The design of environmentally aware travel diaries

Any policy addressing the concerns and trends associated with the impact of travel on the environment should be based on a solid understanding of the activities giving rise to them. While the measurement of the total environment loads by air or noise measurement stations is essential, it needs to be matched by the observation of the human behaviours creating them. This is especially true in the transport sector, which has been rightly or wrongly identified as having the potential to make a substantial contribution to the reduction of air and noise pollution. While the contribution of freight transport is of growing concern and importance, this paper focuses on the measurement of passenger transport throughout.

In the past transport planners have largely relied on the travel diary as their prime instrument to measure traveller behaviour. The travel diary is a survey instrument designed to record all movements during the course of one or more days including their relevant details. It is complemented by spearate household and personal forms for recording general information. In the following paper the term travel diary implies all three elements (the diary, the person form and the household form).

The remainder of the paper discusses to what extent and how the travel diary can be used to capture data for the assessment of policies directed at reducing the impact of transport on the social and natural environment. The requirements of a travel diary and the potential uses of new technologies in realising such a travel diary are then presented. A brief outlook on the possibilities of reasling such a diary concludes the paper.     

What Moves Us? An Interdisciplinary Exploration of Reasons for Traveling

Abstract

We review a number of theories of motivation, and typologies of motivations, in psychological theory and in application to a variety of specific contexts, including shopping, eating, leisure, tourism, and travel. A recurring theme is the distinction between extrinsic (instrumental, utilitarian, functional) and intrinsic (autotelic, hedonic, experiential) motivations. We suggest that travel is a behavior to which intrinsic motivations apply, and that focusing exclusively on the extrinsic motivations to travel runs the risk of substantially underestimating the demand for travel, and the resistance to policies attempting to reduce it or to technologies (notably, information and communication technologies) expected to (partly) replace it. We offer a number of suggestions for improving standard travel surveys to help obtain the data needed to explore intrinsic motivations more fully. As better data become available, travel behavior models can be refined to partly account for such motivations. We believe that the resulting insights will be extremely valuable to policy-makers, planners, and behavioral scholars.     

The ambivalence of ridesharing

Ridesharing is quite a popular topic of discussion among transport authority personnel. It is perceived to be a viable alternative to classical modes of transportation, and receives a great deal of political support from transport planners. However, not much objective information is available on ridesharing behaviors. We use travel survey data to study the evolution of the ridesharing market in an urban area. Our study is based on data from four large-scale OD surveys conducted in the Greater Montreal Area (1987, 1993, 1998 and 2003). In the latest survey conducted in Montreal, car passengers were asked to identify the driver who gave them the opportunity to travel in this way. Their answers were classified according to the type of driver; for instance, a member of their household, a neighbor or a co-worker. We use this information to calibrate a model matching car passengers and car drivers belonging to the same household. This will be referred to as IHHR (intra-household ridesharing). Preliminary results reveal that approximately 70% of all trips made by car passengers are the result of IHHR. Furthermore, around 15% of those trips are questionable, in that they were exclusively generated for another individual’s purposes, consequently generating an additional trip for the journey back home. Moreover, this percentage increased over time. Objective data regarding ridesharing and its evolution in an urban area will undoubtedly help decision makers gain a clearer profile of this means of travel and help to realign attitudes on the issue.

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.     

A time-use activity-pattern recognition model for activity-based travel demand modeling

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).

     

Enhancing the Travel Survey Process and Data Using the CATI System

Abstract

This paper describes an intelligent interface developed to assist in the task of collecting detailed information regarding daily travel behaviours. The computer-assisted telephone interview (CATI) system, developed by the MADITUC Research Group for the Montreal large-scale travel surveys, is structured in three main screens (household, person and trip) and assists the interaction between an interviewer and a respondent during a phone interview. This tool enhances the quality of the data collected by performing real-time validation of the spatio-temporal details related to travel behaviours. In addition to describing the design of the CATI tool, this paper proposes some empirical measurements associated with the CATI use by the interviewers owing to the processing of numerical logs saving every action taken by the user during an interview. Using these data, variables significantly influencing interview duration are identified.     

Reasons for participating in formal employer-led carpool schemes as perceived by their users

Abstract

Carpooling, the process by which individuals share a private vehicle for a particular journey or journeys, has been undertaken both formally and informally for a great number of years. A variety of computational methods for undertaking the ‘ride-matching’ element for the formation of carpools have been developed and subsequently made into integrated tools to allow the formation of multiple carpools. Such tools are commonly used by both Local Authorities and employers who are looking to establish and operate their own formal carpool scheme, increasingly using a web-based interface.

The aim of this paper is to understand how users enrolled with employer-led carpool schemes perceive the importance of several different factors in their decision to participate. It is a further aim to determine the importance they attach to employer provided priority parking spaces. A survey-based approach investigates the perceptions of users from six different employer operated carpool schemes in the UK.

The paper suggests that saving money was perceived as the most important reason for an individual's decision to use a formal employer-led carpool scheme – even amongst carpool schemes where the employer provides significant incentives to participate. No regular access to their own vehicle and ‘more sociable travel’ were generally perceived as less important reasons to participate. For employers who offer priority parking to carpoolers, this was generally valued as important by participants, even when the employment location did not have significant parking pressures.     

A study of traveler behavior under traffic information-provided conditions in the Beijing area

ABSTRACT

The quality of traffic information has become one of the most important factors that can affect the distribution of urban and highway traffic flow by changing the travel route, transportation mode, and travel time of travelers and trips. Past research has revealed traveler behavior when traffic information is provided. This paper summarizes the related study achievements from a survey conducted in the Beijing area with a specially designed questionnaire considering traffic conditions and the provision of traffic information services. With the survey data, a Logit model is estimated, and the results indicate that travel time can be considered the most significant factor that affects highway travel mode choice between private vehicles and public transit, whereas trip purpose is the least significant factor for private vehicle usage for both urban and highway travel.     

Review of GPS Travel Survey and GPS Data-Processing Methods

Abstract

Global positioning system (GPS) devices have been utilised in travel surveys since the late 1990s. Because GPS devices are very accurate at recording time and positional characteristics of travel, they can correct the trip-misreporting issue resulting from self-reports of travel and improve the accuracy of travel data. Although the initial idea of using GPS surveys in transport data collection was just to replace paper-based travel diaries, GPS surveys currently are being applied in a number of transport fields. Several general reviews have been done about GPS surveys in the literature review sections in some papers, but a detailed systematic review from GPS data collection to the whole procedure of GPS data processing has not been undertaken. This paper comprehensively reviews the development of GPS surveys and their applications, and GPS data processing. Different from most reviews in GPS research, this paper provides a detailed and systematic comparison between different methods from trip identification to mode and purpose detection, introduces the methods that researchers and planners are currently using, and discusses the pros and cons of those methods. Based on this review, researchers can choose appropriate methods and endeavour to improve them.     

Man and his transport behaviour. Part 2c Planning as a substitute for implementation: a bicycle case study

Abstract

Researchers, planners and politicians alike have emphasized the need for systematic planning prior to the introduction of new transportation measures and schemes. Innovations ranging from the construction of new freeways to the encouragement of mode shift are therefore usually the result of detailed studies and extensive planning.

With unsettling frequency, however, the planning has come to replace implementation. The governing body identifies a problem area and commissions either a government planning department or a consultant to develop a plan which outlines needs and proposes measures which address the problem. The problem usually has two groups of people anxious to solve it; individuals who are being affected in their day‐to‐day living or travel, and longer‐term planners (usually politicians) who see it in terms of both extended community planning and votes.

The plan satisfies both sides temporarily, but becomes an effective planning tool only if implemented. Unrealistic plans and unwilling implementors have left a trail of plans which have been ends in themselves. Examples from bicycle planning in Australia show how the use of plan formulation can be either an essential element leading to the implementation of a planning scheme, or an expensive substitute for positive action.

A feature of plans which have been implemented is an integrated approach which makes extensive use of data collection to ascertain the needs of the population. A feature of the unimplemented plans is their concentration on the feasibility of introducing physical elements into the transport infrastructure without studying the travel demand.

It is concluded that the greater the concentration on individual needs and constraints, the less likely that planning will come to replace the implementation of a scheme.     

Modeling and forecasting household energy consumption and related CO2 emissions integrating UrbanSim and transportation models: an Atlanta BeltLine case study

Reducing energy consumption and controlling greenhouse gas emissions are key challenges for urban residents. Because urban areas are complex and dynamic, affected by many driving factors in terms of growth, development, and demographics, urban planners and policy makers need a sophisticated understanding of how residential lifestyle, transportation behavior, land-use changes, and land-use policies affect residential energy consumption and associated CO₂ emissions. This study presents an approach to modeling and simulating future household energy consumption and CO₂ emissions over a 30-year planning period, using an energy-consumption regression approach based on the UrbanSim model. Outputs from UrbanSim for a baseline scenario are compared with those from a no-transportation-demand model and an Atlanta BeltLine scenario. The results indicate that incorporation of a travel demand model can make the simulation more reasonable and that the BeltLine project holds potential for curbing energy consumption and CO₂ emissions.     

How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles

This study explores the relationships between adoption and consideration of three travel-related strategy bundles (travel maintaining/increasing, travel reducing, and major location/lifestyle change), linking them to a variety of explanatory variables. The data for this study are the responses to a fourteen-page survey returned by nearly 1,300 commuting workers living in three distinct San Francisco Bay area neighborhoods in May 1998. We first identified patterns of adoption and consideration among the bundles, using pairwise correlation tests. The test results indicate that those who have adopted coping strategies continue to seek for improvements across the spectrum of generalized cost, but perhaps most often repeating the consideration of a previously-adopted bundle. Furthermore, we developed a multivariate probit model for individuals’ simultaneous consideration of the three bundles. It is found that in addition to the previous adoption of the bundles, qualitative and quantitative Mobility-related variables, Travel Attitudes, Personality, Lifestyle, Travel Liking, and Sociodemographics significantly affect individual consideration of the strategy bundles. Overall, the results of this study give policy makers and planners insight into understanding the dynamic nature of individuals’ responses to travel-related strategies, as well as differences between the responses to congestion that are assumed by policy makers and those that are actually adopted by individuals.

Towards integrated traveller information

Traveller information provision has become a key government priority in the UK following the publication of its Transport White Paper in July 1998. Developments are already underway for the integration of information systems across different public transport modes and between public transport information systems covering different regions. The Highways Agency (which is responsible for managing the strategic road network in the UK) is also advancing in its development of information databases and systems for the motorist. This paper considers the prospect of providing travellers with multimodal information with integration of driver information with public transport information. If such integration can be achieved then travellers, and in particular drivers, have the prospect of being presented with comparable information on travel options across modes. An integrated information service has a great potential to inform and influence travel choices. The paper considers the issue of travel choices and the role that information can play particularly in the context of mode choice. The political (UK) and technological climate for information provision is outlined. The paper identifies issues that will need to be addressed to identify the requirements from, and potential benefits of, integrated information, and the obstacles and challenges likely to be faced in moving towards it. Driver reactions, in particular, to the prospect of integrated information provision are fundamental to the assessment of its potential importance and value.     

Interactive stated choice surveys: a study of air travel behaviour

Stated preference (SP) experiments are becoming an increasingly popular survey methodology for investigating travel behaviour. Nevertheless, some evidence suggests that SP experiments do not mirror decisions in real markets. With an increasing number of real world decisions made using the internet, an opportunity exists to improve the realism of the SP counterparts of such choices by aligning the choice environment with such online portals. In this paper, we illustrate the benefits of such an approach in the context of air travel surveys. Our survey is modelled on the interface and functionality of an online travel agent (OTA). As with a real OTA, many ticket options are presented. Sort tools allow the options to be reordered, search tools allow options to be removed from consideration, and a further tool allows attributes to be hidden and shown. Extensive use of these tools is made by the 462 respondents. A traditional SP component was also completed by the respondents. Our exploratory analysis as well as random utility model estimation results confirm not only that respondents seem to engage more actively with the interactive survey, but also that the resulting data allows for better performance in model estimation compared to a more conventional SP experiment. These results have implications for the study of other complex travel choices where interactive surveys may similarly be preferable to standard approaches.     

Envisioning an emission diet: application of travel demand mechanisms to facilitate policy decision making

Emission reduction strategies are gaining attention as planning agencies work towards adherence to air quality conformity standards. Policymakers struggling to reduce greenhouse gases (GHG) must grapple with a growing number of travel demand policies. To consider any of these emerging demand mechanisms as a viable option to meet emission targets, planners and policymakers need tools to better understand the implications of such policies on travel behavior. In this paper we present an integrated multimodal travel demand and emission model of four policy strategies; presenting GHG and air pollutant reduction results at a very detailed level. Multiple policy outcomes are compared within a single modeling framework and study area. The results reveal that while no one demand mechanism is likely to reduce emissions to a level that meets policy-maker’s goals; a first-best pricing strategy that incorporates marginal social costs is the most effective emission reduction mechanism. Implementing such a mechanism may offer total emission reductions of up to 24 %. However, the efficacy of this strategy must be weighed against difficulties of establishing efficient pricing, a costly implementation, and substantial negative impacts to non-highway facilities. Decision makers must select a mixture of pricing and land use strategies to achieve emission goals on all road facilities.     

Big data in public transportation: a review of sources and methods

ABSTRACT

The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.     

Inverse discrete choice modelling: theoretical and practical considerations for imputing respondent attributes from the patterns of observed choices

ABSTRACT

The growing availability of geotagged big data has stimulated substantial discussion regarding their usability in detailed travel behaviour analysis. Whilst providing a large amount of spatio-temporal information about travel behaviour, these data typically lack semantic content characterising travellers and choice alternatives. The inverse discrete choice modelling (IDCM) approach presented in this paper proposes that discrete choice models (DCMs) can be statistically inverted and used to attach additional variables from observations of travel choices. Suitability of the approach for inferring socioeconomic attributes of travellers is explored using mode choice decisions observed in London Travel Demand Survey. Performance of the IDCM is investigated with respect to the type of variable, the explanatory power of the imputed variable, and the type of estimator used. This method is a significant contribution towards establishing the extent to which DCMs can be credibly applied for semantic enrichment of passively collected big data sets while preserving privacy.     

Data display techniques for transportation analysis and planning: an investigation of three computer‐produced graphics

Graphics are a powerful but often costly means of communication. Computer‐drawn graphics offer a new and relatively inexpensive way to assist the communication of complex technical information to both planners and non‐technical people whose full potential is now beginning to be realized. This paper discusses the utility of graphics and introduces three computer‐drawn graphic techniques which may be useful, both for analysis and presentation of results, in the transportation planning process. CENVUE(S) produces a three‐dimensional, perspective‐view map, on which virtually any type of transportation data or performance indicator can be displayed. VAP is designed to display origin‐destination travel patterns in any region. TDN transforms a physical‐distance network into a time‐distance network so that effects of different speeds in the transportation network can be readily seen. The cost‐effectiveness of each technique is briefly discussed and some recommendations for evaluating computer graphics techniques are provided to aid the user in further assessing their utility in the transportation planning process.     

Statistical and activity-based modeling of university student travel behavior

Abstract

This paper conducts a statistical analysis of student travel behavior at Virginia Commonwealth University (VCU). The data source is the ‘University NHTS’ project launched by the Virginia Department of Transportation (VDOT) in 2009. Through this empirical study, it has been found that university student travel behavior is different from that of the general population; urban universities have lower percentages of nonmotorized trips than college-town universities; undergraduate students are likely to make more daily trips than graduate students – similarly, on-campus students make more frequent trips than off-campus students; the most frequent student activities are home and academic activities; and student group categories have virtually no impact on daily activity profiles, though activity types do have a dramatic impact on daily activity profiles. Based on these research findings, the paper makes a series of recommendations regarding trip generation, trip distribution, mode choice, and activity-based modeling.     

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.