Ramin Shabanpour

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.

Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration

ABSTRACT The role of in-home activities in the process of planning and scheduling of individuals’ daily activities has been traditionally ignored because of two reasons: (i) in-home activities are not directly involved with trips; and (ii) scarcity of data sources that provide required details on planning and scheduling of these activities. However, considering the interchangeable nature of out-of-home and in-home activities, and the significant effects that they have on each other, we argue that failing to incorporate in-home activities may result in overestimating frequency and duration of out-of-home activities, which may lead to inconsistent and unrealistic activity schedules. Recently, we have upgraded the ADAPTS activity-based model to account for planning and scheduling of both in-home and out-of-home activities. This paper aims to enhance the in-home activity planning module by modelling the type and duration of the in-home activities in a joint structure. To achieve this goal, using the American Time Use Survey data, we estimate joint discrete-continuous models, in which activity type and activity duration are estimated by a multinomial logit and a log-linear regression model, respectively. The joint structure of these two models is established using copula approach that captures the unobserved shared factors affecting the two activity attributes. The results indicate that the estimated joint models significantly outperform the independent models in terms of goodness-of-fit and prediction accuracy.

Activity start time and duration: incorporating regret theory into joint discrete–continuous models

ABSTRACT Activity start time and duration decisions are two key elements of activity-travel behaviour. As an effort towards a more realistic representation of individuals’ decision behaviour, this paper presents a new copula-based joint model of activity start time and duration. We incorporate the regret theory into the joint modelling framework, which assumes that individuals tend to avoid the situation where non-chosen alternatives outperform the chosen one in one or more attributes. The proposed joint model comprises a hybrid utility-regret model as the discrete component to estimate activity start time and a hazard duration model as the continuous component to estimate activity duration. The comparative analysis of estimation results reveals that the proposed structure is statistically superior to the utility-based joint model and independent models. We also found that applying the regret-based decision rule for variables of travel time and travel time variability significantly improves the model.

Assessing Energy Impacts of Connected and Automated Vehicles at the U.S. National Level—Preliminary Bounds and Proposed Methods

Connected and automated vehicles (CAVs) can have tremendous impacts on transportation energy use. Using published literature to establish bounds for factors impacting vehicle demand and vehicle efficiency, we find that CAVs can potentially lead to a threefold increase or decrease in light-duty vehicle energy consumption in the United States. Much of this uncertainty is due to possible changes in travel patterns (in vehicle miles traveled) or fuel efficiency (in gallons per mile), as well as future adoption levels and patterns of use. This chapter details the factors which go into these estimates, and presents a methodological approach for refining this wide range of estimated fuel consumption.

Developing a Spatial Transferability Platform to Analyze National-Level Impacts of Connected Automated Vehicles

A recent application of the spatial transferability approach is to assess the potential impacts of the emerging connected automated mobility technology on people’s travel behavior at the national level. While there are a few transportation simulation frameworks which can account for potential impacts of this technology in a simulated geographical context, there is yet to be any literature documenting disaggregated estimates of large-scale impacts of connected automated vehicles (CAVs) on travel behavior at the national level. Therefore, in order to provide a platform to assess national-level impacts of CAVs, this study develops a methodological framework based on transferability techniques, which uses data and models from a smaller geographical area—the POLARIS simulation results for the CAVs scenario in the Chicago metropolitan area—to generate disaggregate travel data at the national level. Comparison of the distributions of the transferred variables at the regional and the national contexts indicates that the platform is capable of transferring travel behavior indices to the national level with high level of accuracy.

Supplier Evaluation Model in Freight Activity Microsimulation Estimator

The supplier selection process is one of the main components of the Freight Activity Microsimulation Estimator (FAME), which is a disaggregated and comprehensive framework that simulates the freight movements for all industries and all commodities in the U.S. However, the supplier selection and supplier evaluation models in the FAME face computational issues. Using the result of a nationwide establishment survey, this study analyzes the supplier selection problem by evaluating the potential suppliers. The buyer’s behavior on selecting the distance range in which the trade forms is analyzed using both machine-learning and statistical approaches. A decision-tree model and an ordered probit model are estimated and compared to better comprehend the supplier evaluation process. The results indicate that several factors such as the type of the business, commodity type, number of orders, and the value of orders are significant factors. In addition, the decision-tree model is reliable in forecasting the consumer’s behavior.

Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity

In the context of traffic safety, whenever a motorized road user moves against the proper flow of vehicle movement on physically divided highways or access ramps, this is referred to as wrong-way driving (WWD). WWD is notorious for its severity rather than frequency. Based on data from the U.S. National Highway Traffic Safety Administration, an average of 355 deaths occur in the U.S. each year due to WWD. This total translates to 1.34 fatalities per fatal WWD crashes, whereas the same rate for other crash types is 1.10. Given these sobering statistics, WWD crashes, and specifically their severity, must be meticulously analyzed using the appropriate tools to develop sound and effective countermeasures. The objectives of this study were to use a random-parameters ordered probit model to determine the features that best describe WWD crashes and to evaluate the severity of injuries in WWD crashes. This approach takes into account unobserved effects that may be associated with roadway, environmental, vehicle, crash, and driver characteristics. To that end and given the rareness of WWD events, 15 years of crash data from the states of Alabama and Illinois were obtained and compiled. Based on this data, a series of contributing factors including responsible driver characteristics, temporal variables, vehicle characteristics, and crash variables are determined, and their impacts on the severity of injuries are explored. An elasticity analysis was also performed to accurately quantify the effect of significant variables on injury severity outcomes. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashes.