Analyzing the Impacts of a Successful Diffusion of Shared E-Scooters and Other Micromobility Devices and Efficient Management Strategies for Successful Operations in Illinois

Active transportation can play an important role in promoting more physically active and positive public health outcomes. While walking and biking provide significant physical health benefits, their modal share remains low. As a new form of micromobility service, shared e-scooters can enhance the suite of options available in cities to promote active transportation and fill in the gaps when walking or biking are not preferred. Although e-scooters show potential as a mode of transportation, it is unclear whether people will adopt the technology for everyday use. Furthermore, shared micromobility (e.g., electric scooters) is gaining attention as a complementary mode to public transit and is expected to offer a solution to access/egress for public transit. However, few studies have analyzed integrated usage of shared e-scooters and public transit systems while using panel data to measure spatial and temporal characteristics. This study aims to examine the adoption and frequency of shared e-scooter usage and provide policy implementation. To do so, the researchers launched a survey in the Chicago region in late 2020 and collected a rich data set that includes residents’ sociodemographic details and frequency of shared e-scooter use. To characterize the frequency, the researchers used an ordered probit structure. The findings show that respondents who are male, low income, Millennials and Generation Z, or do not have a vehicle are associated with a higher frequency of shared e-scooter use. Furthermore, this study utilizes shared e-scooter trips for a 35-day measurement period from 10 shared e-scooter operators in Chicago, where the researchers used a random-parameter negative binomial modeling approach to analyze panel effects. The findings highlight the critical role of spatial and temporal characteristics in the integration of shared e-scooters with transit.IDOT-R27-215Ope

Citizen-Centric Smart Cities: Planning for Travel Behavior in a Technology Empowered Future

A multitude of confounding factors contribute to the success of smart cities (Caragliu et al., 2011; Giffinger et al., 2010; Lee et al., 2013). Many of these factors are centered around the citizens and their interactions with its various technological elements. The concept of “citizen-centric smart cities” is proposed in the literature to better comply with this (Allam and Newman, 2018; Lee and Lee, 2014; Yonezawa et al., 2015; Zakzak, 2019). Acknowledging the role of the technology-centric approaches in the past in leading our cities towards ecosystems that favor drivers over pedestrians (Allam and Newman, 2018; Ewing et al., 2018; Shelton et al., 2015; Southworth, 2005), the research presented in this dissertation aims at providing insights into the success of future citizen-centric smart cities. Extracting the influence of socio-psychological contributors such as lifestyles, habits, and higher-level orientations is a dominant note of the present research throughout its four main study chapters, as discussed briefly in the following. First (chapter 3), the dynamics of travelers’ modality styles were analyzed while accounting for the existence of mobility-on-demand (MoD) services in the market. The dynamics of modality styles is an important aspect that has remained understudied to this point. This study contributes to the existing literature by breaking down the modality style dynamics into: (1) the baseline preferences, and (2) the longer-term (i.e., a 30-day time window) inter-modal substitution behavior. Second (chapter 4), the influence of lifestyles on productive travels using public transportation was analyzed, to obtain insights into how a future autonomous transit system can account for the expectations and needs of its users with respect to the efficiency of the activities performed while riding –i.e., cited in the literature as “travel-based multitasking” (Singleton, 2018). How well a transportation mode could facilitate this desire of the travelers is cited in the literature as its “multitaskability” and plays an indisputable role in attracting potential users (Mokhtarian, 2019; Mokhtarian and Salomon, 1997; Pawlak et al., 2016). As such, the results of this research could be used to inform future developments of the transit system towards providing more attractive services. Third (chapters ‎5 and ‎6), the impacts of the COVID-19 pandemic on the dynamics of activity-travel behavior in the future is analyzed based on a comprehensive travel survey conducted in the Chicago metropolitan area. The pandemic forced many to re-examine their habits, and thereby, caused considerable changes in the current travel patterns. Therefore, the last two research chapters of this dissertation is dedicated to understanding the “stickiness” of the heightened levels of shifting towards tele-activities (i.e., online shopping and working from home, etc.) as well as private modes of travel in the post-pandemic future

Week-Long Mode Choice Behavior: Dynamic Random Effects Logit Model

Modeling travelers’ mode choice behavior is an important component of travel demand studies. In an effort to account for day-to-day dynamics of travelers’ mode choice behavior, the current study develops a dynamic random effects logit model to endogenously incorporate the mode chosen for a day into the utility function of the mode chosen for the following day. A static multinomial logit model is also estimated to examine the performance of the dynamic model. Per the results, the dynamic random effects model outperforms the static model in relation to predictive power. According to the accuracy indices, the dynamic random effects model offers the predictive power of 60.0% for members of car-deficient households, whereas the static model is limited to 43.1%. Also, comparison of F1-scores indicates that the predictive power of the dynamic random effects model with respect to active travels is 47.1% whereas that of the static model is as low as 15.0%. The results indicate a significant day-to-day dynamic behavior of transit users and active travelers. This pattern is found to be true in general, but not for members of car-deficient households, who are found more likely to choose the same mode for two successive days

Dynamics of travelers’ modality style in the presence of mobility-on-demand services

Modality style –defined as a set of frequent travel modes characterizing the travelers’ habits, routines, and predispositions– is a key player in forming dynamics of travelers’ mode choice behavior. This study aims to uncover the dynamics of modal preferences while the Mobility-on-Demand (MoD) services operate in the market. Using the 2017 National Household Travel Survey data, a Multiple Discrete Continuous Extreme Value model is developed to analyze the dynamics of individuals’ modality style. This model enables us to take into consideration marginal rates of substitutions between different transportation modes. Variables of interest in this analysis include the frequency of use of mobility-on-demand (MoD) services as well as the frequency of walking, biking, transit, and auto trips over the course of a month. The results of this study offer city planners and policymakers an opportunity to better understand the factors underlying modality styles, and which priorities to focus on when designing a sustainable development plan for resident-centric Smart Cities. According to the results, age, work status, education, auto availability, and the built environments are among the significant contributors to the modality styles. The results also indicate that the extent of the substitution relationship between transit and MoD services is highly context dependent

Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system

As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling. Travel-based multitasking has an indisputable influence on offering a pleasant travel experience to transit users during their rides, given that they can use their travel time to perform desirable activities and gain benefits in various form. For instance, the in-activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of a travel-based multitasking could be under the influence of: (1) the transit user’s lifestyle, and (2) socio-demographics, and (3) the characteristics of the transit trip. Towards this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using latent class modeling approach. Per the results, two classes of transit users could be identified: (1) worthwhileness seekers, productively travelers and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. The findings provide insights to policymakers for improving public transit systems in the current form, as well as designing an autonomous mobility system as the future form of public transit

Analysis of transit users’ waiting tolerance in response to unplanned service disruptions

Public transit not only provides an affordable, efficient, and green service but also plays a critical role in the development of resilient transportation systems in urban areas. Transit disruption as a common incident in transit service operation can severely affect the resiliency of the transportation system as well as users’ satisfaction. While it is of great interest to transportation authorities to understand passengers’ decision behavior during unplanned transit disruptions in order to implement efficacious recovery strategies, still little is known about users’ behavior in case of such incidents. The scarcity of available data is a major underlying factor for this gap. Utilizing a recently collected data of transit users in the Chicago metropolitan area, the current study investigates transit users’ waiting tolerance during unplanned service disruptions and disclose the factors that affect their behavior. A set of interval-censored accelerated failure time models using different survival distributions are developed, compared, and the factors influencing the survival functions of the waiting tolerance are identified. The results of the analysis reveal that, for instance, having experience of using ridesharing services decreases users’ waiting tolerance during a disruption. Further, built-environment attributes (such as the density of pedestrian-oriented links and transit service frequency), availability of alternative modes, transit service type, user’s attitudes, and trip characteristics turn to be significant in users’ decision behavior

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Public transit disruption is becoming more common across different transit services, and can have a destructive influence on the resiliency of the transportation system. Even though transit agencies have various strategies to mitigate the probability of failure in the transit system by conducting preventative actions, some disruptions cannot be avoided because of their either unpredictable or uncontrollable nature. Utilizing recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to an unplanned service disruption and disclose the factors that affect their behavior. In this study, a random parameter multinomial logit model is employed to consider heterogeneity across observations as well as panel effects. The results of the analysis reveal that a wide range of factors including socio-demographic attributes, personal attitudes, trip-related information, and built environment are significant in passengers’ behavior in case of unplanned transit disruptions. Moreover, the effect of service recovery time on passengers is not the same among all types of disrupted services; rail users are more sensitive to the recovery time as compared with bus users. The findings of this study provide insights for transportation authorities to improve the transit service quality in relation to user satisfaction and transportation resilience. These insights help transit agencies to implement effective recovery strategies