Transport workers were among the most affected by the COVID-19 crisis. In several countries, public transport and delivery drivers were considered essential workers during the pandemic, while the demand changed dramatically. In this context, little is known about the actual effects of the pandemic on the lives of drivers, and whether those effects depend on the type and formality of the corresponding job. In this paper, we analyse the impact of the pandemic on the daily jobs of public transport, ride-hailing, and delivery app drivers: we study changes on working time and income, pandemic-related concerns, and deterioration of job satisfaction, through a survey applied to drivers during the first peak of the pandemic in Santiago, Chile. Probit regressions on job satisfaction identify the main COVID-related experiences that explain variations in subjective perceptions. We then discuss the implications for post-pandemic job relationships, drivers’ working conditions and urban mobility. We show that the unstable characteristics of app-based jobs sharpened during the pandemic: Public transport drivers have kept their jobs, with a similar income as in the pre-pandemic situation and keep their social security, whereas ride-hailing and delivery app drivers do not have social security. Several ride-hailing drivers lost their jobs without any compensation, while delivery drivers earn less money per hour, are more exhausted, and express the greatest concerns and largest decrease in their job satisfaction. The COVID-19 crisis has emphasized that the sustainability of post-pandemic passenger and delivery on-demand services needs to rely on formal job regulation and worker protection.

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We analyse the sources of economies and diseconomies of scale in On-Demand Ridepooling (ODRP), disentangling three effects: when demand grows, average costs are reduced due to i) a larger fleet that diminishes waiting and walking times (Mohring Effect), and ii) matching users with more similar routes (Better-matching Effect). A counter-balance force (Extra-detour Effect), occurs when iii) the number of passengers per vehicle increases and users face longer detours. At low demand levels, there is little sharing and the Mohring effect prevails; as demand grows, more passengers per vehicle push for the Extra-detour Effect to dominate; eventually, vehicles run at capacity, and the Better-matching Effect prevails. The last two effects are specific to ODRP as the routes are not fixed but adapted online. Our simulations show that considering both users' and operators’ costs, scale economies prevail, and that ODRP with human-driven vehicles and walks allowed has total costs similar to door-to-door systems with driverless vehicles.

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The most relevant issues related to headway variability in public transport planning, operations and quality of service are reviewed in this paper. We discuss the causes and consequences of headway variability, the alternative metrics that have been proposed to measure it, the preventive and reactive strategies to control headway variability in both research and practice, including the role of drivers and of present and future technology, and how service provision contracts might deal with headway variability through metrics and financial incentives. The most influential elements that explain headway variability along a route are the irregularity at which vehicles are dispatched, the scheduled frequency, the distance travelled or route length, the passenger demand and associated dwell times, and the number of stops. We conclude that there is a large gap between the state-of-the-art and the state-of-practice in terms of identification of headway variability issues, as well as in the development of mitigation and control measures. It is therefore paramount that future research will contribute to closing this gap by addressing organisational, contractual and technological barriers in the implementation of measures aimed at mitigating headway variability in public transport services.

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Ride-hailing (ridesourcing) companies such as Uber, Lyft, and Didi Chuxing have been a disruptive force in the urban mobility landscape around the world during the past decade. In this paper, we analyse the working conditions, earnings, and job satisfaction of ride-hailing drivers. We begin by discussing the regulatory, labour, financial, and urban mobility effects of ride-hailing companies. Then, we present the results of a self-administered survey to ride-hailing drivers in Chile, which is complemented with the use of online tools for the estimation of driving earnings. Our findings show that the flexibility to choose work times is the most appreciated attribute of this job, even though most drivers follow a somewhat fixed routine each week. By contrast, the level of transparency with which ride-hailing apps determine driver pay is the attribute with the lowest satisfaction score. A large number of respondents drive for long daily and weekly periods, which is a health and safety hazard. Current drivers are not concerned about the future deployment of driverless vehicles for on-demand mobility services. Ordered probit models for job satisfaction show that ride-hailing was better evaluated by drivers who use it as a complement to another part-time job, by those who earn more money per week, and by those who have not experienced undesirable situations while working, such as harassment or traffic crashes.

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The COVID-19 pandemic poses a great challenge for contemporary public transportation worldwide, resulting from an unprecedented decline in demand and revenue. In this paper, we synthesize the state-of-the-art, up to early June 2020, on key developments regarding public transportation and the COVID-19 pandemic, including the different responses adopted by governments and public transportation agencies around the world, and the research needs pertaining to critical issues that minimize contagion risk in public transportation in the so-called post-lockdown phase. While attempts at adherence to physical distancing (which challenges the very concept of mass public transportation) are looming in several countries, the latest research shows that for closed environments such as public transportation vehicles, the proper use of face masks has significantly reduced the probability of contagion. The economic and social effects of the COVID-19 outbreak in public transportation extend beyond service performance and health risks to financial viability, social equity, and sustainable mobility. There is a risk that if the public transportation sector is perceived as poorly transitioning to post-pandemic conditions, that viewing public transportation as unhealthy will gain ground and might be sustained. To this end, this paper identifies the research needs and outlines a research agenda for the public health implications of alternative strategies and scenarios, specifically measures to reduce crowding in public transportation. The paper provides an overview and an outlook for transit policy makers, planners, and researchers to map the state-of-affairs and research needs related to the impacts of the pandemic crisis on public transportation. Some research needs require urgent attention given what is ultimately at stake in several countries: restoring the ability of public transportation systems to fulfill their societal role.

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In this article, a hybrid predictive control (HPC) strategy is formulated for the real-time optimisation of a public transport system operation run using buses. For this problem, the hybrid predictive controller corresponds to the bus dispatcher, who dynamically provides the optimal control actions to the bus system to minimise users' total travel time (on-vehicle ride time plus waiting time at stops). The HPC framework includes a dynamic objective function and a predictive model of the bus system, written in discrete time, where events are triggered when a bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions, holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and then predicted based on both offline and online information of passenger behaviour. The resulting optimisation problem of the HPC strategy at every event is Np-hard and needs an efficient algorithm to solve it in terms of computation time and accuracy. We chose an ad hoc implementation of a Genetic Algorithm that permits the proper management of the trade-off between these two aspects. For real-time implementation, the design of this HPC strategy considers newly available transport technology such as the availability of automatic passenger counters (APCs) and automatic vehicle location (AVL) devices. Illustrative simulations at 2, 5 and 10 steps ahead are conducted, and promising results showing the advantages of the real-time control schemes are reported and discussed.@en