On Signal Timing Of The Urban Road Intersections Based On Multi-objective Programming

With the rapid increase of private car possession, urban traffic becomes increasingly crowded and traffic problems become serious. As the intersection is the key point of urban road, it is the main cause of the road congestion and the bottleneck of urban road traffic system. There are so many ways to improve the operating efficiency, in which signal timing is one of the most direct and effective way. Therefore, there is important theoretic value and practical meaning to study urban road signal timing.In this dissertation, it takes urban road intersection signal timing as the research object, and selects the delay, number of stops, and capacity as the indexes of measuring timing scheme. It establishes a multi-objective signal timing model, uses fuzzy compromised ideological to transforme it into a single objective function, which is sovled by using the dynamic acceleration constant coordination with the inertia weight particle swarm optimization (WCPSO), and then gets improved scheme.Based on the existed research results, this dissertation mainly takes the following research work:1. It reviews the development of urban road intersection signal history, and introduces the basic concepts of signal control, signal phase and signal stage which are distinguished by diagram of intersection signal timing. TRANSYT, SCATS, SCOOT which are widely used are had a brief introduction.2. The target of delay, number of stops, capacity is selected to measure the operating efficiency of signalized intersections. Transition function theory and HCM2000 method of calculating the delay are contrasted and analyzed:both the phase delay, respectively, lane group delay as an essential component of the total delay, both normal delays and random delays in the calculation method is not the same.Webster theory, HCM2000, and the domestic approaches (the method of urban road design specification, stop line method, the conflict point method) for computing capacity are contrasted and analyzed:phase capacity, respectively, lane groups traffic capacity, lane traffic capacity as an essential component of the total of capacity, the core idea of the three calculations are the product of saturated flow and split ratio.3. By analyzing the existed multi-objective programming method of signal timing model, it proposes a multi-objective programming model for signal timing based on a fuzzy compromise programming. Using fuzzy compromise programming approach to transform the delay, number of stops, capacity into an single objective function, it makes the three dimensionless and values between zero and one. Calculated based quantitative and qualitative analysis supplemented, it uses fuzzy preference method to determine the membership function weights in a single objective function and the dynamic acceleration constant coordination with the inertia weight particle swarm optimization (WCPSO)(maximum number of iterations, the number of particles were collected 500,20) and to solve the single objective function.4. By research the basic data is obtained and problems are analised. By calculating effective green time of phase is obtained respectively p=l, p=2, p=+∞and delays, number of stops, capacity of the intersection total and sub-phase are calculated. Through comparative analysis, it chooses the programme (p=+∞) as the improved programme: cycle before and after improvement are:189s,150s, effective green time are:173s,134s, which is compared with the previous program signals:the intersection total delays, the total number of stops, total capacity, decreased by 35.8%,19.4%,2.4%, the phase average vehicle delay time decreased by 20.0%,15.3%,18.6%,24.1%, then the number of stops and the traffic capacity partly improved according to the phase.5. To study the impact of the signal timing to fuel consumption and exhaust emissions, it uses Synchro6.0 to simulate the situation of intersection before and after the improvements. It selects the fuel consumption, CO emissions, NOx emissions and VOC emissions of the four phases to analy:the fuel consumption decreased by 10.11%, 3.92%,2.15%,16.95%; CO emissions decreased by 10.85%,2.35%,3.01%,16.93%; NOx emissions decreased by 10.90%,2.20%,2.98%,16.98%; VOC emissions decreased by 10.97%,2.30%,2.99%,17.06%.