Recent studies have demonstrated that Macroscopic Fundamental Diagram (MFD), which provides an aggregated model of urban traffic dynamics linking network production and density, offers a new generation of real-time traffic management strategies to improve the network performance. However, the effect of route choice behavior on MFD modeling in case of heterogeneous urban networks is still unexplored. The paper advances in this direction by firstly extending two MFD-based traffic models with different granularity of vehicle accumulation state and route choice behavior aggregation. This configuration enables us to address limited traffic state observability and to scrutinize implications of drivers’ route choice in MFD modeling. We consider a city that is partitioned in a small number of large-size regions (aggregated model) where each region consists of medium-size sub-regions (more detailed model) exhibiting a well-defined MFD. This paper proposes a route guidance advisory control system based on the aggregated model as a large-scale traffic management strategy that utilizes aggregated traffic states while sub-regional information is partially known. In addition, we investigate the effect of equilibrium conditions (i.e. user equilibrium and system optimum) on the overall network performance, in particular MFD functions.
Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers’ path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities’ utility and that BEV drivers incline to choose the route with charging stations and less charging time.
Despite the growing availability of big mobility data in cities, methodologies to extract meaningful information from them are still scarce. In this paper, we investigate taxi trips in New York City, develop a large-scale weighted and directed mobility network, and apply a macroscopic methodology to extract the spatial-temporal structure of urban mobility. We also present a new approach to study weighted networks of mobility in which links in the network have journey speed or travel time attribute in addition to commonly used link weights representing number of trips between pairs of nodes. We show that the structure of mobility network in a city when temporal characteristics and variations are taken into account exhibit different properties than what was previously observed. Results provide a better understanding of mobility characteristics in cities.
Ride-sourcing is a prominent transport mode because of its cost-effectiveness and convenience. It provides an on-demand mobility platform that acts as a two-sided market by matching riders with drivers. The conventional models of ride-sourcing systems are based on equilibrium assumption, discrete, and suitable for strategic decisions. This steady-state approach is not suitable for operational decision-making where there is noticeable variation in the state of the system, denying the market enough time to balance back into equilibrium. We introduce a dynamic non-equilibrium ride-sourcing model that tracks the time-varying number of riders, vacant ride-sourcing vehicles, and occupied ride-sourcing vehicles. The drivers are modeled as earning-sensitive, independent contractor, and self-scheduling and the riders are considered price- and quality of service-sensitive such that the supply and demand of the ride-sourcing market are endogenously dependent on (i) the fare requested from the riders and the wage paid to the drivers and (ii) the rider's waiting time and driver's cruising time. The model enables to investigate how dynamic wage and fare set by the ride-sourcing service provider affect supply, demand, and states of the market such as average waiting and search time especially when drivers can freely choose when to start and finish working. Furthermore, we propose a controller based on the model predictive control approach to maximize the service provider's profit by controlling the fare requested from riders and the wage offered to drivers to satisfy a certain quality of market performance. We assess three pricing strategies where the fare and wage are (i) time-varying and unconstrained, (ii) time-varying and constrained so that the fare is higher than the wage such that the instantaneous profit is positive, and (iii) time-invariant and fixed. The proposed model and controller enable the ride-sourcing service provider to offer a wage to the drivers that is higher than the charged fare from the riders. The result demonstrates that this myopic loss can potentially lead to higher overall profit when customer demand rate who may opt to use the ride-sourcing system increases while the demand of ride-sourcing vehicles decreases simultaneously.
Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies for individuals and their peers in multiple pairwise games. Payoffs are estimated for two primary evaluation criteria: efficiency and safety, which are quantified by incorporating driver tradeoffs. After that, the discretionary lane-changing (DLC) model is calibrated and validated by real-world vehicular trajectory data. To manipulate drivers' DLC behaviors, two types of lane-changing tolls based on local-optimal and global-optimal rules are introduced to align individual preferences with social benefits. We find prices can close this gap and achieve 'win-win' results by reducing drivers' unnecessary lane changes in the congested traffic. Meanwhile, the tolls collected can be used to compensate drivers who get delayed when yielding, to encourage appropriate yielding behavior and a pseudo-revenue neutral tolling system.
Perimeter traffic flow control, based on Macroscopic Fundamental Diagram (MFD) has been developed for traffic congestion control in heterogeneously congested cities. The perimeter control can be realized based on a set of traffic signals on the border between the urban regions that manipulates transferring flows between the regions to regulate the overall urban traffic system states towards a desirable condition considering disturbances and uncertainties in the modeling and state observability. This paper presents robust control of the two-region MFD system with perimeter control addressing different sources of uncertainties. In this paper two types of controller, sliding mode control (SMC) and linear quadratic regulator (LQR) control, are designed based on nonlinear and linearized system dynamics, respectively. The SMC is designed to assure robustness against all type of uncertainty. A simulation study is performed to evaluate the effectiveness of the proposed control approaches.
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