Modelling traveler’s behavior with respect to mode choice is crucial for effective planning of future transport networks, policy testing and analysis of existing transportation systems. Particularly in developing countries like India, switching between different modes are very common in trip making of a traveler due to availability of multiple modes. Hence, identification of proper mode choice combination plays a vital role in development of a mode choice model. Although mode choicemodelling studies were conducted before, they were based on the four stage modelling approach like the mode choicemodelling of work trips/leisure trips etc. Since the need or desire to engage in an activity at a different location generates a trip, it is the activity based approach which can be used more effectively to explain the mode choice behavior. The reason is that only the activity-based approach explicitly recognizes the fact that the demand for activities produces the demand for travel. This study is an effort to understand the mode choice behavior of rural residents based on activity based approach.
If the Council chooses to use offsetting mitigation as a policy instrument then it is immediately confronted with the problem of deciding on the location and level of mitigation needed to compensate for adverse effects. Key attributes of stream quality can be described from an ecological perspective using descriptors such as nutrient levels, flow rates, condition of indicator species, and so on. The cost of achieving mitigation can also be quantified. However, ecological descriptors and information on mitigation costs are not sufficient to undertake an assessment of benefits and costs as required under the Resource Management Act. Information on community mitigation preferences is needed. Without information on the benefits of mitigation the Council has no rational basis for comparing costs and benefits. Choicemodelling is a technique that has been recently developed for the valuation of environmental changes. The idea underlying choicemodelling is relatively straightforward. People are asked to indicate their single preferred choice from a set of options. Each option is described by several attributes and the levels of those attributes vary across the options. Statistical methods are used to develop a mathematical model of preferences based on the choices that people made.
The purpose of the choice experiment is to gain an understanding of the values that the community places on the effects of wasp incursions on native species. The idea underlying choicemodelling is relatively simple. Alternative attributes of the beech forest ecosystem are defined using information on the biology of wasps and their likely impact. These attributes are then combined into alternative states of the beech forest that are presented as options to individuals, who are then asked to choose their single preferred state. In 2008 two focus group meetings – one in Auckland and the other in Christchurch – identified salient attributes of wasp incursions and their impact on the ecosystem. Results from the two focus group meetings formed the basis for designing the choice sets for the actual experiment. In general, focus group participants were aware of the potential for wasp invasions and some of the consequences, but had little understanding of potential ecological implications.
For only a few people is transport an end in itself. Most need to travel to be able to perform a certain activity, e.g. work, education or shopping. Since activities are carried out at different locations, people tend to make trips between these locations. This results in mobility and has its effect on daily society life. This chapter gives an introduction to the study presented in this report on dynamic traffic assignment route choicemodelling. Paragraph 1.1 gives a short introduction on the growing mobility that partly forms the research motive pointed out in paragraph 1.2. In paragraph 1.3 the research objective and questions are presented. Paragraphs 1.4 and 1.5 describe the context of the study and scope of research respectively. This chapter concludes in paragraph 1.6 by giving an outline of the report contents.
Constructing a list of the physical attributes of a product is reasonably easy; we can measure size, colour, crispness, weight etc with little trouble. What becomes difficult is to identify and quantify the more emotional or intangible attributes that are part of the make-up of an organic food product. The research reviewed in sections 2.5 and 2.6 details some ideas which are a good pointer towards additional attributes the consumer may see and consider of value when appraising an organic food product. For example, an organic food’s chemical free status is such an attribute that would not exist in an otherwise identical but non-organic product and so add value to that organic product. Lancaster’s (1966) view of looking at a product as a collection of attributes was an interesting theory but at the time of its conception lacked a statistical method that would enable empirical analysis of products viewed in this way to be undertaken. A following section on choicemodelling will discuss the system that was created to enable such analysis to be performed.
This paper makes contributions to both the transportation and choicemodelling literatures. The choice environment proposed is well suited to a range of transportation choices, including air travel, rail, coach, public transport, car hire and even route choice, for it mimics a growing array of market offerings. These choice environments have unique properties that provide the analyst with opportunities to gain a deeper understanding of choices processes, and preference heterogeneity. Whilst models have begun to be developed in the marketing literature for online choice environments that are well suited to marketing problems (e.g. Moe 2006), such endeavours have received scant attention in the transportation field. Within the broader choicemodelling literature, much attention has been given in recent years to modelling unobserved preference heterogeneity, principally through the now ubiquitous mixed logit and latent class models. This represents a move away from the modelling of observed preference heterogeneity through the specification of interactions with socio-demographic variables. This paper has demonstrated that search and sort observations may be another way to retrieve and understand preference heterogeneity.
This report is focused on the results of a pilot ChoiceModelling valuation exercise which was developed to determine the potential supply of riparian buffer rehabilitation by landholders in the Fitzroy Basin, in order to provide essential information to assist in the evaluation and design of a MBI to achieve water quality outcomes in the catchment. The technique has traditionally been applied to environmental valuation issues, but there is increasing use of CM to design agricultural markets (Lusk and Hudson 2004). This study extends that application to markets for environmental actions. It investigates landholders’ preference heterogeneity in willingness-to-accept direct monetary incentives for the rehabilitation/restoration of riparian buffers. By predicting a supply function for riparian vegetation, the design of incentive structure can then be addressed. The demand for water quality improvements in the Fitzroy Basin has been assessed in a series of CM valuations (eg Rolfe et al. 2002; Rolfe and Bennet 2003; Windle and Rolfe 2002; 2004) and is synthesised in Rolfe et al. (2004b).
This paper focuses on the design of a ChoiceModelling (CM) valuation exercise to determine the potential supply of riparian buffer rehabilitation by landholders in the Fitzroy Basin, in order to provide essential information to assist in the evaluation and design of a MBI to achieve water quality outcomes in the catchment. The technique has traditionally been applied to environmental valuation issues, but there is increasing use of CM to design agricultural markets (Lusk and Hudson 2004). This study extends that application to markets for environmental actions. It investigates landholders’ preference heterogeneity in willingness-to-accept direct monetary incentives for the
Table 5 shows MWTP estimates for each of the non- cost attributes studied and the associated confidence intervals around these estimates. A positive MWTP d indicates a preference for an attribute taking into ac- count the associated cost, whilst a negative MWTP d indicates a dispreference. As observed in Table 4, there are subtle differences in the MWTP estimates obtained from the different econometric models. We focus on estimates from the LCMNL model as this provided the best fit with the choice data collected. This shows a statistically significant and substantial willingness-to-pay for drug delivery modes that are associated with “moderate” disruption to daily activ- ities and “moderate” risk of adverse events. There is also a statistically significant and substantial willingness-to-pay to avoid drug delivery modes that are associated with “moderate but unmanageable” or “severe” disruptions to daily activities. Similarly, there is a statistically significant and substantial willingness- to-pay to avoid drug delivery modes that are associ- ated with “severe” risk of adverse events. Further, there is a statistically significant and substantial willingness-to-pay to avoid drug administration via the intramuscular route.
Tourist preferences are elicited by means of the choice experiment technique. As is well known, the use of standard econometric techniques for the analysis of discrete choice data permits to generate estimates of the relative importance of the attributes, and to obtain a monetary evaluation of each of them. It is worthy to remind that stated preference approaches are often the only empirical methodology available for demand analysis, given the absence of detailed data and the need to evaluate new policies and interventions. Moreover, a stated preference analysis avoids simultaneity problems which would characterize a study based on real markets data (as a simple example, think of the bi-directional link between overcrowding and tourist demand).
The systematic content analysis has been used successfully within studies across different disciplines in science (see e.g. Li & Cavusgil, 1995 or Bontekoning et al., 2004) and appears an adequate method to cover most of relevant studies. We used computerised literature research to find significant literature, since it is fast, easy and efficient and allows accessing to a huge database referring transport research articles. Although there is some limitation towards accessing all articles (e.g. articles published before the 80s, if not digitalised) most articles dealing with the topic were found because the most relevant ones have been published in last two decades, primarily. The selection of proper search keys (key words) is a main factor of success towards identifying relevant articles/studies. The following search keys were used in different combinations for the computerized literature research: freight, transport, chain, demand, model/modelling, choice, logistics. In order to evaluate the relevance of sources different selection criteria were applied. A pre-selection of the contribution was applied in the first round if the search keys were found in the title, abstract and the key word section of the articles. Secondly, a further selection of articles was conducted regarding the search keys within the content section of the contribution.
The importance of mode choice in transportation policy analysis and decision making has led to a variety of methods for predicting the effects of policy measures on tra vellers’ mode choices . Residential choice location is influenced by many variables including socio-economic characteristics, life cycle, location of work and other major activities such as schools, shopping, family and friend, real estate values, and characteristics of the residential and workplace area. Living close to the workplace reduces vehicle kilometre of travel and thus contribute to a more sustainable transportation system. .
airlines at the three airports (available from the Bureau of Transportation Statistics) was appended to the dataset. The ground-transportation dataset contains information on travel distance, travel time and tolls for car travel, under peak and off-peak conditions, and for varying car-occupancy (which has an impact on tolls and the eligibility to use car-pool lanes). Similarly, the dataset contains information on access-time, wait time, travel time, egress time and fares for public transport journeys. All information is available for peak and off-peak conditions, and this was taken into account in the specification of the choice-set for the different individuals included in the sample. Corresponding values for other modes, such as taxi, limousine and special airport bus services were calculated separately, based on current prices and the changes in the Consumer Price Index for California from August and October 1995 to September 2003. Due to data limitations (c.f. 6), no difference could be made between rental cars and private cars, and parking-cost could not be taken into account. Attempts to explicitly account for the marginal running costs for car journeys were unsuccessful, such that only toll-costs were included in the modelling analysis. Six access-modes were used in the analysis; car, public transport (transit), scheduled airport bus services, door-to-door services, taxi and limousine, where it was assumed that car, taxi and limousine are available for each origin, while the availability of the remaining three modes was location-dependent.
of various attributes, which for the means of cyclist route choice are relevant or important. Since some research studies are a case study or are dealing with a specific environment of cycling, they have certain perceptions for attributes, but mostly the main attributes considered are length or travel time, gradient, existence of bicycle facility such as cycle lanes, intersections, age and experience of cyclist and traffic volume. Sometimes this kind of modelling has been studied altogether with pedestrian traffic (Westerdijk, 1990). Goldsmith (1992) depicts cyclist routings as, “The consideration of factors beyond travel time and distance are particularly important for bicycling as cycling levels are considered to be also affected by many additional characteristics of the network such as road type and automobile volumes”. This gives a clear vision that unlike vehicle routing, cyclists don’t have the time-distance criteria as the main priority, and a few quality matters, concerns their route-choice. In some cases, cyclist’s shortest route to destination would have to pass over a rail track or other obstacles, which would be avoided by cyclists.
Stated preference-based discrete choicemodelling research (Bovy and Bradley 1985; Hopkinson and Wardman 1996; Abraham et al. 2002; Stinson and Bhat 2003) has shown time and safety to be the greatest determinants of a cyclist’s route choice. These studies also show the preference of cyclists for off-road and quieter routes. There is scope for extending research into cyclist route choice to incorporate more detailed analysis of cycle facilities, variation by socio-economic classification, and other variables such as topography and weather. A preference for off-road cycling has also been found in cyclist mode choicemodelling of stated preference data. For example, the model of Ortúzar et al. (2000) shows that segregated cycleways could produce increases in bicycle use of as much as 10% mode share for certain sectors of Santiago.
The aim of this research is to use a stated preference approach, choicemodelling, to determine the non-market value rural landowners place on biodiversity on agricultural land. It employs different attributes for biodiversity, and a payment vehicle of an annual contribution, for a 10-year period, into a council designated fund to which farmers can apply for funding to take actions to enhance indigenous biodiversity on their land. The focus of this study is the Waikato Region, due to its diversity of native flora and fauna and the pressures placed on it from the region's strong agriculture based economy. An online survey was used to survey rural landowners in the region. Usable responses were obtained from 146 respondents, three-quarters of whom operate their own farm and two-thirds of whom have indigenous biodiversity present on their farm.
The second research question for this study is ‘what modelling approaches from other fields could be used to model the impact of ‘smarter choices’ programmes on the mode chosen for commuting trips?. The previous chapter reviewed extensions to the current four stage model that could assist in modelling ‘smarter choices’ such as using finer zones and more detailed networks to achieve a better representation of travel times and costs, the inclusion of latent variables such as attitudes in the choicemodelling and implementing greater segmentation of travellers so as to better group them according to their preferences. Latent class analysis provides a useful clustering technique for identifying such groupings and allocating individuals to the appropriate segment. Even if these enhancements are made though, there will always be difficulties arising from the holding of information in the form of matrices rather than having the trip information associated with particular individuals. The framework also does not lend itself to the modelling of some ‘smarter choices’ such as car sharing which require very detailed knowledge on the potential car sharers and their working arrangements.
Choice experiment attributes and their corresponding levels were chosen based on an extensive review of literature, identifying features of water resources most important to both Māori and non-Māori, and in consideration of relevant issues in the Waikato region. Harmsworth and Warmenhoven (2002), in their development of Māori community goals for enhancing ecosystem health, and Tipa and Tierney (2006), in their development of the CHI, outlined a number of attributes that were important to Māori. Similarly, important attributes to non-Māori were selected based on Kerr and Sharp’s (2003) study of community mitigation preferences and Kerr and Swaffield’s (2007) extensive review of attributes used in choicemodelling. Recurring attributes included; water clarity, water quality, water safety, wildlife habitat, ecosystem abundance and diversity, river flow and levels, riverbank condition, riverbank vegetation, access, recreation, water use, and surrounding land use. In consideration of the potential problems with asking Māori to put a money value on water, alternative cost numeraires considered were the number of local jobs (Marsh, 2010) and change to the regional economy (Mallawaarachchi et al., 2001; Rolfe et al., 2000).
sets. To this extent, air-travel level-of-service data were obtained from BACK Aviation Solutions 1 , containing daily information on the different operators serving the selected routes for the time period used in the present analysis (August and October 1995). Besides the frequencies for the different operators, the dataset also contains information on the average fares paid on a given route operated by a given airline. This clearly involves a great deal of aggregation, as no distinction is made between the fares for the different classes of travel. Furthermore, as no information on advance purchase discounts at the time booking was available, it had to be assumed that fares stay constant, and that availability of a specific fare on a given day is the same across all airports offering that route. Unfortunately, such assumption cannot in general be avoided in the area of airport-choicemodelling, given the lack of adequate data on fares. A number of other attributes were included in the datasets; these were however not used in the present analysis (Hess and Polak, 2004a; 2004b). As the present study ignores the airline-choice dimension, aggregate air-travel level-of-service data were used, assigning to each passenger the industry-level information on frequencies and fares for flights from each of the three airports to the desired destination on the actual date of travel.