A nonlinear canonical correlation analysis of weekly trip chaining behaviour

This research concerns the relationships between the patterns of activities pursued in home-based trip chains and the characteristics of the persons making the chains. The data source is a one-week travel diary reported by persons over 11 years of age in the Netherlands in 1984. All home-based trip chains, including both simple two-link chains and more complex ones, were classified on the basis of the sequence of away-from-home activities. Twenty types were distinguished. The presence or absence of these trip-chain types were then explained in terms of the personal and household characteristics of the travellers using nonlinear canonical correlation analysis. This analysis technique can accomodate multiple dependent variables and nominally-scaled (categorical) variables in both the independent and dependent variables sets. Determined are the category scores for each independent variable that are optimal in explaining patterns in the dependent chain-type variables. Also determined are the optimal combinations of the two variable sets. These results capture the relationships between the sequences of activities in trip chains and the variables age, sex, working status, household income, stage in the family life cycle, household car ownership, and residential location. The most effective variable was found to be life cycle, followed by age and income.     

An empirical investigation on the dynamic processes of activity scheduling and trip chaining

Investigation of the dynamic processes of activity scheduling and trip chaining has been an interest of transportation researchers over the past decade because of its relevance to the effectiveness of congestion management and intelligent transportation systems. To empirically examine the processes, a computerized survey instrument is developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes the investigation on the dynamic processes of activity scheduling and trip chaining based on data collected from a pilot study of the instrument. With the data, ordered logit models are applied to identify factors that are pertinent to the scheduling horizon of activities. Results of the empirical analysis show that a daily schedule often starts with certain activities occupying a portion of the schedule and other activities are then arranged around these pre-occupants. Activities of shorter duration are more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Persons with children often expect more constraining activities than those with no children. The analysis also shows that female respondents tend to be more structured in terms of how the week is planned. Additionally, analysis of travel patterns reveals that many trip-chains are formed opportunistically. Travel time required to reach an activity is positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.     

Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary

Abstract

Trip chaining (or tours) and mode choice are two critical factors influencing a variety of patterns of urban travel demand. This paper investigates the hierarchical relationship between these two sets of decisions including the influences of socio-demographic characteristics on them. It uses a 6-week travel diary collected in Thurgau, Switzerland, in 2003. The structural equation modeling technique is applied to identify the hierarchical relationship. Hierarchy and temporal consistency of the relationship is investigated separately for work versus non-work tours. It becomes clear that for work tours in weekdays, trip-chaining and mode choice decisions are simultaneous and remain consistent across the weeks. For non-work tours in weekdays, mode choice decisions precede trip-chaining decisions. However, for non-work tours in weekends, trip-chaining decisions precede mode choice decisions. A number of socioeconomic characteristics also play major roles in influencing the relationships. Results of the investigation challenge the traditional approach of modeling mode choice separately from activity-scheduling decisions.     

Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria,Australia

Autonomous vehicles (AVs) potentially increase vehicle travel by reducing travel and parking costs and by providing improved mobility to those who are too young to drive or older people. The increase in vehicle travel could be generated by both trip diversion from other modes and entirely new trips. Existing studies however tend to overlook AVs’ impacts on entirely new trips. There is a need to develop a methodology for estimating possible impacts of AVs on entirely new trips across all age groups. This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia. A new methodology for estimating entirely new trips associated with AVs is proposed by measuring gaps in travel need at different life stages. Results show that AVs would increase daily trips by 4.14% on average. The 76+ age group would have the largest increase of 18.5%, followed by the 18–24 age group and the 12–17 age group with 14.6 and 11.1% respectively. If car occupancy remains constant in AV scenarios, entirely new trips and trip diversions from public transport and active modes would lead to a 7.31% increase in car trips. However increases in car travel are substantially magnified by reduced car occupancy rates, a trend evidenced throughout the world. Car occupancy would need to increase by at least 5.3–7.3% to keep car trips unchanged in AV scenarios.     

Modelling the impact of weather conditions on active transportation travel behaviour

Three weather sensitive models are used to explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data are restricted to non-captive commuters who have the option of selecting among five basic modes of auto driver, auto passenger, transit, bike and walk. Daily trip rates in various weather conditions are assessed. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Preference heterogeneity in mode choice based on a nationwide survey with a focus on urban rail

The provision of efficient and effective urban public transport and transport policy requires a deep understanding of the factors influencing urban travellers’ choice of travel mode. The majority of existing literature reports on the results from single cities. This study presents the results of a nationwide travel survey implemented to examine multiple modes of urban passenger transport across five mainland state capitals in Australia, with a focus of urban rail. The study aims to explore differences in mode choices among surveyed travellers sampled from the five cities by accounting for two types of factors: service quality and features of public transport, and socio demographic characteristics. A stated preference approach is adopted to elicit people’s valuation of specified mode-choice related factors and their willingness to pay. In particular, the availabilities of wireless and laptop stations – two factors rarely examined in the literature, were also considered in the SP survey. The survey data were analysed using mixed logit models. To test for preference heterogeneity, socio-demographic factors were interacted with random parameters, and their influences on marginal utilities simulated. The analysis reveals that intercity differences, user group status, gender, income, and trip purposes partially explain observed preference heterogeneity.     

Measuring the performance of airport resilience to severe weather events

The increased severe weather events in recent years as a result of global climate change has created a substantial challenge for aviation system operation. Although transportation engineers and planners have attempted to improve system resilience through the adaptation of new technologies and the implementation of various strategies to achieve effective risk management, it remains unclear how resilience performance (measured by the speed of recovery) of airports varies in different severe weather events and what factors may explain such variations. This paper addresses these fundamental questions using the aviation system in China as an example. A resilience metric, which reflects the speed of recovery (bounce back) from a shock, was developed to measure the performance of airport resilience under various severe weather conditions. In addition, an empirical econometric analysis was conducted based on a dataset that includes both detailed aviation performance and weather conditions for the period of October 2016 – September 2017. The research findings show that airport resilience to severe weather events does vary substantially based on factors, such as weather conditions, airport capacity, and the level of modal substitution. In particular, the recovery time of air services in central and south China tends to be relatively longer in thunderstorms than other weather conditions. The study also confirms that modal substitution is a very effective resilience tactic of the transportation system as the recovery speed of air service was found to be faster by 22.9% if an alternative mode, such as high-speed rail (HSR) service was also available in the city.     

Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities

This study presents a unified framework to understand the weekday recreational activity participation time-use of adults, with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis is important for a better understanding of how individuals incorporate physical activity into their daily activities on a typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore, such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling, since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation. The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how much’ individuals choose to participate in ‘what type of (recreational) activity’.     

The impact of extreme weather conditions on long distance travel behaviour

This paper examines traveller attitudes and responses towards disruption from weather and natural events. An internet-based travel behaviour survey was conducted with more than 2000 respondents in London and Glasgow. Of these respondents, 740 reported information on over 1000 long distance trips affected by extreme weather and natural events over the previous three years. Results show respondents are generally cautious towards travelling during extreme weather events. For a slight majority in the case of air and public transport, and a greater one in the case of car, travellers did not considerably alter their travel plan following the disruption. This was explained not only by less disruptive weather conditions (with heavy snow and volcanic ash being the most disruptive) and impact, but also by the relative importance of their trips. Differences between transport modes were not substantial. Business trips sometimes appeared to give travellers more flexibility, some other times not. Origin and destination did have an impact on reaction, as well as the presence of children whilst travelling. Mixed results were obtained about socio-economic and attitudinal variables. Age in particular did not appear to have a significant effect. Whilst most respondents did acknowledge no external influence in their decision, results showed an important contribution of transport organisation staff, as well as home and mobile internet technology. A limited but still considerable number of respondents indicated their closest friends/relatives as the main influence of their decisions. The results will help planners deploy strategies to mitigate the negative effects of weather related disruptions.     

Travel behaviour change impacts of a major ride to work day event

While there is increasing interest in the field of travel behaviour change, relatively little attention has been given to the behaviour change potential of major events. ‘Ride to Work Day’ is an annual event which attracts thousands of participants and actively promotes riding to and from work throughout Victoria in Australia. The methodology used to assess the impact of the event on travel behaviour has evolved from a monthly panel survey of event participants to a single follow-up survey five months after the event which focuses on travel behaviour and measurement of the stage of engagement in the behaviour change process. About one in five of those participating in the event are riding to work for the first time. More than one in four (27%) of those who rode to work for the first time as part of the event were still riding to work five months after the event. Over 80% of first-timers indicated that the event had a positive impact on their readiness to ride to work with 57% indicating that it influenced their decision to ride. The event was found to have a greater impact on influencing behaviour change for female than male riders.     

The impact of activity chaining on the duration of daily activities

There is a broad body of theoretical and empirical literature dealing with trip chaining behaviour. This paper adds to the literature while focusing on the impact of activity chaining on the duration of time spent on individual purposes. Two questions in particular are addressed: first, does an additional purpose added to a trip chain affect the duration of the activities included? Second, is there any pattern of included activities that explains differences in duration? Duration data models are employed using German data. We find evidence that the number of purposes influences duration significantly. Leisure, shopping and personal business activities are affected by the occurrence of obligatory activities (work, school/university). We cannot find any evidence that personal business or leisure activities influence the duration of shopping, whereas the opposite is supported. Therefore, in terms of daily activities, obligatory and shopping activities are superior to leisure and personal business. We conclude that activity chaining and especially the pattern of combined purposes affect the duration of activities allocated to single purposes while controlling for a wide range of other explanatory variables. The results can be used in transport and simulation models.     

Market segmentation as a mechanism in allowing for variability of traveller behaviour

A major problem with aggregate transport planning models is the accounting of variability in traveller behaviour when the basic unit of analysis is the geographical traffic zone. In an attempt to allow for this variance, recent attention has been given to the role of socio-economic (user and household) characteristics in systematically identifying a homogeneous grouping of travellers with respect to the issue under study rather than restricting the grouping definition according to physical geographical criteria alone. This homogeneous grouping criterion combined eventually with a necessity to represent travel demand in a spatial context, can assist in improving our ability to explain real travel patterns by the development of an improved aggregation condition. The emphasis is on modelling homogeneous groups of travellers separately, and then relating the individual sets of results to each other to obtain an aggregate prediction of behaviour via a knowledge of the representativeness of each group contained in the total sample. This paper presents a technique to identify the relative homogeneity of travellers in accordance with a specified criterion, and illustrates its use with individual household data for the Sydney Metropolitan Area. The paper concludes with a discussion of the advantages of segmentation in operational transport planning, in particular with reference to the aggregation of disaggregate behavioural travel choice models, or movement from a micro-model of individual choice behaviour to an aggregate model of travel demanu.     

Improving the resolution of gridded-hourly mobile emissions: incorporating spatial variability and handling missing data

To more accurately predict hourly running stabilized link volumes for emissions modeling, a new method was recently developed that disaggregates the period-based model link volumes into hourly volumes using observed traffic count data and multivariate multiple regression (MMR). This paper extends the MMR methodology with clustering and classification analyses to account for spatial variability and to accommodate model links that do not have matching observed traffic count data. The methodology was applied to data collected in the South Air Basin. The spatial analysis resulted in identifying five clusters (or 24-h profiles) for San Diego and two clusters for Los Angeles. The MMR models were then estimated with and without clustering. For San Diego, the disaggregated model volumes with clustering were much closer to the observed volumes than those without clustering, with the exception of the a.m. period. For most hours in Los Angeles, the predicted volumes with clustering were only slightly closer to the observed volumes than those predicted without clustering, suggesting that spatial effects are minimal in Los Angeles (i.e., that 24-h volume profiles are fairly similar throughout the region) and clustering is not necessary. Finally, two classification models, one for San Diego and one for Los Angeles were developed and tested for network link data that does not have matching observed count data. The results indicate the procedure is relatively good at predicting a cluster assignment for the unmatched location for Los Angeles but less accurate for San Diego.     

Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice

Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter’s driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures.     

Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues

With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.     

Environmental impacts of transformative land use and transport developments in the Greater Beijing Region: Insights from a new dynamic spatial equilibrium model

This paper reports the insights into environmental impacts of the ongoing transformative land use and transport developments in Greater Beijing, from a new suite of dynamic land use, spatial equilibrium and strategic transport models that is calibrated for medium to long term land use and transport predictions. The model tests are focused on urban passenger travel demand and associated emissions within the municipality of Beijing, accounting for Beijing’s land use and transport interactions with Tianjin, Hebei and beyond. The findings suggests that background trends of urbanization, economic growth and income rises will continue to be very powerful drivers for urban passenger travel demand across all main modes of transport beyond 2030. In order to achieve the dual policy aims for a moderately affluent and equitable nation and reducing the absolute levels of urban transport emissions by 2030, road charging and careful micro-level coordination between land use, built form and public transport provision may need to be considered together for policy implementation in the near future.