Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

The relationship between transportation perceptions and behavior over time

The aim of this paper is to discuss cross-lagged panel analysis in terms of the causal inferences it generates about the relationship of beliefs about modes and mode choice behavior. Frequencies of use of the single-occupant automobile (SOA), bus and carpool, as well as beliefs about each of the modes, were collected from a sample of central business district commuters at two points in time. The belief variables for each mode were summed to form composite measures and were corrected for unreliability due to measurement error.Perceptions of each mode and the frequency of its use were analyzed for influences operating over time. A time interval was assumed to exist during which the variables causally operated on each other. It was assumed that the time necessary for an individual to change modes based on his perception was equivalent to the interval required for a person to alter perceptions based on his experience. The causal structure relating the two variables was also assumed to be stable over time. An additional assumption was required to distinguish between third variable effects, or spuriousness, and dual causation: if a third variable were to be causing the relationship, it would be operating at a relatively constant rate over time.A strong causal relationship was found to be operating between beliefs about SOA and bus and use of those modes over time. The relationship is mutually causative; beliefs determine behavior and behavior reinforces and changes perceptions. Analysis of the carpool data indicated that the causal structure had changed over time and could not be analyzed with this technique. In general, support is evidenced for an adaptation or learning process interpretation of the relationship between beliefs and mode choice behavior.     

The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey

The main focus of travel behaviour research has been explaining differences in behaviour between individuals (interpersonal variability) with less emphasis given to the variability of behaviour within individuals (intrapersonal variability). The subject of this paper is the variability of transport modes used by individuals in their weekly travel. Our review shows that previous studies have not allowed the full use of different modes in weekly travel to be taken into account, have used categorical variables as simple indicators of modal variability and have only considered a limited set of explanatory indicators in seeking to explain modal variability. In our analysis we use National Travel Survey data for Great Britain. We analyse modal variability with continuous measures of modal variability (Herfindahl–Hirschman Index, the difference in mode share between the primary and secondary mode, the total number of modes used). Taking inspiration from Hägerstrand (1970), we conceive that modal variability is determined by different types of spatial mobility constraints and find that reduced modal variability is predicted for having mobility difficulties, being aged over 60, being non-white, working full-time, living in smaller settlement, lower household income, having regular access to a car, having no public transport pass/season ticket and not owning a bicycle. The findings can support a change in perspective in transport policy from encouraging people to replace the use of one mode with another to encouraging people to make a change to their relative use of different transport modes.     

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

     

Car ownership and attitudes towards transport modes for shopping purposes in Singapore

The heightening of issues, such as sustainable development and environmental pollution have resulted in many governments pursuing transport policies which aim to promote the use of public transport modes, including walking, as well as discourage the use of the car for various activities, such as shopping, work, recreation, etc. However, little has been done on understanding shoppers' perceptions of transport modes for shopping purposes. Particularly, not much research has been done on examining the attitudes of car owners and non-car owners towards transport modes for shopping purposes. Using Singapore as a study area, this study has attempted to analyse car owners and non-car owners' perceptions of the different types of transport modes (i.e., car, taxi, bus, mass rapid transit and walk) in their shopping trips. The research found that each transport mode has its own unique set of attributes. In addition, car owners and non-car owners portray different attitudes towards the public transport modes and the car. This calls for different strategies for these two groups of shoppers in encouraging them to use the public transport modes and restrain the use of the car.     

Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat, 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data.     

Toward criteria in the development of urban transportation systems

Improved criteria are necessary to aid in determining awards of federal funds for metropolitan transit projects. Commuting is the main use for public transit. Thus a primary objective of an urban transit system should be to provide a flexible and balanced set of options to the workers in the metropolitan area for their journey to work. This paper discusses various facets of an appropriate balance among the three modes: rapid rail, bus, and automobile. Three cities are selected for further analysis: Baltimore, Kansas City, and Phoenix. These cities represent different stages in economic-transportation development, and also present different spatial patterns of residence and employment. The applicability of rapid rail transit to each city is examined in view of central city worker concentration and recent trends.     

Improving the energy efficiency of freight in the United States through commodity-based analysis: justification and implementation

Efforts to reduce energy use in freight transportation usually center around “mode-based” approaches, namely improving the energy efficiency of energy intensive modes, such as truck, and shifting more freight to energy efficient modes, such as rail. In the first part of this paper we review the recent trends and future prospects for these mode-based approaches, finding that despite substantial improvement in the technological efficiency of freight modes and robust growth in the use of intermodal rail since 1980, total freight energy use across all modes in the US has grown by approximately 33%, with proportional growth in carbon emissions. In the second part of the paper we propose use of a “commodity-based” approach, in which freight energy use is disaggregated by contribution of major commodity groups, in order to support efficiency improvement at the commodity level. Two potential applications of the commodity based approach, namely (1) life cycle analysis of energy use for major commodity groups and (2) spatial analysis of freight patterns, are demonstrated using the 1993 US Commodity Flow Survey data. Results of these preliminary findings suggest that commodity groups vary widely in the ratio of energy use in production to energy use in transport, and that for many commodity groups, there may be substantial opportunities for saving energy by redistributing flow patterns. Through development of the commodity-based approach, we also identify the collaborative involvement of shippers and carriers as a key point in improving energy efficiency, since it can be used to both make the mode-based approach more effective and address new issues such as the underlying growth in tonne-km. Benefits for air quality and other transportation issues are also discussed.     

Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study

The combination of increasing challenges in administering household travel surveys and advances in global positioning systems (GPS)/geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extreme dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.     

A study of tour-based mode choice based on a Support Vector Machine classifier

A new approach in recognizing travel mode choice patterns is proposed, based on the Support Vector Machine classification technique. The tour-based travel demand dataset that is analysed is for New York State, derived from the 2009 U.S. National Household Travel Survey. The main features characterizing each tour are the means used, travel-related variables and socioeconomic aspects. Results obtained demonstrate the ability to predict to some extent, in real settings where car use dominates, which tours are likely to be made by public transport or non-motorized means. Moreover, the flexibility of the technique allows assessing the predictive power of each feature according to the combination of travel means used in different tours. Potential applications range from activity-based travel choice simulators to search engines supporting personalized travel planners – in general, whenever ‘best guesses’ on mode choice patterns have to be made quickly on large amounts of data prejudicing the possibility of setting up a statistical model.     

Optimal transit subsidy policy

The basic justification for transit subsidy is that such a subsidy is necessary, given substantial economies of scale, in order to permit fares to be set at a level which will result in reasonably efficient use of the service. Efficiency is not, however, merely a matter of the level of the fares but even more of the fare structure and pattern. Major changes in fare patterns are needed to permit reasonable efficiency of utilization to be attained, and full advantage derived from subsidy. Differentiation according to time and direction, as well as the distance of travel, is required. Ideally, competing modes such as the private automobile should be priced at marginal cost, differentially by time and place, and the subsidy should be derived from taxes on land values in the areas where such values are enhanced by the presence of transit service at low fares. In the absence of such conditions, fares should differ from marginal cost in ways that take into account the impacts of transit fare variations on auto traffic and congestion, and on the subsidy requirements and the adverse impacts of the taxes imposed to finance the subsidy.In addition to these economic efficiency considerations there may be added considerations of distributional impact and political acceptability, which may modify the optimal solution somewhat but should not greatly change the main outlines of the patterns to be recommended.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA

This study investigates the relationship between land use and shopping tour generation using an activity-based shopping model that captures the effects of land use patterns on household decisions of shopping tour frequency, tour scheduling and mode choice. The model was calibrated using travel data collected in three traditional neighborhoods located in the Puget Sound region, WA, and shopping travel patterns across seven common household structures were analyzed. The results reveal that land use patterns have virtually no impact on overall shopping tour frequency. However, land use does seem to be associated with decisions about the type of shopping tours undertaken. For example, households with poorer accessibility tend to make fewer one-stop shopping tours, and are more likely to combine shoppingtrips with other trips to form multi-stop shopping tours as a means of compensating for locational deficiencies. Finally, we also found that traditional neighborhood residents who live closer to the neighborhood commercial street, and thus, have greater accessibility, are more inclined to use non-auto modes for one-stop shopping tours.     

Estimation of origin‐destination matrices for a multimodal public transit network

This paper presents a procedure for the estimation of origin‐destination (O‐D) matrices for a multimodal public transit network. The system consists of a number of favored public transit modes that are obtained from a modal split process in a traditional four‐step transportation model. The demand of each favored mode is assigned to the multimodal network, which is comprised of a set of connected links of different public transit modes. An entropy maximization procedure is proposed to simultaneously estimate the O‐D demand matrices of all favored modes, which are consistent with target data sets such as the boarding counts and line segment flows that are observed directly in the network. A case study of the Hong Kong multimodal transit network is used to demonstrate the effectiveness of the proposed methodology.     

Elderly travel frequencies and transport mode choices in Greater Rotterdam,the Netherlands

Demographic ageing is a key societal challenge in Europe as well as in many other western and non-western societies. A crucial dimension concerns elderly daily mobility patterns. While still partaking fewer and shorter trips than younger generations, today’s elderly have been found increasingly (auto)mobile. Although the elderly benefit from the independence, freedom of movement, and social inclusion, concerns may rise regarding the environmental and accessibility impacts of this induced mobility. The present study adds to the expanding literature on elderly mobility, an integrated analysis of the effects of socio-demographic, health, trip, spatial and weather attributes on elderly mobility. Utilizing travel diary data for Greater Rotterdam, The Netherlands, trip frequencies and transport mode choices of the elderly are analysed by means of zero-inflated negative binomial models as well as multinomial logit regression models, and contrasted to the non-elderly subpopulation to explore (dis)similarities. While the results show common determinants, the models also highlight important differences in the magnitude of the estimated coefficients and factors only influencing transport patterns for the elderly. Embedded in the context of an aging population, the empirical findings assist policy-makers and planners in several respects: For transportation plans and programs it is critical to recognize mobility needs of the elderly. As the seniors are becoming increasingly automobile, the results call for strategies to encourage older people to use more physically active and environmentally friendly transport modes such as public transport, walking and cycling.     

Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

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).

     

If London is more spread out than Paris,why don't Londoners travel more than Parisians?

London and Paris are two megalopoleis with much in common but one main distinguishing feature, their densities: London is considerably more spread out than Paris. Since so many of their other features are similar, such as their population, their household structure, their employment structure, their household incomes, their car ownership levels, their public transport systems, their road networks, this separating characteristic allows a good test of some of the current theories about the relation of travel to land use, and about the influence of travel on the expansion of cities and especially about the changing relation between the central city, the inner core and the outer ring.In order to show more clearly the nature of the similarities and differences, the available data for London and Paris are presented in rings by distance from the centre, using the smallest available analysis units for each data set with the appropriate geographical coding and allocating to 2 km wide bands. This avoids all the problems caused by arbitrary political units.Analyses are presented to justify the contention that many of their features are similar, as noted above, with the notable exception of density. Paris may, in fact, be characterised as having a population distribution equivalent to that of London forty years earlier, though, because Paris is now expanding faster than London was then, this time lag is diminishing.The daily travel patterns of the inhabitants are then presented, using the same distance from centre basis, using both distance travelled and time taken, and separating travellers according to the modes or mode combinations used in the course of a day. These patterns are taken from the various travel surveys which, with the 1981 surveys, now span up to 20 years.The contrast between the traditional land use transport model philosophy, as embodied in the models operated by both city administrations, and as represented in the continuous space, monocentric, radially symmetric conception of the city in Angel and Hyman's model, and the philosophy of Zahavi with his emphasis on time and money budgets as the starting point of such modelling is discussed in the context of the results presented. Some comments on the possible ways this might help to illuminate the question of the expansion of cities are given.     

Accounting for scale heterogeneity within and between pooled data sources

There is growing interest in incorporating both preference heterogeneity and scale heterogeneity in choice models, as a way of capturing an increasing number of sources of utility amongst a set of alternatives. The extension of mixed logit to incorporate scale heterogeneity in a generalised mixed logit (GMXL) model provides a way to accommodate these sources of influence, observed and unobserved. The small but growing number of applications of the GMXL model have parameterized scale heterogeneity as a single estimate; however it is often the case that analysts pool data from more than one source, be it revealed preference (RP) and stated preference (SP) sources, or multiple SP sources, inducing the potential for differences in the scale factor between the data sources. Existing practice has developed ways of accommodating scale differences between data sources by adopting a scale homogeneity assumption within each data source (e.g., the nested logit trick) that varies between data sources. This paper extends the state of the art by incorporating data-source specific scale differences in scale heterogeneity setting across pooled RP and SP data set. An example of choice amongst RP and SP transport modes (including two ‘new’ SP modes) is used to obtain values of travel time savings that vary significantly between a model that accounts for scale heterogeneity differences within pooled RP and SP data, and the other where differences in scale heterogeneity is also accommodated between RP and SP data.