Charlotte Frei

Stretching resources: sensitivity of optimal bus frequency allocation to stop-level demand elasticities

Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of elasticities corresponding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal elasticities based on time of day, (2) spatial elasticities via grouping stops into demand, supply and land use classes and (3) spatio-temporal elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons.

Impact of crime statistics on travel mode choice: Case Study of the city of Chicago, Illinois

Whether crime or the perception of it has any direct and significant influence on travelers’ mode choice is a topic for which the evidence remains inconclusive. Studies have revealed various, and in some cases counterintuitive, roles that safety concerns can play in individuals’ travel behavior. In addition, characteristics of the physical environment such as land use and walkability are also influential factors in travelers’ decisions. This study explored these questions through the study of individual travel behavior by using discrete choice models applied to the reported home-based work trips in the Chicago household travel survey. Mode choice was modeled as functions of variables such as sociodemographics, neighborhood crime density (as a safety measure), and walk score (as a measure of walkability). Different crime types were examined, and a crime index was introduced. Results suggest that both walk score and the crime index at the destination can be considered meaningful predictors of individuals’ mode usage. The crime index at origin, however, does not show a significant and meaningful effect.

Riding More Frequently: Estimating Disaggregate Ridership Elasticity for a Large Urban Bus Transit Network

Stop-level transit elasticities with respect to service frequency are estimated and discussed. Public transportation ridership is typically studied at an aggregate level, with variables influencing ridership averaged over time and space for a metropolitan area. Understanding transit rider-ship at a finer temporal and spatial level is generally limited to mode choice models. Most aggregate analyses are unable to capture important effects at the parcel or block level. Such analyses also cannot account for variation in demand over time of day, an issue that has been addressed to some extent by time series modeling. Data for the Chicago, Illinois, transit system were used, and results suggest that aggregate analyses overestimate the effect of service frequency on demand. In the context of other disaggregate analyses, these findings suggest that walking quality results in distinct increases in ridership, even after accounting for land use and for population and other demographics. A headway elasticity of rider-ship was estimated to be −0.263 to −0.277, very similar to recent disaggregate analysis of New York City transit data. The case is made for a better spatio temporal understanding of transit ridership for the effective allocation of resources.

Walking behavior the role of childhood travel experience

With the growing interest in nonmotorized modes of transportation in general and walking in particular, there is an increasing need to understand the underlying motives and determinants of walking behavior. Active modes such as walking and biking can enhance public health and safety. As many cities seek to shift drivers out of their vehicles toward more active transportation modes by building the appropriate walking and biking infrastructure, research should capture the individual and community attributes that influence these travel mode choices. While this question has been addressed in the literature, through analysis of the role of neighborhood characteristics, walkability, self-selection, attitudes, and inertia, among others, the aim of this paper was to explore the role of childhood experience on these factors. To do so, this study gathered retrospective information through an attitudinal survey divided into sections pertaining to sociodemographics, neighborhood characteristics, residential location choice factors, and precollege travel experience. The survey results proved the existence of a relationship between travel behavior during childhood and the determinants of walking behavior during adulthood. The results also suggested that the determinants of walking differed on the basis of the individuals region of residence. These results might suggest that transportation policy at the childhood level could result in benefits not only during childhood but also throughout the life cycle of the individual.

Integrating Behavioral Models in Network Operations: Evaluating Traveler Information and Demand Management for Weather-Related Events

The main goal of the study was to integrate demand models into weather-responsive network traffic estimation and prediction system methodologies. The study examined the behavioral responses of travelers along several dimensions in response to weather-related transportation management strategies in conjunction with active travel demand management strategies before and during severe weather events. Specific management interventions included pretrip, information-based mode, and departure time choice adjustments, as well as policy-based rescheduling of school hours. The paper presents a case study of the Chicago, Illinois, area network under snow conditions to assess the effect of a combination of demand management strategies to maintain the same level of network performance as under clear weather conditions. A combination of earlier dissemination of information and school-opening policy resulted in a similar level of network performance maintained under a median snow day as compared with a clear weather day. The paper presents integrated supply and demand models for simulation and an assessment of demand management strategies in conjunction with weather-related congestion.