This session examnes various aspects of travel behavior with respect to public transportaiton, including first and last mile issues, market segmentation analysis
This session offers a selection of posters based on papers covering a variety of traveler behavior, traveler response, and traveler characteristics topics surrounding public transportation demand.
Who will use the train? A market segmentation study of potential light rail users in Montreal QC, Canada
Nicolette Dent, McGill UniversityShow Abstract
Leila Hawa, McGill University
DeWeese James, McGill University
Rania Wasfi, Consultant
Yan Kestens, Universite de Montreal
Ahmed El-Geneidy, McGill University
An understanding of the public transport market can help agencies reach their goals of achieving high levels of utilization of major infrastructure investments. The goal of this research is to better understand the potential users market of one of Montreal’s current major public endeavors, the Réseau express métropolitain (REM). We conducted a survey of the Montreal population in fall 2019 while the $6.3 billion light rail system is being constructed and before it is operational. Drawing on vetted transport market segmentation frameworks, this study employs an exploratory factor analysis to reveal factors that affect respondents’ propensity to use the REM. A k-means cluster test is applied to the factors to articulate market segments. The analysis returned four clusters that form a clear spectrum of least likely to most likely REM users: car friendly non-users, urban core potential users, transit friendly users, and leisure and airport users. Positive opinion, proximity, and desire to use the REM for leisure or non-work trips are three key characteristics of likely users. There is a visible relationship between clusters who are likely to use the REM and clusters who agree that the REM will benefit their neighborhood. Improving perceived neighborhood benefit, accommodating leisure use, emphasizing destinations, and highlighting public transport connections are four core ways that planners and engineers can increase the number of people who are likely to use such major infrastructure.
More than Time and Cost: Factors Affecting Mode Choice for Transit Commutes from Northern New Jersey to New York City
Jon Carnegie (email@example.com), Rutgers UniversityShow Abstract
Devajyoti Deka, Rutgers University
This paper presents findings from an ongoing study that examines the mode choice preferences of New Jersey commuters traveling to New York City. This paper reports the analysis of data from a stated preference survey conducted by the authors. Survey data were collected from a total of 2,134 commuters and a mixed multinomial logit model (MMLM) was used for data analysis. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey into New York City. Essential model results are presented for all four modes, with detailed analysis discussed for the ferry, PATH, and bus modes. Model results showed that commuters’ choices are affected not only by travel time and cost for the segment of the trip that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes require to make the trip, and travel time and cost of trips from home to stations/terminals used by commuters before crossing the river. An important finding is that commuters are highly attached to the mode they currently use, indicating that this attachment can be a barrier to switching to another mode even if the other mode is competitive from a cost and travel time perspective.
Does the Joint Implementation of Hard and Soft Transportation Policies Lead to Travel Behavior Change? A Hybrid Modeling Approach
Francesco Piras (firstname.lastname@example.org), University of CagliariShow Abstract
Eleonora Sottile, Universita degli Studi Di Cagliari
Giovanni Tuveri, Universita degli Studi Di Cagliari
Italo Meloni, Universita degli Studi Di Cagliari
Over the last few decades, there has been a growing interest in a variety of behavioral policies ( soft measures), aimed at persuading people to reduce their car use. The current pandemic that is sweeping our planet has undoubtedly highlighted the importance of individual contribution. One problem with soft interventions is the difficulty in properly measuring their effectiveness, not only in terms of descriptive analysis but also in employing predictive models that account for both objective and socio-psychological variables. Additionally, though a combination of hard and soft measures are recognized as achieving the best results in reducing car use, few studies differentiate between the effects of the two types. The aim of this work is to quantify the effect of a combination of hard (introduction of a new light railway line) and soft measures (Voluntary Behavior Change program - VTBC) in the metropolitan area of Cagliari (Italy). We used data collected before and after the implementation of a VTBC program, where a control group was identified to disentangle the effect of the hard from the soft measure. We estimated a Hybrid Choice Model to assess the effect of both objective characteristics and some socio-psychological variables on the choice to use a new light railway service or not. One of the results obtained is the change in travel behavior of 34% after implementation of the hard measure, and of 46% with the VTBC program.
Heterogeneity in Activity-travel Patterns of Public Transit Users
Rezwana Rafiq (email@example.com), University of California, IrvineShow Abstract
Michael G McNally, University of California, Irvine
Public transit is considered a sustainable mode of transport that can reduce automobile dependency and can provide environmental, economic, and societal benefits. However, with the typical temporal and spatial constraints such as fixed routes and schedules, transfer requirements, waiting times, and access/egress issues, public transit offers lower accessibility and mobility services than private vehicles and thus it is considered a less attractive mode to many people. To improve the performance of transit and in turn to increase its usage, a better understanding of daily activity-travel patterns of transit users is required. This study analyzes transit-based activity-travel patterns by classifying users via Latent Class Analysis (LCA). Using data from the 2017 National Household Travel Survey, the LCA model suggests that the transit user population can be divided into five distinct classes where each class has a representative activity-travel pattern. Class 1 constitutes Caucasians employed males who make transit-dominant simple work tours. Class 2 is composed of Caucasian females who make complex work tours. Caucasian employed millennials comprise Class 3 and make multimodal complex tours. Transit Class 4 are non-Caucasian younger or older adult groups who make transit-dominant simple non-work tours. Last, Class 5 members make complex non-work tours with recurrent transit use and comprise single older women. This study will help transit agencies to understand the activity-travel patterns of various transit user groups and to consider market strategies that can address their travel needs.
Making Last-Mile Connectors Work: The Key to Adapting Regional Rail to Suburban Employment Centers
TR Hickey, JacobsShow Abstract
SEPTA Route 201 was the first of a series of a private-public partnerships intended to cost-effectively extend public transportation to serve suburban employment centers. Local “last-mile” shuttle services can extend the reach of a traditional radial rail network and attract “reverse-peak” (city-to-suburb) and intrasuburban commuters. It requires comprehensive (“holistic”) approach to planning and operations that encompasses every aspect of the journey across agency, institutional and modal boundaries.
Detecting Stochastic and Deterministic Structures of Short-Term Metro Passenger Flow with CEEMDAN and RQA
Hao Huang, Southwest Jiaotong UniversityShow Abstract
Jiannan Mao, Southwest Jiaotong University
Yuting Chen, Southwest Jiaotong University
Weike Lu, University of Alabama
Lan Liu, Southwest Jiaotong University
Considering the nonlinear, non-stationary, and chaotic characteristics of the short-term passenger flow, this paper proposes a hybrid model combing the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and recurrence quantification analysis (RQA), to detect the stochastic and deterministic structures of short-term passenger flow. In the model, CEEMDAN is used to decompose the original data into several intrinsic mode functions and a residue, while RQA is performed to reconstruct the decomposed modes into a stochastic part, a deterministic part and a trend part via determinism evaluation. Further, RQA and a windowed RQA are conducted to analyze the static and time-evolving dynamic behaviors of the reconstructed components. The data from Chengdu metro system, China, is employed to verify the proposed model. The results suggest that the proposed model provides an effective way to detect the intrinsic dynamics and uncover underlying structures of the short-term metro passenger flow.
Will COVID-19 be the End for the Public Transit? Investigating the Impacts of Public Health Crisis on Transit Mode Choice
Sk Md Mashrur, University of TorontoShow Abstract
Kaili Wang, University of Toronto
Khandker Nurul Habib (firstname.lastname@example.org), University of Toronto
COVID-19 had an unprecedented impact on transit demand and usage. Stiff and vigilant hygiene safety requirements changed peoples’ mode choice preferences during the COVID-19 time; specifically, transit modal share is radically impacted. Quantitative measurements on transit demand impact are urgently needed to facilitate evidence-based policy responses to COVID-19. Thus, we collected data (of around 1000 random individuals) through a web-based survey in the Greater Toronto Area (GTA) on traveler’s modal choices before and after COIVD-19. The paper presents a firsthand analysis of the dataset to understanding transit users' behavioral adaptation resulting from the COVID-19 spread. We found that during the pandemic transit use, the frequency dropped by 21% to 71% of various socio-economic groups in the GTA. The transit model share also dipped for all trip purposes. Around 70% of transit users who tried to avoid transit for COVID-19 switched to private vehicles if they had access to cars. More than 60% of those without cars switched to active transportation for all travel purposes. Also, ride-hailing services are the second popular substitutions for transit at this time. The survey collected respondents' opinions on future transit usage. More than 80% agreed to return to public transit in the future. More than 80% of all respondents have positive attitudes towards all transit safety policies listed in the survey. Econometric models are estimated to capture relationships between transit model choice and various factors. We found that the daily number of new COVID-19 cases impact the choice of transit negatively. However, vaccine availability and mandatory face-covering onboard have positive impacts on transit mode choice.
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