Kun An

Service Reliability-Based Transit Network Design with Stochastic Demand

In the study on which this paper is based, a service reliability–based formulation was developed for the transit network design problem with stochastic demand. The approach considered two types of services: rapid transit services, such as bus or rail, and flexible services, such as taxi or dial-a-ride services. The notion of service reliability was used to address the issue of stochastic demand and formulate the problem as a two-phase stochastic program in which the network and frequencies of the transit lines are determined in Phase 1, and flexible services are determined in Phase 2 depending on the demand realization. The objective was to decide the optimal combination of these two service types to minimize the total expected operating cost while serving all realized origin–destination demands. The transit line alignment and passenger flow distribution pattern were studied under system optimal flows. A gradient solution approach was developed to solve the problem. An illustrative example was constructed to demonstrate the performance of the solution algorithm and the advantages of the combined services as compared with rapid transit or flexible service alone under stochastic demand.

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

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.

Congestion patterns of electric vehicles with limited battery capacity

The path choice behavior of battery electric vehicle (BEV) drivers is influenced by the lack of public charging stations, limited battery capacity, range anxiety and long battery charging time. This paper investigates the congestion/flow pattern captured by stochastic user equilibrium (SUE) traffic assignment problem in transportation networks with BEVs, where the BEV paths are restricted by their battery capacities. The BEV energy consumption is assumed to be a linear function of path length and path travel time, which addresses both path distance limit problem and road congestion effect. A mathematical programming model is proposed for the path-based SUE traffic assignment where the path cost is the sum of the corresponding link costs and a path specific out-of-energy penalty. We then apply the convergent Lagrangian dual method to transform the original problem into a concave maximization problem and develop a customized gradient projection algorithm to solve it. A column generation procedure is incorporated to generate the path set. Finally, two numerical examples are presented to demonstrate the applicability of the proposed model and the solution algorithm.

Optimizing multi-agent based urban traffic signal control system

Abstract Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods.

Transit network design with stochastic demand

After an introduction on travel strategies and a relatively brief state of the art, the chapter starts by recalling the main factors influencing path choice decision making and focuses on unreliable dynamic service networks, on which a strategy-based path choice should be used. Travel strategies, with their related hyperpaths and diversion rules, together with the different types of optimal strategies, are then defined and analysed. The search methods of the normative optimal strategies are hence presented, taking due consideration of their applications in a real-time predictive info context. Finally, some conclusions are drawn and further necessary research developments are indicated.