A Study On Sectional Green Wave Model With Transit Signal Priority And Coordinated Control On Arterials

The growing urbanization in China has brought challenges along with opportunities to our urban transportation development nowadays. The increasing traffic demands has put much burden on urban networks, leading to more and more heavy traffic jams in all major cities. Public transit, as an energy-saving traffic mode due to its capability of transporting a large number of passengers at one time, has been treated as a key solution to relieve urban traffic congestions. Therefore, developing public transit has been treated as a national strategy and achieved more and more attention in recent years. Though there are a variety of researches and achievements in the field of public transit priority, they seldom formed as a mature transport system, and are imperfect to fit into the transportation system in our country. Hence, researches needs to be done to figure out how to improve the operation efficiency of public transit arterials, as they’re important in the public transit system.This dissertation is part of the research program-"Traffic condition sensation and guarantee of urban transit arterials"-which is supported by the National High Technology Research and Development Program of China (863 Program). Combined with simulation data, this dissertation carried out studies from the following aspects about public transit priority on transit arterials:First, according to the operational characteristics of transit vehicles, the author built a transit priority control model considering coordination between multiple intersections based on green wave calculation model using algebraic method and time-space equivalent conversion theory. In this model, transit priority is realized through a control linkage of sectional green waves along the intersections on the arterial. The model chooses the reference value of optimal cycle length based on the actual traffic situation, and calculates the travel time of an equivalent road section. The model then sets up and solve an objective function maximizing the green wave band, and acquire the signal timing plans of the green waves along each segments, as well as the optimal driving speed of the transit vehicles on each road sections. A feedback mechanism of vehicles’ guidance information is built in this dissertation using this model.Second, the author established distribution requirements for transit priority detectors, including setting up signal control machines at intersections and installing information transmission devices in the transit vehicles. Based on the techniques of transit vehicle detection and information transmission, the author proposed methods to decide the optimal sequence of the signal priorities from multiple transit vehicles and methods to decide the optimal signal plan for realizing transit priority. The idea is to sort all the priority requests according to their importance and adjust their signal timings before each phase begins. The optimal results will be sent back to each transit vehicles afterwards.Third, according to the characteristics of urban transit distribution and bus operation, the author developed the optimal bus headway calculating method considering overlapping bus routes. The objective function is based on maximizing the interests of both passengers and environment, and the restrictive functions are carefully chosen. The optimal bus headway can be used in flat peak periods. According to the bus operation characteristics in peak periods, the author put forward the transit operation and management method for peak periods.Last but not least, based on the field data collected on Lingnan Arterial and Fenjiang Road in Foshan, China, the author uses the micro simulation software, VISSIM, to simulate the traffic operation at multiple intersections under both the transit priority coordinated control model with linkage of sectional green waves and the transit priority coordinated control model with multiple transit priority requests, and evaluates the mode according to the key indicators. Moreover, the author makes an evaluation of the departure time control model by comparing the calculated the departure time of transit vehicles at their terminal stations with the information from the field data.