Optimizing Customized Transit Service considering Stochastic Bus Arrival Time

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.

Making the Links between Ride-hailing and Public Transit Ridership: Impacts in Medium and Large Colombian Cities

As transit ridership continues to fall in many cities across the globe, key policy debates continue around whether Uber and other ride-hailing services are contributing to this trend. This research explores the effects of the introduction of ride-hailing to Colombian cities on public transportation ridership using Ubers timeline as case study. We test the hypothesis that ride-hailing may either substitute or compete with public transit, particularly in cities with large transit service gaps in coverage or quality. Our analysis builds on historic transit ridership data from national authorities and uses a staggered difference-in-difference model that accounts for fixed effects, seasonality, socioeconomic controls, and the presence of integrated transport systems. Despite large reductions in transit ridership in most cities, our results suggest that Uber is not statistically associated with the observed drop in ridership. Moreover, consistent with evidence from previous research, public transit reforms implemented between 2007 and 2015 throughout Colombian cities appear to have contributed substantially to the declines in transit ridership observed across the country. Findings in this paper inform policy-targeted insights and contribute to current debates of the links between ride-hailing and public transit in cities in Latin America.

Reducing public transit compounds social vulnerabilities during COVID-19

The COVID-19 pandemic has severely impacted public transit services through a combination of plummeting ridership during the lockdown and subsequent budget cuts. This study investigates the equity impacts of reductions in accessibility due to public transit service cuts during COVID-19 and their association with urban sprawl. We evaluated accessibility to essential services such as grocery stores and both urgent and non-urgent health care across 22 cities across the United States in three phases during 2020: pre-lockdown, lockdown, and post-lockdown. We estimated the spatio-temporal coverage of transit service during the peak and off-peak periods in each phase. We found stark disparities in food and health care access for various socio-economic groups. Economically disadvantaged and suburban neighborhoods were more likely to lose food and health care access by public transit during COVID-19. In particular, transit service cuts worsened accessibility for population groups with multiple social vulnerabilities, such as low-income workers with zero vehicle ownership, poor households living in urban neighborhoods, and non-white populations residing in suburban neighborhoods. Moreover, our study suggests that sprawled cities experienced greater losses in access to food and health care during COVID-19 than compact cities, highlighting the influence of urban form on the functionality of transit services during crises.