Joint Discrete-continuous Model Research Articles

Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers including income. Furthermore, we estimate the potential for rebound effect and the effectiveness of a feebate policy. To accomplish this, we developed a joint discrete-continuous model of car choice and usage based on revealed preference survey data from approximately 8000 new car buyers from India and China who purchased cars in 2016–17. Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0.12 to 0.15. For both markets, the mean segment-level direct elasticities of fuel consumption relative to car price and fuel economy range from 0.57 to 0.65. The rebound effect on fuel savings due to cost-free fuel economy improvement is found to be 17.1% for India and 18.8% for China. A revenue-neutral feebate policy, with average rebates and fees of up to around 15% of the retail price, resulted in fuel savings of around 0.7% for both markets. While the feebate policy's rebound effect is low – 7.3% for India and 1.6% for China– it does not appear to be an effective fuel conservation policy.

Mobility as a service and private car use: Evidence from the Sydney MaaS trial

Australia’s first Mobility as a Service (MaaS) trial commenced in April 2019 in Sydney, running for two years. The objective of the trial is at least twofold – to assess interest in various MaaS subscription plans through bundling public transport, rideshare, car share and car rental with varying financial discounts and monthly subscription fees, in contrast to pay as you go (PAYG); and to assess the extent to which the use of the private car might change following a subscription to a monthly mobility bundle. This paper assesses the second objective by investigating the potential for changes in monthly car use in the presence of a MaaS program. There is no previous research that we are aware of that has tested the relationship between MaaS bundle uptake and private car use. The paper develops a joint discrete-continuous model system to explain the choice between monthly bundles and PAYG, and subsequently, the total monthly car kilometres. Controlling for monthly differences due to other influences such as seasonal travel activity, the findings suggest that the offered bundles do have an encouraging impact on private car use. Within the limits of what was tested under the Sydney MaaS trial, indicative evidence suggests that MaaS has the potential to change travel behaviour in a way aligned with sustainability objectives, although this evidence should not be taken as suggesting that MaaS is a commercially viable mobility strategy.

Mode choice and travel distance joint models in school trips

We test a copula-based joint discrete–continuous model to unravel mode choice and travel distance decisions in a joint framework for school trips. This framework explicitly accounts for common unobserved factors that may affect both the mode choice and travel distance. Joint estimation of the models makes a significant difference in the effect of travel distance on willingness to walk to school. The absolute value of the travel distance coefficient in the mode choice model increases by 22% when a joint formulation is adopted instead of the conventional single estimations. We find a significant decrease of 19% in the coefficient of travel safety perception in the joint mode choice model compared to the single model. This underscores the impact of model specification, in terms of the variable effect interpretation and policy assessments. The effect magnitude of several policy-sensitive variables is discussed and compared with previous studies. Particularly, we indicate that the probability of walking is reduced by 0.85% due to a 1% increase in travel distance; accordingly, it propels parents to select non-active modes, particularly school bus. This study also demonstrates how addressing parental concerns about travel safety could double the propensity to walk to school.

Copula-Based Joint Discrete–Continuous Model of Road Vehicle Type and Shipment Size

A major issue in freight modeling is the interrelationship between logistics choices that can be seen as a learning process that shippers or carriers undertake to optimize their logistics process, with the aim of minimizing their cost, maximizing their level of service, or both. This study looked at the interrelated decisions of vehicle type choice and shipment size in urban freight transportation by formulating a model that considered ( a) the nature of these two dependent variables via a joint discrete–continuous model, ( b) the correlation between the two decisions via a copula-based approach, ( c) the differences in decision making between carriers and shippers via the estimation of two models, and ( d) the relaxation of the assumption of pure utility maximization via a hybrid utility–regret specification. Results show differences between shippers’ and carriers’ preferences. These differences were logical because many urban shippers owned an efficient fleet of commercial vehicles, whereas carriers evaluated alternatives to maximize their aggregated utility and minimize their direct costs. Results also show the importance of considering jointly the two decisions as well as the relevance of using a hybrid utility–regret formulation for the cost. Practical findings emerge from the model: ( a) when faced with night delivery and intercity trips, carriers were more likely to use heavier vehicles and more voluminous shipments, whereas smaller shipments were preferred during the afternoon peak hour; ( b) urban shippers tended to deliver larger shipments during night by light trucks but preferred trailers for longer distances; and ( c) commodity types played a role in these joint decisions because some commodities were more likely to be transported by for-hire carriers and others were more likely to be transported by shippers.

Joint Discrete-Continuous Model of Travel Mode and Departure Time Choices

This paper presents a cluster-based joint modeling approach to investigating heterogeneous travelers’ behavior toward trip mode and departure time choices by considering those choices as a joint decision. First, a two-step clustering algorithm was applied to classify travelers into six distinct clusters to account for the heterogeneity in their decision-making behavior. Then, a joint discrete-continuous model was proposed for each cluster, in which the travel mode and departure time were estimated by a multinomial logit and a log-linear regression model, respectively. These two models were jointly estimated with a copula approach. For an investigation of the performance of the proposed approach, its results were compared with an aggregate joint model on all nonclustered observations to assess the potential benefits of population clustering. The goodness-of-fit measures and prediction accuracy results demonstrated that the proposed cluster-based joint model significantly outperformed the aggregate joint model. Further, the variations in the estimated parameters of different clusters indicated significant behavioral differences across clusters. Hence, the proposed cluster-based joint model, while offering higher accuracy, possesses a significant potential for transportation policy making because it has the capability to target different types of travelers on the basis of their decision-making behavior.

A joint discrete-continuous model considering budget constraint for the continuous part: application in joint mode and departure time choice modelling

This article presents an econometric modelling framework for a discrete-continuous choice structure. The proposed model ensures random utility maximisation (RUM) approach for both discrete and continuous decisions and explicit correlation between the two choices. The continuous choice is modelled as RUM under budget limitation. The model is applied for joint mode choice and departure time choice modelling. The empirical application considers continuous time scale for departure time under daily 24 h time budget constraint. The empirical model is estimated by using data collected in Toronto, Canada. The estimated parameters reveal many behavioural details of commuters’ mode and departure time choices. The RUM-based discrete-continuous model with the incorporation of time budget constraints for the continuous part is a generic econometric model. This article considers modelling ‘mode and departure time’ choice bundle for empirical application of the model. However, the model can be applied for any other similar choice bundle situation, for example, in the case of activity-based travel demand and land use modelling.

Daily Commute Time Prediction Based on Genetic Algorithm

This paper presents a joint discrete‐continuous model for activity‐travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA) is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity‐travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity‐travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete‐continuous model can be used to predict the agenda of a simple daily activity‐travel pattern containing only work, and it provides potential for transportation demand management policy analysis.

Joint Model of Vehicle Type Choice and Tour Length

Tour-based microsimulation model systems are increasingly being applied to the forecasting of travel demand. This paper examines the relationship between two dimensions of tours: the type of vehicle (in a household that owns multiple vehicles of different types) chosen to undertake the tour and the overall length (distance traveled) of the tour. These two dimensions are of much interest in the current planning context, in which concerns about energy sustainability and greenhouse gas emissions are motivating planners to seek ways to mitigate the adverse impacts of automotive travel. Moreover, virtually all tour-based models currently used do not explicitly account for choice of vehicle type in the modeling of tour attributes, despite the critical importance of the choice of vehicle type for energy and emissions analysis. This paper presents a joint discrete–continuous model of choice of vehicle type and length of tours. Estimation results suggested that significant common unobserved factors affected vehicle type choice and length of tours. These factors justified the use of modeling approaches with joint simultaneous equations to model tour attributes. The model specification in which vehicle type choice affected tour length performed better than the specification in which tour length affected vehicle type choice. This outcome suggested that choice of vehicle type (and allocation to household members) was a longer-term choice that influenced shorter-term tour-length choices.

An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

The trip timing and mode choice are two critical decisions of individual commuters mostly define peak period traffic congestion in urban areas. Due to the increasing evidence in many North American cities that the duration of the congested peak travelling periods is expanding (peak spreading), it becomes necessary and natural to investigate these two commuting decisions jointly. In addition to being considered jointly with mode choice decisions, trip timing must also be modelled as a continuous variable in order to precisely capture peak spreading trends in a policy sensitive transportation demand model. However, in the literature to date, these two fundamental decisions have largely been treated separately or in some cases as integrated discrete decisions for joint investigation. In this paper, a discrete-continuous econometric model is used to investigate the joint decisions of trip timing and mode choice for commuting trips in the Greater Toronto Area (GTA). The joint model, with a multinomial logit model for mode choice and a continuous time hazard model for trip timing, allows for unrestricted correlation between the unobserved factors influencing these two decisions. Models are estimated by occupation groups using 2001 travel survey data for the GTA. Across all occupation groups, strong correlations between unobserved factors influencing mode choice and trip timing are found. Furthermore, the estimated model proves that it sufficiently captures the peak spreading phenomenon and is capable of being applied within the activity-based travel demand model framework.

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.