Flexible spatial dependence structures for unordered multinomial choice models: formulation and application to teenagers’ activity participation

The current article proposes an approach to accommodate flexible spatial dependency structures in discrete choice models in general, and in unordered multinomial choice models in particular. The approach is applied to examine teenagers’ participation in social and recreational activity episodes, a subject of considerable interest in the transportation, sociology, psychology, and adolescence development fields. The sample for the analysis is drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) as well as other supplementary data sources. The analysis considers the effects of a variety of built environment and demographic variables on teenagers’ activity behavior. In addition, spatial dependence effects (due to common unobserved residential neighborhood characteristics as well as diffusion/interaction effects) are accommodated. The variable effects indicate that parents’ physical activity participation constitutes the most important factor influencing teenagers’ physical activity participation levels, In addition, part-time student status, gender, and seasonal effects are also important determinants of teenagers’ social-recreational activity participation. The analysis also finds strong spatial correlation effects in teenagers’ activity participation behaviors.     

A multivariate ordered-response model system for adults’ weekday activity episode generation by activity purpose and social context

This paper proposes a multivariate ordered-response system framework to model the interactions in non-work activity episode decisions across household and non-household members at the level of activity generation. Such interactions in activity decisions across household and non-household members are important to consider for accurate activity-travel pattern modeling and policy evaluation. The econometric challenge in estimating a multivariate ordered-response system with a large number of categories is that traditional classical and Bayesian simulation techniques become saddled with convergence problems and imprecision in estimates, and they are also extremely cumbersome if not impractical to implement. We address this estimation problem by resorting to the technique of composite marginal likelihood (CML), an emerging inference approach in the statistics field that is based on the classical frequentist approach, is very simple to estimate, is easy to implement regardless of the number of count outcomes to be modeled jointly, and requires no simulation machinery whatsoever.The empirical analysis in the paper uses data drawn from the 2007 American Time Use Survey (ATUS) and provides important insights into the determinants of adults’ weekday activity episode generation behavior. The results underscore the substantial linkages in the activity episode generation of adults based on activity purpose and accompaniment type. The extent of this linkage varies by individual demographics, household demographics, day of the week, and season of the year. The results also highlight the flexibility of the CML approach to specify and estimate behaviorally rich structures to analyze inter-individual interactions in activity episode generation.     

On allowing a general form for unobserved heterogeneity in the multiple discrete–continuous probit model: Formulation and application to tourism travel

This paper proposes a new econometric formulation and an associated estimation method for a finite discrete mixture of normals (FDMN) version of the multiple discrete–continuous probit (MDCP) model. To our knowledge, this is the first such formulation and application of an MDCP model in the econometric literature. Using the New Zealand Domestic Travel Survey data set, the model is applied to analyze individual-level decisions regarding recreational destination locations and the number of trips to each destination. The results provide insights into the demographic and other factors that influence individuals’ preferences for different destinations, and show that the FDMN MDCP model is able to identify different segments of the sample, each one of them with different effects of the exogenous variables on destination choice.     

Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model

In this paper, we apply Bhat and Dubey’s (2014) new probit-kernel based Integrated Choice and Latent Variable (ICLV) model formulation to analyze children’s travel mode choice to school. The new approach offered significant advantages, as it allowed us to incorporate three latent variables with a large data sample and with 10 ordinal indicators of the latent variables, and still estimate the model without any convergence problems. The data used in the empirical analysis originates from a survey undertaken in Cyprus in 2012. The results underscore the importance of incorporating subjective attitudinal variables in school mode choice modeling. The results also emphasize the need to improve bus and walking safety, and communicate such improvements to the public, especially to girls and women and high income households. The model application also provides important information regarding the value of investing in bicycling and walking infrastructure.     

Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan

This paper conducts a comparative discrete choice analysis to estimate consumers’ willingness to pay (WTP) for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) on the basis of the same stated preference survey carried out in the US and Japan in 2012. We also carry out a comparative analysis across four US states. We find that on average US consumers are more sensitive to fuel cost reductions and alternative fuel station availability than are Japanese consumers. With regard to the comparative analysis across the four US states, consumers’ WTP for a fuel cost reduction in California is considerably greater than in the other three states. We use the estimates obtained in the discrete choice analysis to examine the EV/PHEV market shares under several scenarios. In a base case scenario with relatively realistic attribute levels, conventional gasoline vehicles still dominate both in the US and Japan. However, in an innovation scenario with a significant purchase price reduction, we observe a high penetration of alternative fuel vehicles both in the US and Japan. We illustrate the potential use of a discrete choice analysis for forward-looking policy analysis, with the future opportunity to compare its predictions against actual revealed choices. In this case, increased purchase price subsidies are likely to have a significant impact on the market shares of alternative fuel vehicles.     

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

A discrete dynamical system of formulating traffic assignment: Revisiting Smith’s model

Through relaxing the behavior assumption adopted in Smith’s model (Smith, 1984), we propose a discrete dynamical system to formulate the day-to-day evolution process of traffic flows from a non-equilibrium state to an equilibrium state. Depending on certain preconditions, the equilibrium state can be equivalent to a Wardrop user equilibrium (UE), Logit-based stochastic user equilibrium (SUE), or boundedly rational user equilibrium (BRUE). These equivalence properties indicate that, to make day-to-day flows evolve to equilibrium flows, it is not necessary for travelers to choose their routes based on actual travel costs of the previous day. Day-to-day flows can still evolve to equilibrium flows provided that travelers choose their routes based on estimated travel costs which satisfy these preconditions. We also show that, under a more general assumption than the monotonicity of route cost function, the trajectory of the dynamical system converges to a set of equilibrium flows by reasonably setting these parameters in the dynamical system. Finally, numerical examples are presented to demonstrate the application and properties of the dynamical system. The study is helpful for understanding various processes of forming traffic jam and designing an algorithm for calculating equilibrium flows.     

Factors affecting children’s journeys to school: a joint escort-mode choice model

A child’s mode of travelling to school is influenced by, or dependent on, parental choices. Thus, an increasing proportion of car trips may reflect parental choices and constraints. Whether a parent can escort their children to school may depend on their scheduling and spatial constraints, e.g., work schedule and job location in relation to home and school locations. This research aims to understand the effect of household bundling constraints on a child’s escort-mode choice. In this study, school trip data are drawn from the 2001 SCAG (Southern California Association of Governments) Post Census Regional Household Travel Survey. The study area is the five-county Los Angeles region. Our findings show that the parents’, especially the mother’s, increased working hours and more distant job locations result in an increased likelihood of several alternative escort-mode choices. Mothers who work longer hours and further away from home are less likely to chauffeur their children. These trips have been substituted by alternative escort choices such as independent travel and being escorted by fathers, or alternative mode choices such as active commuting and busing. The effect of increased working hours may be offset by the option of flexible working hours, which allows parents to arrange more escort trips. This study elucidates an important aspect in explaining children’s changing mode choice in journeys to school and sheds light on current policy efforts in reducing children’s car dependency.     

Developing an integrated scobit-based activity participation and time allocation model to explore influence of childcare on women’s time use behaviour

Focusing on the influence of childcare on women’s time use behaviour, this paper develops an integrated model of activity participation and time allocation, where the former is represented based on a scobit model and the latter based on a multi-linear utility function under the utility-maximizing principle. The integration of the scobit model with the time allocation model is done by applying Lee’s transformation. Especially, the scobit model is adopted to relax the assumption, made in the Logit or Probit model, that individuals having indifferent preferences over participation and non-participation are most sensitive to changes in explanatory variables. Using a large-scale time use data (66,839 persons) collected in Japan, the effectiveness of the proposed integrated model is empirically confirmed. It is revealed that the probabilities of participating in compulsory-contracted activities and discretionary activities with the highest sensitivity to changes in explanatory variables are 65 and 81%, respectively. Variances of social childcare variables explain about half of the total variance of the time use for discretionary activities; however, for compulsory-contracted activities, social childcare variables explain only less than 1% of the total variance of activity participation and less than 10% of total variable of time allocation.     

Foreign participation and competition: A way to improve the container port efficiency in China?

This paper investigates how the involvement of foreign and local ownerships, intra- and inter-port competition and hinterland affect the container terminal efficiency in China and its neighboring countries. The operational efficiency of sample container terminals is estimated by data envelopment analysis, which is followed by regression analysis to examine factors affecting container terminal efficiency. We find that having some Chinese ownership may make a container terminal more efficient, while a container terminal is less efficient with Chinese as the major shareholder. It is also found that intra- and inter-port competition may enhance container terminal efficiency. Finally, the efficiency growth of terminals is examined, and implications for the regional economic disparity in China are discussed.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Children’s mode choice for the school trip: the role of distance and school location in walking to school

Rising levels of childhood obesity in the United States and a 75% decline in the proportion of children walking to school in the past 30 years have focused attention on school travel. This paper uses data from the US Department of Transportation’s 2001 National Household Travel Survey to analyze the factors affecting mode choice for elementary and middle school children. The analysis shows that walk travel time is the most policy-relevant factor affecting the decision to walk to school with an estimated direct elasticity of −0.75. If policymakers want to increase walking rates, these findings suggest that current policies, such as Safe Routes to School, which do not affect the spatial distribution of schools and residences will not be enough to change travel behavior. The final part of the paper uses the mode choice model to test how a land use strategy—community schools—might affect walking to school. The results show that community schools have the potential to increase walking rates but would require large changes from current land use, school, and transportation planning practices.

A partial differential equation formulation of Vickrey’s bottleneck model,part II: Numerical analysis and computation

The Vickrey model, originally introduced in Vickrey (1969), is one of the most widely used link-based models in the current literature in dynamic traffic assignment (DTA). One popular formulation of this model is an ordinary differential equation (ODE) that is discontinuous with respect to its state variable. As explained in Ban et al., 2011, Han et al., 2013, such an irregularity induces difficulties in both continuous-time analysis and discrete-time computation. In Han et al. (2013), the authors proposed a reformulation of the Vickrey model as a partial differential equation (PDE) and derived a closed-form solution to the aforementioned ODE. This reformulation enables us to rigorously prove analytical properties of the Vickrey model and related DTA models.In this paper, we present the second of a two-part exploration regarding the PDE formulation of the Vickrey model. As proposed by Han et al. (2013), we continue research on the generalized Vickrey model (GVM) in a discrete-time framework and in the context of DTA by presenting a highly computable solution methodology. Our new computational scheme for the GVM is based on the closed-form solution mentioned above. Unlike finite-difference discretization schemes which could yield non-physical solutions (Ban et al., 2011), the proposed numerical scheme guarantees non-negativity of the queue size and the exit flow as well as first-in-first-out (FIFO). Numerical errors and convergence of the computed solutions are investigated in full mathematical rigor. As an application of the GVM, a class of network system optimal dynamic traffic assignment (SO-DTA) problems is analyzed. We show existence of a continuous-time optimal solution and propose a discrete-time mixed integer linear program (MILP) as an approximation to the original SO-DTA. We also provide convergence results for the proposed MILP approximation.     

A partial differential equation formulation of Vickrey’s bottleneck model,part I: Methodology and theoretical analysis

This paper is concerned with the continuous-time Vickrey model, which was first introduced in Vickrey (1969). This model can be described by an ordinary differential equation (ODE) with a right-hand side which is discontinuous in the unknown variable. Such a formulation induces difficulties with both theoretical analysis and numerical computation. Moreover it is widely suspected that an explicit solution to this ODE does not exist. In this paper, we advance the knowledge and understanding of the continuous-time Vickrey model by reformulating it as a partial differential equation (PDE) and by applying a variational method to obtain an explicit solution representation. Such an explicit solution is then shown to be the strong solution to the ODE in full mathematical rigor. Our methodology also leads to the notion of generalized Vickrey model (GVM), which allows the flow to be a distribution, instead of an integrable function. As explained by Han et al. (in press), this feature of traffic modeling is desirable in the context of analytical dynamic traffic assignment (DTA). The proposed PDE formulation provides new insights into the physics of The Vickrey model, which leads to a number of modeling extensions as well as connection with first-order traffic models such as the Lighthill–Whitham–Richards (LWR) model. The explicit solution representation also leads to a new computational method, which will be discussed in an accompanying paper, Han et al. (in press).     

Man and his transport behaviour Part 4a. User response to price changes: application of the ‘threshold’ concept

Summary

(1) The response of an individual consumer to change in such characteristics as price will be to change behaviour at a critical point, a ‘threshold’ at which a change of behaviour is perceived to be beneficial.

(2) Most choices can be viewed as binary, for example, between pairs of transport modes. A cumulative normal distribution of responses will give an S‐shaped curve, the mid‐point being at the average threshold value.

(3) An aggregate demand curve should show the response of a given group of people to a range of price changes at one point in time. Most curves derived from revealed behaviour do not permit this. To some extent, a demand curve must be derived from interviews and other tests, giving hypothetical behaviour. Such methods are used in non‐transport consumer tests, and work by Brög et al. gives a similar picture for transport users, supporting the concept of the S‐shaped curve.

(4) Allowance for frequency of trip‐making modifies this picture, suggesting that a smoother curve may be appropriate for some conditions, such as non‐work trips. These approaches may be combined by use of catastrophe theory, with two control factors. The hysteresis effect is found around the threshold where repeated changes in the basic stimulus produce successively smaller responses.

(5) There is some evidence of symmetrical response by public transport users to real increases and reductions in cash‐paid graduated fares, but this is not the case where different forms of pricing are involved.

(6) An example of threshold effects in private transport may be found in the monitoring of tolls on the Itchen Bridge by Atkins. Demand became particularly sensitive to price in a certain range.

(7) In the public transport field, there is similar evidence from the experience of introducing flat or zonal fares where graduated fares previously applied. Where travelcards are sold, the effect is much greater, and cases such as the West Midlands show little if any effect on sales despite real price increases. Here, trips are about 7% higher than would have been expected for the same revenue target, had graduated fares been retained. However, it may well be possible to exceed the threshold, especially where fares simplification and increases are combined, as the Trondheim experience suggests.     

Commuters’ acceptance of and behavior reactions to license plate restriction policy: A case study of Tianjin,China

License plate restriction (LPR) policies are currently being implemented in major Chinese cities with the aim of mitigating traffic congestions. Meanwhile, much controversy regarding the effectiveness of the LPR policies is arising. To better understand the impact of the LPR policies, this paper studies commuters’ acceptance of and behavior reactions to the policies after their implementation. A theoretical model was proposed as the first step, followed by a questionnaire survey that was conducted in Tianjin, China, where an LPR policy has been in place since March 2014. Car owners frequently commuting within the restricted area were sampled as respondents, and a multi-variable regression method was employed to analyze the collected survey data. The results indicate that it is necessary to promote public’s acceptance of the LPR policy, because lower acceptance will lead to more negative reactions towards the policy, which may weaken its effectiveness. Main factors affecting the level of acceptance of the policy are also found, which may serve as a reference for transportation authorities seeking to increase commuters’ acceptance of the policy. These findings are beneficial to designing and implementing LPR policies.     

Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden

Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.