公共电动自行车系统运营模式设计模型

当前对传统公共自行车系统运营模式的研究主要是定性分析,各种方法标准不一,且研究结论与实际差别较大,不能直接用于资金、设施等要求更高的公共电动自行车系统的运营模式设计问题.因此,提出了一种定量设计模型,利用0-1混合整数规划模型对系统的经营主体进行筛选,再利用动态规划模型分配资源.利用算例进行模拟分析,结果表明:①与以资金多和收益好为优的常规的定性分析法相比,定量模型能更合理配置系统构成要素,降低政府的财政负担,分别为政府节省18.68%和23.09%的资金.②在公共电动自行车系统建设和运营过程中,宜采用政企合作模式.③相对于系统运营而言,系统建设期间对政府投入的资金反应更灵敏,因此政府有必要加大建设期的资金投入.     

基于均衡流量的城市停车费用优化模型

基于随机用户平衡原则, 分析了出行者的路径选择和停车选择行为, 建立了满足Logit关系的城市道路停车流量分配模型。基于收益管理思想, 以停车收益最大为决策目标, 建立了城市停车费用的双层规划模型, 并以7个节点和2个停车场的小型网络进行实例验证。分析结果表明: 当停车需求为10 000pcu·h^-1时, 随着停车场2停车费用的提高, 停车流量不断向停车场1转移, 停车场1在停车费用不变的情况下收益不断增加, 当停车场2的停车费用为4元·h^-1时, 总的停车收益达到最大; 当停车需求为20 000pcu·h^-1时, 随着停车费用的上升, 总的停车收益将持续增加; 2个停车场之间存在博弈关系, 停车费用存在纳什均衡点, 当停车需求为10 000pcu·h^-1时, 2个停车场的最优停车费用均为5元·h^-1; 当停车需求为20 000pcu·h^-1时, 2个停车场的最优停车费用均为最高限价10元·h^-1。     

PHEV能量管理策略研究

针对一款串联型插电式混合动力公交车,文章基于MTLALB建立整车和各部件数值模型。在中国典型城市公交工况下,建立基于全局优化的DP策略,并与CD-CS策略和PMP策略进行能耗对比分析。结果表明:(1)基于CD-CS策略的后期SOC在一定范围内波动,DP策略与PMP策略的SOC轨迹近似呈线性变化。(2)CD-CS策略较DP策略的能耗成本高22.68%,PMP策略比DP策略的能耗成本高0.30%。DP策略与PMP策略可以合理分配能量源,实现全局最优,但DP策略相比PMP策略计算量大。     

基于个体出行链的区域城际铁路规划方法研究

基于"基础设施即服务"的理念研究区域城际铁路规划问题,精准考虑带时间窗的个体出行特征和运输服务供给,构建时空网络联系基础设施、服务供给和出行需求3个维度.在所有路段存在的前提下,考虑城际铁路、城市轨道交通和城市道路等基础设施及其运输服务供给,为每类出行者生成满足其出行时间窗的备选出行链集合,以所有出行者广义费用和总投资...     

Dynamic traffic metering in urban street networks: Formulation and solution algorithm

Traffic metering offers great potential to reduce congestion and enhance network performance in oversaturated urban street networks. This paper presents an optimization program for dynamic traffic metering in urban street networks based on the Cell Transmission Model (CTM). We have formulated the problem as a Mixed-Integer Linear Program (MILP) capable of metering traffic at network gates with given signal timing parameters at signalized intersections. Due to the complexities of the MILP model, we have developed a novel and efficient solution approach that solves the problem by converting the MILP to a linear program and several CTM simulation runs. The solution algorithm is applied to two case studies under different conditions. The proposed solution technique finds solutions that have a maximum gap of 1% of the true optimal solution and guarantee the maximum throughput by keeping some vehicles at network gates and only allowing enough vehicles to enter the network to prevent gridlocks. This is confirmed by comparing the case studies with and without traffic metering. The results in an adapted real-world case study network show that traffic metering can increase network throughput by 4.9–38.9% and enhance network performance.     

Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO₂ and SO_x) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.     

An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput

In this research, we present a data-splitting algorithm to optimally solve the aircraft sequencing problem (ASP) on a single runway under both segregated and mixed-mode of operation. This problem is formulated as a 0–1 mixed-integer program (MIP), taking into account several realistic constraints, including safety separation standards, wide time-windows, and constrained position shifting, with the objective of maximizing the total throughput. Varied scenarios of large scale realistic instances of this problem, which is NP-hard in general, are computationally difficult to solve with the direct use of commercial solver as well as existing state-of-the-art dynamic programming method. The design of the algorithm is based on a recently introduced data-splitting algorithm which uses the divide-and-conquer paradigm, wherein the given set of flights is divided into several disjoint subsets, each of which is optimized using 0–1 MIP while ensuring the optimality of the entire set. Computational results show that the difficult instances can be solved in real-time and the solution is efficient in comparison to the commercial solver and dynamic programming, using both sequential, as well as parallel, implementation of this pleasingly parallel algorithm.     

A multiobjective land development optimization model: the case of New Castle County,Delaware

This paper develops a multiobjective optimization model to consider transportation impacts of the future development of land. The output of the model is the best location and type of land use that has minimal negative transportation effects and uses the maximum available public transportation infrastructure. It provides tools for both planners and transportation engineers and enables them to consider different scenarios of possible policies and land development. Since multiple objectives and their nonlinear structures are considered, the model is solved using mixed integer nonlinear programming. The final results are shown in both tabular and graphical format. The effectiveness of the model is applied to the northern part of New Castle County, Delaware. The results show that the model successfully finds the best locations for both residential and commercial land uses in order to meet several criteria discussed in the paper.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.