average. With regard to transport mode choice the results indicate that older seniors (75+) are less likely to choose the bicycle, relative to driving. No other significant age effects were found. Significant effects were found for gender, household structure, education level, car ownership, having a disability, urban density, distance and the purpose of the social activity.     

The effect of travel cost on frequencies of shopping and recreational trips in Sweden

This article focus on how the cost of travel affects travel behavior. A trip frequency model for recreational and shopping trips is suggested and used to investigate this. The data that is used comes from a Swedish travel habit survey where the respondents’ trip frequencies of both types of trips on a certain day are recorded. This is likely to introduce a correlation structure, which is incorporated in the model. Special attention is paid to the effect of travel cost on trip frequencies for different regions and income groups. As a measure of the sensitivity of cost changes, elasticity of demand is calculated. The precision of the elasticities are evaluated with simulated p-values.     

Mitigating the impact of errors in travel time reporting on mode choice modelling

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple – sometimes incongruent – sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements.     

Measuring individuals' travel behaviour by use of a GPS-based smartphone application in Dar es Salaam,Tanzania

A range of mega-cities in the Global South have started to invest in Bus Rapid Transit (BRT) systems, as a complement or replacement for informal paratransit services, in an effort to improve the mobility and accessibility in the city. Yet, few studies have tried to analyse the impact of such systems on the mobility patterns of cities' residents, in part because traditional travel diary surveys are often too expensive to conduct and unsuitable to capture spatial mobility patterns in fast growing cities with a high level of informality in spatial development. In this study, we analyse the applicability of a new method of data collection, i.e. a GPS-based smartphone application, to capture individuals travel behaviour in fast growing mega-cities in the Global South. Our case study is the city of Dar es Salaam (DES) in Tanzania, where the first BRT line is currently being implemented. In our study, the GPS-based app was used by individuals in DES to record distances, departure times and destinations of their trips. Socio-demographic data of respondents were recorded in short questionnaires. The spatial distribution of the trip patterns shows the mobility demand in both high and less connected areas. The results reveal a variation in departure times, travel destinations and trip distances that are one the one hand spatially limited within neighbourhoods and away from the planned BRT, and on the other hand along major roads connecting to the Central Business District (CBD). The short average distances of the trips (<3 km) reveal the characteristics of paratransit modes. The GPS-based smartphone application provides an opportunity to policy makers to engage deeply with the spatial reality of local communities, as a basis for transport investments and policy improvements as steps towards an integrated public transport system.     

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.     

The influence of socioeconomic characteristics,land use and travel time considerations on mode choice for medium- and longer-distance trips

This paper contributes to the limited number of investigations into the influence of the spatial configuration of land use and transport systems on mode choice for medium- and longer-distance travel (defined here as home-based trips of 50 km and over) in the Netherlands. We have employed data from the 1998 Netherlands National Travel Survey to address the question as to how socioeconomic factors, land use attributes, and travel time affect mode choice for medium- and longer-distance travel, and how their role varies across trip purposes: commuting, business, and leisure. The empirical analysis indicates that land use attributes and travel time considerations are important in explaining the variation in mode choice for medium- and longer-distance travel when controlling for the socioeconomic characteristics of travellers.     

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.     

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.     

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.     

Does the built environment make a difference? Additional evidence from the daily activity and travel behavior of homemakers living in New York City and suburbs

Homemakers, unlike employed people who have jobs and unemployed people who are seeking jobs, are a special group who do not have to spend time working out of the home, commuting to work, or looking for a job. Given that a regular job typically takes 9 h (This includes an assumed half-hour one-way commute time.) a day, the discretion to allocate their time is presumably much greater than other groups.In this paper, we focus our attention on homemakers’ activity and travel behavior in neighborhoods with different characteristics (e.g., very dense areas, dense areas, and suburbs). The question to be answered is quite simple: are there differences between travel behaviors of homemakers living in different types of neighborhoods? If yes, can these differences be attributed to differences in the built environment?The dataset used in the study is the Household Interview Survey (HIS) collected in 1997/1998 in the New York metropolitan area. We found significant differences in activity and travel related behavior by homemakers living in different types of neighborhoods. Compared to suburban homemakers, New York City homemakers spend more time on discretionary activities and less time on maintenance activities; use public transportation and walk more frequently; and conduct fewer trip chains. The study found that both individuals’ socio-economic characteristics and built environment appear to play a role in explaining behavior. A probably more important factor in explaining people’s time use behavior is the interrelationship between activities and trips, and between different types of activities.     

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.     

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.     

Anticipating long-distance travel shifts due to self-driving vehicles

Increasing population and travel demand has prompted new efforts to model travel demand across the United States. One such model is rJourney that estimates travel demand among thousands of regions and models mode and destination choice. rJourney includes records representing 1.17 billion long-distance trips throughout the year 2010. Although inter-regional impacts caused by an increase of automated vehicles (AVs) has been investigated, there is little research on inter-regional travel and how longer distance destination and mode choices will change. Because of conveniences offered by AVs, the value of travel time of drivers is expected to fall, thus reducing the generalized cost of AV travel. To initially analyze the impacts of AVs in the United States, a new AV mode was added to a subset of the rJourney mode and destination choice models. With an initial scenario assuming an operating cost of AVs that is 118% of traditional cars, two outcomes are observed that are solely based on model results. First, the attractiveness of AVs severely digs into the airline travel market, reducing airline revenues to 53%. Second, the introduction of AVs results in a shift of destination choice, increasing travel in further distances for personal vehicles, but favoring closer distances across all modes, for an overall 6.7% decline in US passenger-miles traveled on existing long-distance trips. While this preliminary research has revealed an initial perspective on how an existing model can support AVs, the increasing availability of data as AVs emerge will refine nationwide long-distance modeling.     

How do passengers allocate and evaluate their travel time? Evidence from a survey on the Shanghai–Nanjing high speed rail corridor,China

Although previous studies have demonstrated that travel time is not wasted, only a limited number of studies have conducted an in-depth investigation of how passengers allocate their travel time and what factors will impact their subjective valuation of travel time (SVTT). Investigating such questions will uncover some of the possible economic, social, technological and behavioral reasons that influence SVTT. Using a survey of 822 passengers traveling along the Shanghai-Nanjing high speed rail (HSR) corridor in 2016, this study examines passengers' allocation of their travel time, and explores the determinants of SVTT for business and non-business travelers, respectively. Empirical results indicate that around half of the respondents spend the longest amount of travel time on ICT discretionary activities, but there are some differences across trip purpose. Ordered logit models suggest that SVTT is determined by a range of factors, which are classified into travel time allocation, HSR environment, travel attributes, and socio-demographic characteristics. However, the specific factors associated with SVTT are somewhat different between business and non-business trips. The findings of this study provide a better understanding of the perceived value of travel time in a supportive travel environment and in a typical e-society of a developing country, and offer implications for more comprehensively exploring determinants of SVTT in the future.     

Airline daily fare differentiation in a medium-size travel market

This paper examines variation in airline fares for trips in a medium-size travel market. It develops a conceptual model of fares offered, and uses daily information about fare, plane and flight characteristics, and trip characteristics easily available on the internet. Based on this information it estimates a two-way fixed effects model of airline fares. The results show large differences in fares among the airlines, large variation in daily fares offered, and provide evidence of fare differentiation in the travel market analyzed.     

Built environment effects on leisure travel for children: Trip generation and travel mode

This study empirically analyzed the effects of built environment on leisure travel among children. Students of three elementary schools, namely Yangmingshan, Sanyu and Shilin, all located in the Shilin District of Taipei, were chosen to provide sample data. The negative binomial regression model and multinomial logit model were used to analyze trip generation and travel mode, respectively. This study reached the following empirical findings: (1) mixed land use, employment density, walkway quality, leisure facility supply and leisure travel distance encouraged generation of leisure trips for children; (2) intersection density, building density, employment density and walkway quality encouraged a child to use transit systems or non-motorized travel modes for leisure travel; and (3) vehicle density and leisure travel distance discouraged walking and biking but encouraged the use of transit systems for leisure travel involving children. Local government can use the empirical findings of this study to develop urban planning strategies to encourage children to perform leisure activities outside the home using transit systems or non-motorized travel modes.