Demand and welfare effects in recreational travel models: Accounting for substitution between number of trips and days to stay

In this paper we present a non-linear demand system for households’ joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO₂ tax. In particular, we focus on the effect of including substitution between households choice of the number of trips and days to spend at a destination in the welfare analysis. The analysis reveals that the equivalent variation (EV) measure, for the count data demand system, can be seen as an upper bound for the households welfare loss. Approximating the welfare loss by the change in consumer surplus, accounting for the positive effect from longer stays, imposes a lower bound on the households welfare loss. The difference in the estimated loss measures, from the considered CO₂ tax reform, is about 20%. This emphasizes the importance of accounting for substitutions toward longer stays in travel demand policy evaluations.     

The propensity for motorists to walk for short trips: Evidence from West Edinburgh

The trade-offs individuals make between the motor car and walking for short trips are investigated, using data from a West Edinburgh household survey. Propensity to walk discrete choice models are estimated from a stated preference experiment within the survey questionnaire. This includes segmented models using socio-economic, spatial and attitudinal variables. The relative importance of the three attributes of journey time, petrol cost and parking cost are examined; value of time estimates are also generated. With all other factors remaining constant, for short trips motorists are more likely to walk in response to an increase in parking costs than a rise in petrol prices. The use of parking as a transport policy tool is discussed.     

Correcting for endogeneity in behavioral choice models with social influence variables

While psychologists and behavioral economists emphasize the importance of social influences, an outstanding issue is how to capture such influences in behavioral models used to inform urban planning and policy. In this paper we focus on operational models that do not require explicit knowledge of the individual networks of decision makers. We employ a field effect variable to capture social influences, which is calculated as the percent of population in the peer group that has chosen the specific alternative. We define the peer group based on socio-economic status and spatial proximity of residential location. As in behavioral economics and psychology, the concept is that one is influenced by the choices made by one’s peers. However, using such a social influence variable in a behavioral model causes complications because it is likely endogenous; unobserved factors that impact the peer group also influence the decision maker, yielding correlation between the field effect variable and the error. The contribution of this paper is the use of the Berry, Levinsohn, and Pakes (BLP) method to correct the endogeneity in a choice model. The two-stage BLP introduces constants for each peer group to remove the endogeneity from the choice model (where it is difficult to deal with) and insert it into a linear regression model (where endogeneity is relatively easier to deal with). We test the method using a mode choice data set from the Netherlands and readily available software and find there is an upward bias of the field effect parameter when endogeneity is not corrected. The procedure outlined presents a practical and tractable method for incorporating social influences in choice models.     

Relationship between proximity to transit and ridership for journey-to-work trips in Chicago

The use of privately owned vehicles (POVs) contributes significantly to US energy consumption (EC) and greenhouse gas emissions (GHGe). Strategies for reducing POV use include shifting trips to other modes, particularly public transit. Choices to use transit are based on characteristics of travelers, their trips, and the quality of competing transportation services. Here we focus on the proximity of rail stations to trip origins/destinations as a factor affecting mode choice for work trips. Using household travel survey data from Chicago, we evaluate the profile of journey-to-work (JTW) trips, assessing mode share and potential for more travelers to use rail. For work trips having the origin/destination as close as 1 mile from rail transit stations, POVs were still the dominant travel mode, capturing as much as 61%, followed by rail use at 14%. This high degree of POV use coupled with the proportion of JTW trips within close proximity to rail stations indicated that at least some of these trips may be candidates for shifting from POV to rail. For example, shifting all work trips with both the origin/destination within 1 mile of commuter rail stations would potentially reduce the energy associated with all work-related POV driving trips by a maximum of 24%. Based on the analysis of trips having the origin and destination closest to train stations, a complete shift in mode from POV to train could exceed CO₂ reduction goals targeted in the Chicago Climate Action Plan. This could occur with current settlement patterns and the use of existing infrastructure. However, changes in traveler behavior and possibly rail operation would be necessary, making policy to motivate this change essential.     

Modeling residential sorting effects to understand the impact of the built environment on commute mode choice

This paper presents an examination of the significance of residential sorting or self selection effects in understanding the impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found that built environment attributes can indeed significantly impact commute mode choice behavior. The paper concludes with a discussion of the implications of the model findings for policy planning.

A dichotomous choice survey for quantifying option and non-use values of bus services in Korea

The total economic value for a transportation service consists of use, option, and non-use value. The use benefit is based on a traveler’s willingness to pay for usual consumption of the service. The optional value, on the other hand, is related to the possible use of the service for trips not yet anticipated or currently accommodated by other travel modes. The non-use value, however, is derived from the intrinsic merit of the service, even though a trip-maker never actually or potentially depends on the mode. A closed-ended contingent valuation method is considered for the quantification of the option and non-use values. A survey of single- and double-bounded dichotomous choices is conducted with a case study of South Korean bus operations. A logistic regression model and a survival analysis for the single- and double-bounded approaches, respectively, are applied. The estimation result is examined according to the statistical property required and the behavioral validity expected. In particular, three issues from the output are discussed. First, the results help to show the preferable framework between single- and double-bounded surveys for addressing an individual’s option and non-use values. Second, the differences in the absolute values of option and non-use values are compared. Thirdly, the relationship between trip-makers’ willingness to pay and the level of service of their primary travel modes are investigated. In conclusion, the summary of research and the possibilities for future studies are given.     

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.     

Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design

There is a growing interest in traveller behaviour research to explore alternative information processing strategies (often referred to as heuristics or rules) adopted by individuals when assessing packages of attributes describing alternatives in a choice set, and making a choice. One popular attribute processing rule relates to attributes not being considered (i.e., being ignored), for all manner of reasons, referred to in the small but growing literature as attribute non-attendance or non-preservation. Researchers have used a mixture of methods to study the role of attribute non-attendance, including supplementary questions on whether each attribute is ignored or not, and methods in which the functional form of the utility expressions defining an alternative can recognise the possibility, up to a probability, of an attribute being ignored. Although supplementary questions are worthy of further consideration, despite the controversy as to the reliability of the response, recent interest has focused on ways to establish the incidence of attribute non-attendance without recourse to such evidence. In this paper we use an existing data set of choice amongst four attributes describing alternative car non-commuting trips, to illustrate the proposed method, and to compare values of travel time savings under each possible combination of non-attendance attributes relative to a model in which all attributes are assumed to be fully attended to. The paper reveals a major concern with the way that attribute levels and ranges are selected in the design of choice experiments, which can induce non-attendance situations where willingness to pay estimates cannot be obtained.     

Modeling residential location choice in relation to housing location and road tolls on congested urban highway networks

We present a reformulation of the residential location submodel of the Integrated Model of Residential and Employment Location as a network equilibrium problem, thereby making travel costs by auto endogenous. The location of housing supply is examined as a welfare maximization problem for both user-optimal and system-optimal travel costs using concepts of bilevel programming. Finally, we briefly discuss how the employment submodel can be reformulated, and the entire model solved as a variational inequality problem.     

Modeling the bilateral micro‐searching behavior for urban taxi services using the absorbing markov chain approach

This paper develops a mathematical model that is based on the absorbing Markov chain approach to describe taxi movements, taking into account the stochastic searching processes of taxis in a network. The local searching behavior of taxis is specified by a logit form, and the O‐D demand of passengers is estimated as a logit model with a choice of taxi meeting point. The relationship between customer and taxi waiting times is modeled by a double‐ended queuing system. The problem is solved with a set of non‐linear equations, and some interesting results are presented. The research provides a novel and potentially useful formulation for describing the urban taxi services in a network.     

Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars

Recently, policy makers’ expectations about the role of electric cars in reducing emissions have risen substantially. In parallel, academic research on purchase intentions has dramatically increased. Originally, most studies have focused on utility attributes and price. More recently, several hybrid choice models have been estimated to include the impact of attitudes on choice probabilities. In addition, a few studies have caught the attention to social influence. In contributing to this line of research, this paper reports the results of an expanded hybrid choice, which simultaneously estimated all these different effects in a single integrated model of purchase intention. Results indicate that the model performs well. Costs considerations contribute most to the utility of electric cars. Social influence is less important, but there is also evidence that people tend to take it into consideration when there are positive public opinions about electric cars and the market share becomes almost half of friends of their social network. The intention to purchase an electric car is also influenced by attitudes about environmental concerns and technology acceptance.     

Importance of traveler attitudes in the choice of public transportation to work: findings from the Regional Transportation Authority Attitudinal Survey

The commute mode choice decision is one of the most fundamental aspects of daily travel. Although initial research in this area was limited to explaining mode choice behavior as a function of traveler socioeconomics, travel times, and costs, subsequent studies have included the effect of traveler attitudes and perceptions. This paper extends the existing body of literature by examining public transit choice in the Chicago area. Data from a recent Attitudinal Survey conducted by the Regional Transportation Authority (RTA) in Northeastern Illinois were used to pursue three major steps. First, a factor analysis methodology was used to condense scores on 23 statements related to daily travel into six factors. Second, the factor scores on these six dimensions were used in conjunction with traveler socioeconomics, travel times, and costs to estimate a binary logistic regression of public transit choice. Third, elasticities of transit choice to the six factors were computed, and the factors were ranked in decreasing order of these elasticities. The analysis provided two major findings. First, from a statistical standpoint, the attitudinal factors improved the intuitiveness and goodness-of-fit of the model. Second, from a policy standpoint, the analysis indicated the importance of word-of-mouth publicity in attracting new riders, as well as the need for a marketing message that emphasizes the lower stress level and better commute time productivity due to transit use.     

Parameter transfer of common-metric attributes in choice analysis: implications for willingness to pay

There is a growing literature that promotes the presence of a mix of compensatory and semi-compensatory processing strategies in the way that individuals evaluate packages of attributes in real or hypothetical markets, and make choices. This paper proposes a specification for the utility form in a choice model to test if, given a pair of attributes with a common-metric (e.g., components of travel time or cost), the attribute with the dominating level defines the marginal (dis)utility that is assigned to both attributes. We refer to this processing strategy as a parameter transfer rule. We use a stated choice data set, in the context of car driving individuals choosing between tolled and non-tolled routes, to estimate a mixed logit model which incorporates the presence of the parameter transfer rule and the conventional fully compensatory rule, both existing up to a probability. We find that if this parameter transfer heuristic is part of the mix, the WTP is more than 30% higher, on average, than when only a fully compensatory rule is imposed. We also contrast the parameter transfer rule with other semi-compensatory heuristics which have been investigated in other papers, and show that the finding adds further support to the accumulating evidence that a semi-compensatory attribute processing rules tend to result in higher mean WTP estimates compared to the fully compensatory attribute processing rule.     

Modeling NOx and NO2 emissions from mobile sources: A case study for Hamilton, Ontario, Canada

This study characterizes the dispersion of emissions of oxides of nitrogen and nitrogen dioxide from traffic sources in the Hamilton census metropolitan area. The Integrated Model of Urban Landuse and Transportation for Environmental Analysis software was used to estimate emissions of oxides of nitrogen from traffic sources. The Air Pollution Model, was used to simulate dispersion of the traffic emissions over the city. The models account for the two primary wind directions in this region – the prevailing southwest and a secondary northeast direction. The results show a prominent triangle of high pollution defined by major roads and highways along the periphery of the Hamilton Harbour, at peak-hour. With southwest winds, residential areas along the northern shoreline of the Harbour are affected, while the western sections of the city are affected by northeast winds. High concentrations are persistent in some areas, for both wind directions. The resulting dispersion surfaces characterize the spatial distribution of traffic emissions and thus provide a means of assessing population exposure over the Hamilton area.     

Treatment of seasonal tickets for public transport in estimation and application of mode/destination choice models

A new model system dealing with trips of length up to 100 km has recently been developed in Norway. A new way of dealing with seasonal passes for public transport is used in the travel-to-work model. The objective was to account for the fact that a respondent that posses a seasonal pass for public transport may behave as if public transport is free on the day they report a travel diary. On the other hand, we can not assume that public transport is free for respondents that used other modes of transport or that public transport is free to alternative destinations. This problem was solved by defining seasonal pass as a separate alternative in the form of a nest that included all modes of travel. The cost of a seasonal pass is a common cost for all modes in the nest and will thus not affect the choice within the nest. The estimation of this specification is compared with the more common approach of assigning an average cost per day based on the cost of a monthly pass and the number of workdays in a month. The comparison indicates that the “average cost per day” approach may produce biased estimates for several parameters. It also turns out that the cost parameter for seasonal pass is higher than the parameter for “out of pocket” cost, probably reflecting that there will be some uncertainty with respect to the actual use of a seasonal pass.     

A methodology to derive the critical demand density for designing and operating feeder transit services

Feeder lines are one of the most often used types of flexible transit services connecting a service area to a major transit network through a transfer point. They often switch operations between a demand responsive and a fixed-route policy. In designing and running such systems, the identification of the condition justifying the operating switch is often hard to properly evaluate. In this paper, we propose an analytical model and solution of the problem to assist decision makers and operators in their choice. By employing continuous approximations, we derive handy but powerful closed-form expressions to estimate the critical demand densities, representing the switching point between the competing operating policies. Based on the results of one-vehicle and two-vehicle operations for various scenarios, in comparison to values generated from simulation, we verify the validity of our analytical modeling approach.     

A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area

Airport choice is an important air travel-related decision in multiple airport regions. This paper proposes the use of a probabilistic choice set multinomial logit (PCMNL) model for airport choice that generalizes the multinomial logit model used in all earlier airport choice studies. The paper discusses the properties of the PCMNL model, and applies it to examine airport choice of business travelers residing in the San Francisco Bay Area. Substantive policy implications of the results are discussed. Overall, the results indicate that it is important to analyze the choice (consideration) set formation of travelers. Failure to recognize consideration effects of air travelers can lead to biased model parameters, misleading evaluation of the effects of policy action, and a diminished data fit.