Defining and understanding trip chaining behaviour

Trip chaining is a phenomenon that we know exists but rarely investigate. This could be attributed to either the difficulty in defining trip chains, extracting such information from travel diary surveys, the difficulty in analysing all the possible trip chain types, or all of the above. Household travel diary surveys provide a wealth of information on the travel patterns of individuals and households. Since such surveys collect all information related to travel undertaken, in theory it should be possible to extract trip-chaining characteristics of travel from them. Due to the difficulty in establishing and analysing all of the possible trip chain types, the majority of research on trip chaining has appeared to focus on work travel only. However, work related travel in many cities does not represent the majority of activities undertaken and, for some age groups, does not represent any travel at all. This paper begins by reviewing existing research in the field of trip chaining. In particular, investigations into the definitions of trip chaining, the defined typologies of trip chains and the research questions that have been addressed are explored. This review of previous research into trip chaining facilitates the following tasks: the identification of the most useful questions to be addressed by this research; defining trip chaining and associated typologies and defining data structures to extract trip chaining information from the household travel surveys conducted in metropolitan Adelaide, South Australia. The definition and typology developed in our research was then used to extract trip-chaining information from the household travel diary survey (MAHTS99) conducted in Adelaide in 1999. The extracted trip chaining information was then used to investigate trip-chaining behaviour by households. The paper reports the results of this analysis and concludes with a summary of the findings and recommendations for further investigations.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

The importance of the multi-purpose journey to work in urban travel behavior

This study examines the journey to work as a multiple-purpose trip (home-to-home circuit). Using disaggregate travel diary data collected over 35 consecutive days, the study shows the importance of the multi-purpose work trip in the overall travel pattern of the urban household. A large proportion of many households' total travel is undertaken in conjunction with the journey to and from work. The paper also examines the nature of these work-induced travel linkages and finds that many types of urban establishments depend heavily upon stops made in connection with the work trip. In fact, there is a group of urban functions that have stronger travel links with the workplace than with the home or with any other type of urban establishment. The study examines the implications of the multi-purpose journey to work for policies regarding mode use and the viability of centrally-located urban functions.     

Impact of telecommuting on spatial and temporal patterns of household travel

A spatial and temporal analysis of travel diary data collected during the State of California Telecommuting Pilot Project is performed to determine the impacts of telecommuting on household travel behavior. The analysis is based on geocoded trip data where missing trips and trip attributes have been augmented to the extent possible. The results confirm the earlier finding that the Pilot Project telecommuters substantially reduced travel; on telecommuting days, the telecommuters made virtually no commute trips, reduced peak-period trips by 60%, total distance traveled by 75%, and freeway miles by 90%. The spatial analysis of the trip records has shown that the telecommuters chose non-work destinations that are closer to home; they exhibited contracted action spaces after the introduction of telecommuting. Importantly, this contraction took place on both telecommuting days and commuting days. The telecommuters distributed their trips, over the day and avoided peak-period travel on telecommuting days. Non-work trips, however, show similar patterns of temporal distribution on telecommuting days and commuting days. Non-work trips continued to be made during the lunch period and late afternoon and evening hours.     

Systematic variability in repetitious travel

The focus of this paper is the degree to which day-to-day variability in the individual's travel pattern has a systematic, or nonrandom, component. We first review the different sources of variability in travel, emphasizing the difference between between-individual and within-individual variation and the implications of this difference for travel analysis. After discussing the impact of measurement (i.e. the way in which travel behavior is measured) on the study of repetition and variability, we use the Uppsala data to examine the level of systematic variability in an individual's longitudinal travel record. The analysis focuses on two questions:

- How well does observation over one week capture longer-term (five-week) travel behavior; in other words, is behavior highly repetitive from week to week?

- How systematic is within-individual variability; in other words, are certain stops distributed over the five-week record in a nonrandom, that is either regular or clustered, fashion?

Using measures of travel that include more than one stop attribute (e.g. activity, mode, time of day, and location), we found that:

- A seven-day record of travel does not capture most of the separate behaviors exhibited by the individual over a five-week period, but it does capture, for most people, a good sampling of the person's different typical daily travel patterns.

- Whereas a considerable portion of intraindividual variability is systematic (nonrandom), clustering is a more important source of nonrandom variation than is regularity.

The results suggest that behavior does not follow a weekly cycle closely enough for a one-week travel record to measure the longer-term frequency with which the individual makes certain stops or to assess the level of day-to-day variation present in the individual's record. Because these results are likely to reflect the particular measures of behavior we used, one conclusion of this study is the need for other studies that replicate the aims of this one but use a variety of other travel measures. Only through such additional work can we truly assess the sensitivity of our findings to measurement techniques.     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.     

Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph

Aiming to develop a theoretically consistent framework to estimate travel demand using multiple data sources, this paper first proposes a multi-layered Hierarchical Flow Network (HFN) representation to structurally model different levels of travel demand variables including trip generation, origin/destination matrices, path/link flows, and individual behavior parameters. Different data channels from household travel surveys, smartphone type devices, global position systems, and sensors can be mapped to different layers of the proposed network structure. We introduce Big data-driven Transportation Computational Graph (BTCG), alternatively Beijing Transportation Computational Graph, as the underlying mathematical modeling tool to perform automatic differentiation on layers of composition functions. A feedforward passing on the HFN sequentially implements 3 steps of the traditional 4-step process: trip generation, spatial distribution estimation, and path flow-based traffic assignment, respectively. BTCG can aggregate different layers of partial first-order gradients and use the back-propagation of “loss errors” to update estimated demand variables. A comparative analysis indicates that the proposed methods can effectively integrate different data sources and offer a consistent representation of demand. The proposed methodology is also evaluated under a demonstration network in a Beijing subnetwork.     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

Travel-based multitasking: review of the empirical evidence

This paper reviews 58 studies with empirical evidence on travel-based multitasking, identifies gaps in terms of data collection methods and provides a comprehensive review of findings about the significance of variables with an impact on the prevalence and type of multitasking. We identified the limitations of quantitative or qualitative surveys and advocate a mixed methods approach to provide an in-depth understanding of travel-based multitasking. We revealed that cross-country comparisons are missing due to the lack of empirical evidence outside the developed countries. While there are indications of increasing multitasking with mobile devices, we found only two longitudinal surveys that provide evidence. We call for a standardisation of definitions of multitasking activities to enable more longitudinal research. We identified 75 variables that were tested for impact on travel-based multitasking in previous research, of which 60 were found to be significant. Sufficient evidence (i.e. minimum three papers), however, only exists for age, gender, trip duration, travel mode, trip purpose, time of the day and day of the week of the trip and the presence of a travel companion. Therefore, more research is suggested to determine the influence of attitude, comfort, availability of equipment, time use and spatial attributes on the type and prevalence of travel-based multitasking.     

Modeling the modal choice decision process

Most models of modal choice are macroanalytic in nature — focusing on the behavior of large groups of travelers — and have limited explanatory power. Transportation managers need to know more about the decision processes of individual travelers in selecting a mode for a particular trip, if they are to be able to develop strategies for influencing these decisions. A microanalytic model of modal choice is therefore developed in flow-chart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need by first specifying the characteristics of the trip itself and then specifying the “ideal” modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this “ideal” solution and the consumer is assumed to select that mode which provides the best match. The model explicitly recognizes the impact of psychological variables on modal choice as well as the consumer's need for information if he or she is to evaluate realistically all alternatives.     

Effects of household structure and accessibility on travel

The concept of accessibility has been widely used in the transportation field, commonly to evaluate transportation planning options. The fundamental hypothesis of many studies related to accessibility could be “greater accessibility leads to more travel”. However, several studies have shown inconsistent results given this common hypothesis, finding instead that accessibility is independent of the trip/tour frequency. In addition, empirical aggregate urban modeling applications commonly produce either non-significant or negative (wrong sign) relationships between accessibility and the trip/tour frequency. For this reason, many practitioners rarely incorporate a measure of accessibility into trip/tour generation models out of consideration of the induced demand. In this context, this study examined the effect of accessibility in urban and suburban residences on the maintenance and discretionary activity tour frequencies of the elderly and the non-elderly using household travel survey data collected in the Seoul Metropolitan Area of Korea. The major finding of this study is that a higher density of land use and better quality of transportation service do not always lead to more tours due to the presence of intra-household interactions, trip chaining, and different travel needs by activity type. This finding implies that accessibility-related studies should not unquestioningly accept the common hypothesis when they apply accessibility measures to evaluate their transportation planning options or incorporate them into their trip/tour generation models.     

Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment

Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and wellbeing. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled.     

Developing an activity-based trip generation model for small/medium size planning agencies

The primary shortcoming of traditional four-step models is that they cannot capture derived travel demand behaviors. However, travel demand modeling (TDM) is an essential input for urban transportation planning. TDM needs to be highly precise and accurate by integrating the accurate base year estimation along with suitable alternatives. Currently, activity-based models (ABMs) have been developed mostly for large metropolitan planning organizations (MPO), whereas smaller/medium-sized MPOs typically lack these models. The main reason for this disparity in ABM development is the complexity of the models and the cost and data requirements needed. We posit however that smaller MPOs could develop ABMs from traditional travel surveys. Therefore, the specific aim of this paper is to develop a probabilistic home-based destination activity trip generation model considering travel time behavior. Results show that the developed model can significantly capture the actual number of trip generations.     

A measure of linked‐trip accessibility

The concept of accessibility has been variously interpreted as being the “nearness to places,” the “nearness to activities” and more recently the “ease of participating in activities.” With each of these qualitative interpretations, there has also been a variety of quantitative definitions of accessibility. This paper shows that many of the proposed definitions of accessibility can in fact be gathered together to form a spectrum of accessibility measures. These measures differ with respect to the factors included in their formulation and their degree of behavioural interpretation.

Existing measures of accessibility are shown to be deficient in one major aspect. That is, they assume that for any one measure of accessibility there is but one origin of trips. Thus, in estimating the accessibility of a point within a region it is assumed that all potential trips, which contribute to the accessibility of that point, start from that single point. In view of the considerable amount of evidence demonstrating the widespread, and increasing, occurrence of trip‐linking such a proposition must be viewed as being rather doubtful.

In the light of this, the paper proceeds to develop a measure of accessibility which explicitly accounts for the linking of trips. The implications of this measure, compared to a conventional unlinked‐trip accessibility measure, are discussed as are some problems which are foreseen in the practical implementation of such a measure.     

A framework for robustness analysis of road networks for short term variations in supply

There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.     

Trip misreporting forecast using count data model in a GPS enhanced travel survey

As Global Positioning System (GPS) technology advances, it has been increasingly used to supplement traditional self-reported travel surveys due to its promising features in capturing travel data with better accuracy and reliability. Realizing the limitations of diary-based surveys, this paper presents a study that directly accounts for trip misreporting behavior in trip generation models. Travel data were obtained from prompted-recall assisted GPS survey along with a diary-based survey. Negative Binomial models for count data were developed to accommodate misreporting behavior by introducing interaction effects of the sample-indicator variable with various personal and household variables. The interaction effects indicate how the impacts of the socioeconomic and demographic variables on trip-making vary across the two samples. Assuming that the GPS sample represents the ground truth, the interaction effects actually capture the likelihood and the extent of trip misreporting by detailed personal and household characteristics. The model results reveal significant interaction effects of several personal and household variables, indicating misreporting behavior associated with these attributes. The addition of interaction coefficients to the main effect model represents the real impacts of the independent variables, after compensating for trip misreporting behavior, if any.     

Urban activity spaces: Illustrations and application of a conceptual model for integrating the time and space dimensions

This work extends the conceptual argument for the use of ellipses to portray activity spaces and offers one example of how the ellipse construct can be used to analyze urban travel characteristics, based on observed trip making behavior and socio-economic variables. A problem in characterizing activity spaces has been in integrating the time and space dimensions into the same analytical framework while maintaining an understandable graphical representation of the space-time geographies envisioned by Hagerstrand and others. The ellipse allows this, as well as providing several quantifiable measures to be used for analyzing and characterizing activity spaces and urban travel behavior. In the current application, analysis of variance is used to analyze the resulting elliptic variables of 653 travelers. The results indicate that home location and household size are important factors in determining activity space characteristics and that the ellipse variables provide a different and useful approach for understanding urban travel behavior.     

Driving by choice or necessity?

From just about all accounts, Americans are driving more than ever, not just to work but to shopping, to school, to soccer practice and band practice, to visit family and friends, and so on. Americans also seem to be complaining more than ever about how much they drive—or, more accurately, how much everyone else drives. However, the available evidence suggests that a notable share of their driving is by choice rather than necessity. Although the distinction between choice and necessity is not always so clear, it is important for policy makers. For necessary trips, planners can explore ways of reducing the need for or length of the trip or ways of enhancing alternatives to driving. For travel by choice, the policy implications are much trickier and touch on basic concepts of freedom of choice. This paper first develops a framework for exploring the boundary between choice and necessity based on a categorization of potential reasons for and sources of “excess driving”, and then uses in-depth one-on-one interviews guided by this framework to characterize patterns of excess driving. This research contributes to a deeper understanding of travel behavior and provides a basis for developing policy proposals directed at reducing the growth in driving.     

The use of GIS and relational database management systems to improve the scheduling operations of paratransit

The scheduling operations of many paratransit agencies in the United States are undertaken manually. Those customers who are eligible to travel call in their requests the day before the trip is needed. As the trip requests are received, they are entered into a list of unscheduled trips. In order to schedule these trips, the scheduler must first determine the number of drivers and shuttle buses that are available as well as the time of availability of each. The scheduler must then try to match the rides that are in “similar” areas around the “same” time to place together on the driver's schedule. As new trip requests are made, the schedulers must adjust the trips that are already scheduled to try and schedule as many trips as possible in the most efficient way.

By developing a system that would improve the scheduling system operations of, in this case, DART (Delaware Administration for Regional Transit) First State Paratransit, customers can expect to receive better service that will improve their ability to travel throughout the community. Some devices that could also improve the operations of paratransit agencies are described in this paper, such as satellite‐based Global Positioning System (GPS), radio communication systems, mobile computers, radio frequency‐based data communication systems, internet web pages, automated paratransit information systems, and card‐based data storage and transfer media. However, because paratransit systems are difficult to operate cost‐efficiently, the optimum and most cost‐efficient device must be selected. The system chosen for DART First State Paratransit includes the use of a relational database management system (RDMS) and a transportation Geographic Information System (GIS). RDMS keeps track of the database information as well as the scheduled trips and the GIS is ideal for analyzing both geographic and temporal data. This system is shown to be superior to the manual system.     

Error and uncertainty in travel surveys

Transportation study home interview surveys normally underestimate the amount of trip-making by residents of a study area, and the data has to be adjusted subsequently to compensate for this effect. One important reason for the shortfall appears to be the incomplete recall and reporting of travel information by respondents. The paper examines the influence of the survey instrument on recall by comparing reported travel behaviour in two surveys conducted in the same town, one using a conventional travel diary and the other an activity diary. The latter format appears to encourage a more complete response, giving rise to significantly higher reported trip rates and travel times. The paper discusses some research and policy implications of this finding and concludes with tentative suggestions for improved travel survey instruments.