A link-node reformulation of ridesharing user equilibrium with network design

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

“…In [98], travelers are categorized into solo/ride-sharing drivers and passengers; a mixed complementarity program is devised for the ridesharing UE over an extended network. Path-flow based ride-sharing UE is formulated as NCP in [99], and is improved to a link-node based one in [100]. The latter one drastically reduces problem size and computational burden, and also allows problem decomposition for large-scale systems.…”

A Bi-Level Programming Model for the Railway Express Cargo Service Network Design Problem

“…To better explain the process of generating the service network and assigning the shipments to the service network, in the following we would like to provide more details using the example depicted in Figure 1. FOR PEER REVIEW 3 of 18 for maritime transportation, Wang and Lo [18] for ferry service network design, Nourbakhsh and Ouyang [19] for public transit network optimization, Crainic [4] for long-haul transportation, Bai et al [20] for stochastic service network design, Ji et al [21] for intermodal transportation network design problem, Di et al [22] for ridesharing network design problem, and Liu et al [23] for boundedly rational decision rules of travelers in a dynamic transit service network. In this paper, we propose a bi-level programming formulation for the railway express cargo service network design problem.…”

A Multi-Modal Route Choice Model with Ridesharing and Public Transit

“…Recently, there is a small but growing body of studies with respect to ridesharing activities to explain travel behavior within the transportation domain. Some researchers have conducted empirical studies [8,11,[20][21][22][23][24][25][26][27][28]; some researchers have studied travelers' route choice and mode decision making process when ridesharing activities are incorporated into the morning commute problem [29][30][31] and the traffic assignment problem [32][33][34][35][36][37]. As for empirical studies, for instance, Morency [8] used travel data from four large-scale origin-destination (OD) surveys to study the evolution of the ridesharing market in the Greater Montreal Area, and found that commuters were at one time more inclined (or forced) to share car seats and then less people chose to share rides with others (because travelers wanted more freedom to travel as they wish with the rapid urban development and economic growth); Caulfield [20] conducted a logistic regression analysis to examine the characteristics of the individuals that rode shares in Dublin, and found that females and those in couples were most likely to rideshare; Erdogan et al [21] studied the demand for ridesharing in a university campus context by using a commuter survey data, developed ordered probit models to investigate interest in ridesharing, and found that taste heterogeneity significantly affected propensity to rideshare; Lee et al [25] conducted a self-reported online survey among Uber users in Hong Kong, used the structural equation modeling technique to analyze the empirical results, and found that perceived risks, perceived benefits, trust in the platform, and perceived platform qualities significantly influenced users' intention to participate in Uber; Stiglic et al [26] used an extensive computational study to investigate the potential benefits as well as synergies of the seamless integration of ride-sharing and public transit, and found that such a system could significantly enhance mobility and increase the use of public transport.…”

“…The advent of a ridesharing program provides travelers more travel modes (i.e., sharing a ride as a driver, sharing a ride as a rider) and makes the traffic assignment model add car capacity constraints, the goal of the traffic assignment model with ridesharing is to determine travelers' path and mode choices to minimize their generalized path travel cost (not their actual path travel cost on account of the car capacity constraints). At a ridesharing user equilibrium (RUE) state, no traveler can improve his or her generalized travel cost by either unilaterally changing his or her routes or travel modes [32,33,35,36].…”

“…In a general transportation network, a ridesharing scheme was integrated into the static traffic assignment model with elastic travel demands [32] and with inelastic travel demands [35,36], assuming that ridesharing drivers can only pick up riders with the same origin-destination (OD) pair; Xu et al [33] relaxed the same-OD-pair assumption, formulated the ridesharing user equilibrium with fixed travel demands as a mixed complementarity problem, and assumed that a passenger can be picked up by a driver and then dropped off at an intermediate node to be picked up again by different drivers. However, the aforesaid traffic assignment studies ignore the public transit mode (i.e., transit passenger).…”