On the inefficiency of ride-sourcing services towards urban congestion

The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companies (TNCs) due to their internet-based nature. Although ride-sourcing is the most notorious service TNCs provide, little is known about to what degree its operations can interfere in traffic conditions, while replacing other transportation modes, or when a large number of idle vehicles is cruising for passengers. We experimentally analyze the efficiency of TNCs using taxi trip data from a Chinese megacity and an agent-based simulation with a trip-based MFD model for determining the speed. We investigate the effect of expanding fleet sizes for TNCs, passengers’ inclination towards sharing rides, and strategies to alleviate urban congestion. We observe that, although a larger fleet size reduces waiting time, it also intensifies congestion, which, in turn, prolongs the total travel time. Such congestion effect is so significant that it is nearly insensitive to passengers’ willingness to share and flexible supply. Finally, parking management strategies can prevent idle vehicles from cruising without assigned passengers, mitigating the negative impacts of ride-sourcing over congestion, and improving the service quality.

Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing

Nikolaos Geroliminis, Nan Zheng

Cruising-for-parking constraints mobility in urban networks. Car-users may have to cruise for on-street parking before reaching their destinations. The accessibility and the cost of parking significantly influence people's travel behavior (such as mode choice, or parking facility choice between on-street and garage). The cruising flow causes delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore important to understand the impact of parking limitation on mobility, and to identify efficient parking policies for travel cost reduction. Most existing studies on parking fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general, lack the treatment of dynamics of the cruising-for-parking phenomenon, or require detailed input data that are typically costly and difficult to collect. In this paper, we propose an aggregated and dynamic approach for modeling multimodal traffic with the treatment on parking, and utilize the approach to design dynamic parking pricing strategies. The proposed approach is based on the Macroscopic Fundamental Diagram (MFD), which can capture congestion dynamics at network-level for single-mode and bi-modal (car and bus) systems. A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion. Pricing strategies are developed with the objective of reducing congestion, as well as lowering the total travel cost of all users. A case study is carried out for a bi-modal city network with a congested downtown region. An elegant feedback dynamic parking pricing strategy can effectively reduce travel delay of cruising and the generic congestion. Remarkably, such strategy, which is applicable in real-time management with limited available data, is fairly as efficient as a dynamic pricing scheme obtained from system optimum conditions and a global optimization with full information about the future states of the system. Stackelberg equilibrium is also investigated in a competitive behavior between different parking facility operators. Policy indications on on-street storage capacity management and pricing are provided.

Elsevier2016

Congestion and departure time choice equilibrium in urban road networks

Raphael Ali Francis Lamotte

When drivers are regularly faced with congestion, they try to optimize their departure time. If the demand and the road network evolve slowly enough, the entire system may approach an equilibrium, i.e. a state such that no one can be better off by unilaterally changing departure time. The transportation community has devoted a significant effort to identify such equilibria. The ultimate goal is to be able to predict the consequences of large infrastructure projects and to design smart policies to alleviate congestion. Yet, several issues still limit the applicability of the existing literature. This thesis identifies three complementary challenges and attempts to address them. We first investigate whether, from a theoretical viewpoint, real world unidirectional flows are likely to be in a near-equilibrium state. Our analytical findings reveal the influence of schedule preferences on stability, and explain why morning commutes are more likely to be unstable than evening ones. Fortunately, user heterogeneity or socially optimal pricing can soften the effects of instability. Residual oscillations result in a congestion cost decomposition that differs from the one observed at equilibrium, but the overall average congestion cost at equilibrium is remarkably accurate. We then characterize departure time choice equilibria in isotropic regions, representing multi-directional road networks. In this context, users can slow down others who started their trips earlier and trip length is an important determinant of departure time choice. We show that with a widely used type of schedule preferences, users with long trips tend to avoid the peak period. Although the First-In, First-Out (FIFO) property does not hold in general with heterogeneous trip lengths, such a pattern emerges among families of early and late users having the same preferences. Simulations suggest that the social cost and its decomposition greatly differ from those observed in unidirectional settings. We finally propose an alternative way of reducing congestion, based on an optional booking service with dedicated right-of-way. Such a service would be advantageously implemented with shared and/or autonomous vehicles, due to similar requirements and complementarity. We recognize that participation to such a program would entail an alternative-specific inconvenience and evaluate the consequences on welfare depending on the way it is administrated and on the capacity split. Overall, this thesis advances the state of knowledge regarding the prevailing traffic conditions and suggests ways to improve them. The adopted approach is largely analytical to provide insight and generality. Complementary simulations add realism and push back the limits of tractability.

EPFL2018

Toward Sustainable Ridesourcing Services: A Simulation Study on the Effects of Congestion, Fleet Size, and Willingness to Share

Caio Vitor Beojone, Nikolaos Geroliminis

Human mobility in large cities is a complex dynamical system with high density of population, many transport modes to compete for limited available space and many operators that try to efficiently manage different parts of this system. New emerging modes of transportation such as ride-sourcing (known as Transportation Network Companies – TNCs) and on-demand services create additional opportunities, but also more complexity. It is imperative to understand how TNCs' operations can interfere in traffic conditions while replacing other transportation modes to seek improvements in urban mobility. Moreover, the matching process shall have a place and thus the impact of passengers' behavior too, in a ridesplitting scenario. Therefore, this paper aims to investigate the effect of expanding fleet sizes for TNCs, passengers with different willingness to share, and operational strategies over congestion conditions under a sustainable perspective. This investigation considered a simulated urban network with real demand data from taxis in a megacity and incorporated traffic conditions through a Macroscopic Fundamental Diagram (MFD). Results show that sharing (allowing passengers to share their ride and have many passengers willing to do so) by itself is not capable of decreasing VKT if there is no control to the fleet size. On the other hand, sharing decreases the number of vehicles needed to provide high coverage and service times. To reduce emissions (by reducing VKT), TNCs should change their modus operandi; in a way to avoid that their fleet cruises without a defined trip (passenger with origin and destination).