Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48% and 46.84%, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.

To solve the problems of when to set up connected automated vehicles (CAVs) dedicated lanes and how many CAVs dedicated lanes to set up under different penetration rates of CAVs, this work focuses on modeling the fundamental diagram of mixed traffic flow with dedicated lanes for CAVs. Firstly, the car-following modes and their proportion of mixed traffic flow without and with CAVs dedicated lanes are analyzed. Secondly, the fundamental diagram of mixed traffic flow is derived based on car-following models to analyze the traffic capacity with and without CAVs dedicated lanes. Then, the relevant properties of the fundamental diagram with CAVs dedicated lanes are proposed and proved. Finally, the sensitivity of related parameters (e.g., CAVs penetration rate, time headway, and free-flow speed) in the fundamental diagram is discussed. Results show that (1) the increase of CAVs penetration rate and free-flow speed can improve the traffic capacity; (2) the design of smaller CAVs time headway benefits traffic capacity. Moreover, one and two CAVs dedicated lanes of three manual lanes are set to improve the traffic capacity to the greatest extent when the penetration rate of CAVs reaches 0.6 and 0.86, respectively. It is noteworthy that developing CAVs dedicated lanes under a reasonable CAVs penetration rate does not waste resources and increases traffic congestion while improving traffic capacity.

Autonomous intersection management (AIM) guides vehicles to pass through signal-free intersections individually. Since vehicles are permitted to turn in any direction from any lane in AIM, it is desired to erase lane changes on the entry lane to achieve high traffic efficiency. However, erasing lane changes exposes intersections to complex conflicts. To study the trade-off between lane changes on the entry lanes and conflicts inside the intersection, this paper makes a comprehensive comparison of two kinds of intersection systems. First, all-direction turn lanes (ADTL) and specific-direction turn lanes (SDTL) are designed to distinguish lane change behaviors. Second, a two-stage method is proposed to manage the oncoming vehicles at the intersection. The first stage is timing schedule optimization and the second stage is trajectory optimization. Then, a method based on Monte Carlo Tree Search (MCTS) is designed to solve the timing schedule optimization model at high traffic demands. Finally, two parts of simulation experiments are conducted in this paper. The first part verifies the performance of the MCTS-based method. The second part compares ADTL and SDTL in multiple scenarios. The results show that ADTL outperforms SDTL in the efficient system, but the opposite conclusion appears in the fault-tolerant system. Besides, combining ADTL with lane changes may bring out the full value of ADTL.

The optimal design of the walkway at an urban rail transit station is a vital issue. The Transit Capacity and Quality of Service Manual (TCQSM) TCRP-100 report for the design of urban rail transit station walkway and the existing design models neglect the important factors such as randomness in the passenger arrival rate, randomness and state-dependent service time of the walkway and blocking phenomenon when the passenger flow demand exceeds the walkway capacity. There obviously exists a need to develop a design approach that overcomes these shortcomings. For this purpose, this paper details a simulation-based optimization approach that provides width design through automatic reconfiguration of walkway width during the simulation–optimization process based on phase-type (PH) distribution. The integrated PH/PH(n)/C/C discrete-event simulation (DES) model and optimization method that uses the genetic algorithm (GA) work together concurrently to obtain optimized (design) widths for different passenger flow and level of service (LOS) The numerical experiments are conducted to compare the proposed model with the existing design methods. It reveals that: (1) The width obtained by our proposed model is higher than the existing width design models; (2) when squared coefficient of variation of passenger arrival interval increases, the walkway width increases more for our proposed model than the existing design models; (3) when the arrival rate increases, the walkway width of our proposed model increases faster than the existing design models; (4) the increase in the length of walkway has no significant effect on the walkway width.