text: We consider a transportation system of heterogeneously connected vehicles, where not all vehicles are able to communicate. Heterogeneous connectivity in transportation systems is coupled to practical constraints such that (i) not all vehicles may be equipped with devices having communication interfaces, (ii) some vehicles may not prefer to communicate due to privacy and security reasons, and (iii) communication links are not perfect and packet losses and delay occur in practice. In this context, it is crucial to develop control algorithms by taking into account the heterogeneity. In this paper, we particularly focus on making traffic phase scheduling decisions. We develop a connectivity-aware traffic phase scheduling algorithm for heterogeneously connected vehicles that increases the intersection efficiency (in terms of the average number of vehicles that are allowed to pass the intersection) by taking into account the heterogeneity. The simulation results show that our algorithm significantly improves the efficiency of intersections as compared to the baselines.
  @cite_4: The paper presents the design approach, the objectives, the development, the advantages, and some application results of the traffic-responsive urban control (TUC) strategy. Based on a store-and-forward modelling of the urban network traffic and using the linear-quadratic regulator theory, the design of TUC leads to a multivariable regulator for traffic-responsive co-ordinated network-wide signal control that is particularly suitable also for saturated traffic conditions. Simulation investigations demonstrate the efficiency of the proposed approach. Results of TUC's first field implementation and evaluation are also presented. Finally, summarising conclusions are drawn and future work is outlined.
  @cite_22: A formula is derived for the average delay to vehicles at junctions controlled by fixed-cycle traffic signals. The predicted delays are close to those observed in practice. Optimum signal settings are then derived using the delay formula.
  @cite_8: Abstract PRODYN is an algorithm able to compute in real time the best signal settings with respect to the delay criterion for any flow demand in traffic networks. The hierarchical algorithm uses Forward Dynamic Programming (FDP) to compute controls at the lower level (intersections) and decomposition coordination technics at the upper level. The implementation on the field relies on the use of network structure of microprocessors the tasks of which are optimization and state estimation. The major goal to reach was to solve for such an application the dimensionality problem of FDP. Results show substential gains on delay to give an idea of about 16 with respect to fixed time policies.
  @cite_21: Abstract The paper discusses a real-time traffic-adaptive signal control system referred to as RHODES. The system takes as input detector data for real-time measurement of traffic flow, and “optimally” controls the flow through the network. The system utilizes a control architecture that (1) decomposes the traffic control problem into several subproblems that are interconnected in an hierarchical fashion, (2) predicts traffic flows at appropriate resolution levels (individual vehicles and platoons) to enable pro-active control, (3) allows various optimization modules for solving the hierarchical subproblems, and (4) utilizes a data structure and computer communication approaches that allow for fast solution of the subproblems, so that each decision can be downloaded in the field appropriately within the given rolling time horizon of the corresponding subproblem. The RHODES architecture, algorithms, and its analysis are presented. Laboratory test results, based on implementation of RHODES on simulation models of actual scenarios, illustrate the effectiveness of the system.
  @cite_1: 
  @cite_3: Microsimulation models are normally used to evaluate traffic-adaptive signal control systems. This paper develops an analytical approach for this evaluation based on queuing models. In particular, a queuing model is developed for a simplified adaptive control strategy that is based on rolling horizon scheme, in which a signal serves two movements alternatively. In this strategy, the first movement is served until the queue dissipates, then the second movement is served until the queue dissipates, then the signal goes back to serving the first movement, and this cyclic process repeats. A numerical algorithm is developed in a stochastic context to compute steady-state performance measures such as average delays and expected queue lengths. These results are compared with simulation-based results, and indeed, the analytically derived numerical method predicts well the simulation results
  @cite_6: 
  @cite_19: Many large cities have, or plan to have, urban traffic control (utc) systems that centrally monitor and control the traffic signals in their jurisdiction. The present generation of utc systems usually co-ordinates the signals on fixed-time plans, which consist of sets of timings that determine when each signal turns red and green. The plans are precalculated to suit average conditions during each part off the day (e.g. A.M. peak) and do not respond to variations in flows in the network. Since 1973 the UK Transport and Road Research Laboratory has been researching a vehicle responsive method of signal control called SCOOT (split, cycle and offset optimisation technique). Research was carried out in Glasgow by a small team from TRRL and the Ferranti, GEC and Plessey traffic companies, with assistance from Strathclyde Regional Council. In 1976 the success of the research phase led to a development project between the departments of transport and of industry and the three traffic companies. TRRL continued research into SCOOT and in 1979 carried out a full-scale trial of SCOOT in Glasgow. As part of the development project, and with the co-operation of West Midlands County Council, SCOOT was installed in Coventry. A further full-scale trial of the developed system was carried out in 1980. This paper describes the SCOOT system and the results of the trials which compared SCOOT with up-to-date fixed-time systems. It is concluded that SCOOT reduced vehicle delay by an average of about 12 percent during the working day. The surveys demonstrate that scoot rapidly adapts to unusual traffic conditions as well as to the usual variations in demand that occur throughout the day and night. It is an important benefit of SCOOT that there is no need to periodically prepare new fixed-time plans and that the signal timings are automatically kept up-to-date. The traffic model in SCOOT provides real-time information on flows and queues and is likely to be a key element in the development of new traffic management strategies that make the best overall use of roads in urban areas. This paper is a shortened version of TRRL Report LR 1014 (see TRIS 348845). This paper was presented at the IEE's Conference on Road Traffic signalling, Londong, March 1982. See also TRIS abstracts 368871 and 368872. (TRRL)
  @cite_5: Optimization Policies for Adaptive Control (OPAC) is a computational strategy for real-time demand-responsive traffic signal control. It has the following features: (a) It provides performance results that approach the theoretical optimum, (b) it requires on-line data that can be readily obtained from upstream link detectors, (c) it is suitable for implementation on existing microprocessors, and (d) it forms a building block for demand-responsive decentralized control in a network. Studies undertaken in the development of this strategy and the testing of its performance via the NETSIM simulation model are described.
  @cite_15: 
summary: Traffic phase scheduling: Design and development of traffic phase scheduling algorithms have a long history; more than 50 years @cite_22 . Thus, there is huge literature in the area, especially on the design of optimal pre-timed policies @cite_22 @cite_6 @cite_1 , which activate traffic phases according to a time-periodic pre-defined schedule. These policies do not meet expectations under changing arrival times, which require adaptive control @cite_3 . The adaptive control mechanisms including @cite_4 , @cite_5 , @cite_8 , @cite_19 , @cite_15 and @cite_21 , optimize control variables, such as traffic phases, based on traffic measures, and apply them on short term.