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.
This paper presents an advanced transit signal priority (ATSP) control model that considers bus progression at downstream intersections when giving priority at upstream intersections and stochastic bus arrival times. The ATSP control model is applicable to arterials with bus lanes. At the center of the ATSP control model is a stochastic programming model formulated to find the optimum priority strategies at each intersection of interest, which minimize bus delays while having the least negative impact on general traffic. The uncertainty in bus arrivals is taken into account by considering stochastic bus dwell times. The ATSP control is implemented in a traffic micro-simulation environment and compared with conventional transit signal priority (CTSP) control. Extensive simulation experiments are conducted with different traffic congestion levels, bus headway levels, and dwell-time distributions. Results show that the ATSP control generates an additional reduction in bus delay of around 10% when compared to the CTSP control.
This paper analyzes the influence of urban development density on transit network design with stochastic demand by considering two types of services, rapid transit services, such as rail, and flexible services, such as dial-a-ride shuttles. Rapid transit services operate on fixed routes and dedicated lanes, and with fixed schedules, whereas dial-a-ride services can make use of the existing road network, hence are much more economical to implement. It is obvious that the urban development densities to financially sustain these two service types are different. This study integrates these two service networks into one multi-modal network and then determines the optimal combination of these two service types under user equilibrium (UE) flows for a given urban density. Then we investigate the minimum or critical urban density required to financially sustain the rapid transit line(s). The approach of robust optimization is used to address the stochastic demands as captured in a polyhedral uncertainty set, which is then reformulated by its dual problem and incorporated accordingly. The UE principle is represented by a set of variational inequality (VI) constraints. Eventually, the whole problem is linearized and formulated as a mixed-integer linear program. A cutting constraint algorithm is adopted to address the computational difficulty arising from the VI constraints. The paper studies the implications of three different population distribution patterns, two CBD locations, and produces the resultant sequences of adding more rapid transit services as the population density increases.
This paper presents an integrated optimal controller for the tandem intersection with lane function design and signal control, aiming to improve intersection efficiency and service reliability with a multi-objective formulation. The tandem intersection is a type of unconventional intersection that can re-organize the vehicles at entrance lanes with sorting areas, and improve intersection capacity through the coordination of pre-signals and main signals. However, most existing studies related to tandem intersection control assume that the lane functions in the sorting areas for both the through and left-turn movements are the same, and the traffic demand remains static. To fill these gaps, this paper first identifies six different tandem control modes based on the different lane functions and phase sequence schemes in the sorting area, and the corresponding delay models for each mode are derived. Furthermore, an integrated optimization model is developed to minimize the mean and semistandard deviation of the intersection delay, and the Non-dominated Sorting Genetic Algorithm-II is used to obtain the optimal solution. A case study is conducted in a real-world intersection in Melbourne, Australia, under various traffic conditions. The results show that the proposed method can decrease average delay and queue length by 19.61% and 20.94%, respectively, compared with conventional intersection design.
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