Overlapping phase and split phase are two typical phase patterns at signalized intersections in practice. The existing bandwidth-oriented progression control schemes mainly focus on overlapping phase to adopt the various layout of intersections and volumes rather than split phase. To address this issue, a two-way signal progression model, namely Pband, is developed to simultaneously optimize phase pattern choice from overlapping phase or split phase, phase sequence, and offsets. Also, the proposed Pband is formulated with a mixed integer nonlinear programming technique to guarantee an optimal solution. A numerical test based on a field arterial from the county of Jiashan, Jiaxing, China is employed to validate Pband under designed traffic patterns and signal timings. Numerical results have indicated that the Pband can show promise in increasing progression bandwidths and reducing average travel delay and the number of stops via traffic simulation experiments compared with the conventional Multiband model.

This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO₂ emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared.

This study proposed a mathematical model for designing a feeder transit service for improving the service quality and accessibility of transportation hubs (such as airport and rail station). The proposed model featured an integrated framework, which simultaneously guided passengers to reach their nearest stops to get on and off the bus, designed routes to transport passengers from these selected pick-up stops to the transportation hubs, and calculated their departure frequencies. In particular, the maximum walking distance, the upper and lower limits of route frequencies, and the load factor rate of each route were fully accounted for in this study. The main objective of the proposed model was to simultaneously minimize the total walking, riding time, and waiting time of all passengers. As this study explored an NP-hard problem, a two-stage genetic algorithm combining the Dijkstra search method was further developed to yield metaoptimal solutions to the model within an acceptable time. Finally, a test instance in Chongqing City, China, demonstrated that the proposed model was an effective tool to generate a pedestrian, route, and operation plan; it reduced the total travel time, compared with the traditional model.

In the existing bandwidth-based methods, through traffic flows are considered as the coordination objects and offered progression bands accordingly. However, at certain times or nodes in the road network, when the left-turn traffic flows have a higher priority than the through traffic flows, it would be inappropriate to still provide the progression bands to the through traffic flows; the left-turn traffic flows should instead be considered as the coordination objects to potentially achieve better control. Considering this, a general maximum progression model to concurrently synchronize left-turn and through traffic flows is established by using a time-space diagram. The general model can deal with all the patterns of the left-turn phases by introducing two new binary variables into the constraints; that is, these variables allow all the patterns of the left-turn phases to deal with a single formulation. By using the measures of effectiveness (average delay time, average vehicle stops, and average travel time) acquired by a traffic simulation software, VISSIM, the validity of the general model is verified. The results show that, compared with the MULTIBAND, the proposed general model can effectively reduce the delay time, vehicle stops, and travel time and, thus, achieve better traffic control.

Bandwidth-based traffic signal coordination has long been an effective technique to make traffic flows within a network more efficient, smoother, and safer. Existing network bandwidth optimization models mainly focus on maximizing the bandwidth under NEMA phasing. The network bandwidth maximization under other typical phasing schemes, namely the split or mixed phasing, has not been intensively studied. To address this, a group of bandwidth-based network traffic signal coordination models is proposed for different types of traffic networks. All the proposed models can simultaneously optimize the key signal control variables, namely the phase sequences, offsets, and common cycle times. Additionally, all the developed models are formulated as mixed-integer linear programming problems, which guarantees that the global optimal solutions can be obtained using the branch-and-bound algorithm. The results of the presented index, which is defined as the absolute difference of the interference variables obtained through theoretical solutions and time—space diagrams, indicate that all the proposed models are correct. Furthermore, simulation results demonstrate that split phasing can significantly reduce the average delay time and the average number of stops compared with the existing NEMA phasing.