Ridesplitting optimization is one of the hot issues in the studies of on-demand ride services. An efficient ridesplitting optimization model can provide an insightful basis for the platform's decision-making and management. In reality, on-demand ride service orders include pre-trip reservation orders and real-time orders. This paper proposes a grouping approach to ridesplitting optimization for pre-trip reservation orders in a large-scale urban road network. To improve the ridesplitting rate, we combine reservation orders into travel groups which can be dispatched to vehicles and model this process as an integer linear programming problem. Both CPLEX and a greedy algorithm are used to solve the problem and get the travel groups. Besides, we adopt the CPLEX to solve the dispatching problem between vehicles and travel groups by minimizing the total waiting time of passengers. Finally, the proposed approach is applied to a numerical experiment in a large-scale network based on real-world trip order data of DiDi Chuxing. The results show that our model can effectively improve the ridesplitting rate. © ASCE.

We present a model of the ride-sourcing market where users differ in their characteristics. Passenger demand is derived in terms of passengers' value of time and willingness to pay. Driver supply is derived in terms of drivers' value of time and willingness to accept. Demand and supply are matched based on a general bilateral matching function. Contrary to intuition, we find that both the same-side and cross-side network effects at a stationary equilibrium state can be either positive or negative, which are determined by the signs of terms called characteristic times. Characteristic times depend on factors such as distributions of user characteristics, the endogenously determined passenger waiting time and driver idle time, the returns to scale of the production of passenger-driver matchings, platform price structure, and driver cost function. A numerical experiment is presented to illustrate these theoretical findings. © ASCE.

This paper proposes a framework of future-oriented agent-based modeling and simulation (ABMS) for various operational scenarios and optimization of shared automated electric vehicles (SAEVs). We establish an efficient scheduling algorithm between vehicles and passengers, and real-time matching algorithm for vehicles and charging stations. The scheduling algorithm includes two processes. First, each customer finds a candidate vehicle, and then the platform performs the final scheduling. The ABMS framework simulates the complicated matching relationship and interactions among the on-demand ride services platform, passengers, vehicles, and charging stations. Field operations of a large fleet of SAEVs are implemented using the real ride-sourcing order data in the road network of Hangzhou, China. The simulation results under different scenarios are comprehensively compared. The sensitivity of several critical parameters is analyzed, e.g., the SAVE fleet size, recharge mileage, and charging speed. The proposed ABMS modeling framework can be extended to incorporate a variety of vehicle types, and support decision making of advanced vehicle scheduling strategies, pricing, and relocation. © ASCE.

Travel time variability in a network is an important measure of the transportation system performance and a major factor influencing the decision-making of system management such as congestion pricing. The congestion-pricing problem with reliability maximization as the goal of a transportation network is characterized by expensive-to-evaluate objective functions without closed forms. In this paper, an effective simulation-based optimization (SBO) method is utilized to solve the problem. The network reliability is measured by a weighted average link travel time coefficient of variation (CV) with link traffic flows as the weights. We employ DynusT to evaluate the system reliability as the objective function corresponding to different toll charges for a new toll road in Maryland. The results show that the two optimal toll charge strategies improve the network-wide reliability, reducing the weighted travel time CV by 9.60% and 1.16%, respectively, when compared to the baseline toll.