Adam Weiss

Development of an Employer-Based Transportation Demand Management Strategy Evaluation Tool with an Advanced Discrete Choice Model in Its Core

This paper presents a tool for the evaluation of employer-based transportation demand management (TDM) strategies. The conventional method of evaluating TDM strategies has typically been to conduct expensive before-and-after strategy implementation surveys. As an alternative approach, this research uses a joint revealed preference (RP) and stated preference (SP) survey (the RP–SP survey) administered before deployment of the TDM strategy, which is more cost-effective and efficient. The data collected from the RP–SP survey were used to estimate an advanced discrete choice model, which was packaged into a spreadsheet-based tool for TDM decision support. The tool adopted the concept of penetration rate, whereby only a subset of the target population could be targeted for any specific TDM strategy. The tool that was developed provides an alternative approach for the predeployment evaluation of any TDM strategy for efficient implementation. Moreover, the empirical model used in the tool reveals many behavioral details about commuters’ responses to employer-based TDM strategies.

Stated Preference Survey Pivoted on Revealed Preference Survey for Evaluating Employer-Based Travel Demand Management Strategies

This paper presents a study of commuters’ responses to various employer-based transportation demand management (TDM) strategies that was conducted in the Region of Peel, Ontario, Canada. The study involves design and implementation of a web-based survey of daily commuting mode choices and an efficient design-based stated preference (SP) experiment on the mode choice effects of potential employer-based TDM strategies. For the SP experiments, the survey also collected an elicited confidence rating from the respondents. The survey of 835 random commuters was conducted in fall 2014 and spring 2015. The paper uses empirical models of mode choices (revealed and stated) and an ordered probability model of the elicited confidence rating information to evaluate the data quality. The empirical models reveal that parking cost, monthly parking scheme, indoor parking facilities, emergency ride home, and bike share had higher impacts on commuting mode choices than did bike access facilities and a carshare strategy at the workplace. In relation to respondents’ confidence on SP responses, commuters with a higher number of cars in the household and with longer commuting distances seemed more certain and confident in their responses than did others. In addition, females were found to be more confident when answering SP choice questions.

Who is Picking Up the Child from Day Care? Understanding the Intrahousehold Dynamics in Drop-Off and Pickup Allocation for Households with Dependent Children

This paper presents a joint model for the allocation of drop-off and pickup responsibilities and the choice of the day care location for two-adult households with dependent children. This analysis aims to capture the trade-offs that occur at the household level in the selection of both day care location and drop-off and pickup responsibility allocation. The paper uses a stochastic frontier modeling approach to predict feasible locations for each possible allocation pair. The frontier model predicts the maximum distance an individual would be willing to travel within a time budget constraint. This travel time prediction is then applied to each individual for generating feasible location sets given two endogenous anchor points. The paper then presents a joint econometric-choice model of task allocation and day care location with heterogeneous sampling correction factors for each possible allocation. Captured within the model are the choice of who performs the drop-off and pickup activities and the eventual location that is selected for day care. The model structure provides key insights into both the choice to use day care and the allocation of drop-off and pickup responsibilities.