Modeling and Optimization of Pooled Rideshare Services in Future Shared Transportation Systems

Why is traffic congestion getting worse? A decomposition of the contributors to growing congestion in San Francisco-Determining the Role of TNCs

Transportation network companies (TNCs) such as Uber and Lyft have grown tremendously over the last decade, particularly in the San Francisco Bay Area. Nonetheless, relatively little publicly available data exist about the users of these services, their travel behaviors, volume of use, the times and locations of TNC trips, and how TNC services are affecting transportation system performance overall. This paper describes the methods and descriptive results of the first large-scale smartphone-based TNC user survey conducted in the California Bay Area in the fall 2018 and spring of 2019.

Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company’s historical sales data. A mixed-integer non-linear programming model is presented to maximize the company’s profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-023-05296-4.

Different from Transportation Network Companies and shared-car providers, social carpooling is personalized incentives-based and smartphone-enabled peer-to-peer ridesharing. Social carpooling emerges as a community-based strategy to reduce car ownership and mitigate congestion. It facilitates the transition from solo driving to effective carpooling by matching individuals’ travel demand in space and time. Personalized incentives, such as real-time information, travel feedback, and monetary incentives, are leveraged to spur the change. This project investigates the impact of social carpooling on users’ travel behavior and the system’s performance. The empirical data are supported by Metropia, which is a social carpooling platform enabling ride-match. Three research tasks are planned. First, at an individual level, we will identify what factors impact the travel mode change toward social carpooling. The findings will help explain how social carpooling varies by individuals’ income, gender, race, and other sociodemographic characteristics. Meanwhile, we will explore the effectiveness of different personalized incentive schemes to use social carpooling. Second, at an area level, we will estimate how many social carpooling trips can be generated if such programs are fully deployed by the public. This step will generate social carpooling trip demands for all traffic analysis zones, which is the basis for the following trip distribution, mode split, and dynamic traffic assignment. Third, at the system level, we will answer how much road congestion can be mitigated with a large-scale social carpooling deployment. Statistical models, machine learning algorithms, and simulation methods, including panel data analysis, synthetic minority oversampling technique, and dynamic traffic assignment, will be applied.

As transportation alternatives facilitated by TNCs (Transportation Network Companies) have gained popularity around the world, research has increasingly focused on understanding their impacts on urban mobility, with several studies examining whether they are competing with public transit trips, increasing vehicle kilometers, or contributing to congestion. Recent policy discussions have turned to whether these services could also have positive benefits, such as complementing mass transit services as a first- and last-mile solution. Most research to date has focused on industrialized countries, with little work focusing on Global South cities. Seeking to fill significant evidence gaps on the impacts of ride-hailing on travel behavior, this paper builds on a stated preference survey in three large metropolitan areas in Latin America (Bogota, Medellin, and Mexico City) to evaluate the potential of introducing an integrated scheme of ride-hailing and mass transit. The scenario of the integrated scheme places ride-hailing as a filler of the first- and last-mile gap left by mass transit. We use discrete choice models and simulations to assess the potential for modal shifts under different pricing scenarios. Results suggest limited feasibility (or modal shifts) of an integrated system of ride-hailing with mass transit under the scenarios considered. Even with significant discounts on the integrated fare, for two of the three cases, the additional ridership would remain comparatively low. Nevertheless, this opens the door for considering other app-based mobility options operating under a sharing perspective and that can reduce operative costs in an integrated scheme.

This paper studies drivers' travel behaviour in a road network degraded due to natural disasters. Actual route choice and travel choice behaviour of drivers using a questionnaire survey are analyzed. It is found that 85% of drivers did not cancel their trips and use diversion routes. Due to the insufficient supply of road networks, 70% of diverted drivers were obliged to use lower class of road sections with higher risk of travel. Models describing travel choice behaviour are developed using disaggregate demand modelling methods. Those models are applied to the road network in the Shikoku area. Although 3% ofdrivers are effected by road section closure, the estimated trip cancel rate and diversion rate are consistent with actual travel behaviour in the degraded network.

In recent years, innovative mobility and shifts in travel and consumption behavior are changing how people access and use the curb. Shared mobility—the shared use of a vehicle, bicycle, scooter, or other mode—coupled with outdoor dining, curbside pick-up, and robotic delivery are creating new needs related to the planning, management, and enforcement of curb access. This study examines curb planning and management from several angles, such as safety, social equity, and multimodal connections. This research employs a multi-method approach to identify the changing needs for curb space management and how to meet these needs through new planning and implementation policies and strategies. As part of this study, the authors conducted 23 interviews. Respondents were chosen to represent public, private, and non-profit sector perspectives. Additionally, the authors employed a survey of 1,033 curb users and 241 taxi, transportation network company (TNC), and public transportation drivers. The study finds that changes in mode choice and curbside use can result in a variety of impacts on access, social equity, congestion, device management, pick-up and drop-off, and goods delivery, to name a few. The curb also has the potential to be disrupted by emerging modes, such as robotic delivery vehicles (also known as personal delivery devices) and automated vehicles. As these emerging developments continue to impact the curb, it is becoming increasingly important for policymakers to have an appropriate framework for planning and managing curb space in urban areas.

Continuum modeling of park-and-ride services in a linear monocentric city with deterministic mode choice

Ridesourcing services from transportation network companies, like Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand.1 Ridesourcing vehicles' high use intensity is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. We optimize fleet composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy demand at minimum cost and compare findings across a wide range of present-day and future scenarios for three cities. In nearly all cases, the optimal fleet includes a mix of technologies, HEVs and BEVs make up the majority of distance traveled, and CVs are used primarily for periods of peak demand (if at all). When life cycle air pollution and greenhouse gas emission externalities are internalized via a Pigovian tax, fleet electrification increases and externalities decrease, suggesting a role for policy. Externality reductions vary from 10% in New York (where externality costs for both gasoline and electricity consumption are relatively high and a Pigovian tax induces a partial shift to BEVs), to 22% in Los Angeles (where high gasoline and low electric grid externalities lead a Pigovian tax to induce a near-complete shift to BEVs).

Prelims

Widespread congestion in metro systems often hinders passengers from boarding the first arriving train, making them compelled to adopt an alternative route, some of which involve travelling backwards. While this travel strategy has direct consequences for forecasting passenger flow distribution in congested networks, little is known about the travelling backwards phenomenon and why people adopt this travelling behaviour. The aim of this study is to understand passengers’ perception of time in various segments considering travelling backwards. To achieve this, we develop a route choice model using revealed preference data from smart card records. We find that passengers exhibit a greater aversion to waiting time and onboard time while travelling backwards. Specifically, passengers perceive each minute spent waiting on the turn-back stations’ platform as equivalent to 1.97 min on the origin platform. Similarly, each minute spent onboard the backwards train is perceived as equivalent to 1.24 min on the forwards train. Ignoring this difference in perception would result in the underestimation of the expected social benefits of demand management policies. Finally, we assess the potential benefits of travelling backwards under various passenger flow conditions, offering valuable policy insights regarding whether and how this behaviour should be regulated or promoted.

Urban transportation systems and travel behavior vary widely, even among countries with similar per-capita income, technology, and urbanization. This article compares modal—split—how people get from place to place—for 12 countries in Western Europe and North America. Differences in travel behavior arise largely from public policy differences, especially from differences in automobile taxation. In addition, variations in transit subsidies, land use controls, and housing programs significantly influence travel choices, although sometimes only indirectly. The success of public transportation depends more on supportive urban development and automobile taxation policies than on transit subsidies. The absence of such complementary policies in the U.S.—unlike the other countries studied—explains the ineffectiveness of the attempt to revive American public transportation exclusively through large subsidies.

An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems

A joint behavioral choice model for adoption of automated vehicle ride sourcing and carsharing technologies: Role of built environment & sustainable travel behaviors