Evaluation of Signal Optimization Software : Comparison of Optimal Signal Pans from TRANSYT and LinSig – A Case Study

• Main Author: ODHIAMBO, EVANS OTIENO
• Format: Others
• Language: English
• Published: KTH, Transportplanering 2019
• Subjects: TRANSYT; LinSig; Delay; Practical Reserve Capacity; Optimal cycle time; Optimization.; Other Engineering and Technologies; Annan teknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259541

The design of traffic signal control plan is directly related to the level of traffic congestion experienced both at the junction level and the network particularly in urban areas. Ensuring signals are well designed is one of the most cost-effective ways of tackling urban congestion problems. Signal time plans are designed with the help of signal optimization models. Optimization can either be done for multiple or single objectives and is formulated as a problem of finding the appropriate cycle lengths, green splits, and offsets. Some of these objective functions include; better mobility, efficient energy use, and environmental sustainability. LinSig and TRANSYT are two of the most widely used traffic signal optimization tools in Sweden. Each of them has an inbuilt optimization function which differs from the other. LinSig optimizes based on delay or maximum reserve capacity while TRANSYT optimization is based on performance index (P.I) involving delay, progression, stops and fuel consumption.This thesis compared these optimization models through theoretical review and application to a case study in Norrköping. The theoretical review showed that both TRANSYT and LinSig have objective functions based on delay and its derivatives. The review also showed that these models suffer from the inability to accurately model block back as they are based on the assumption of vertical queuing of traffic at the stop line. Apart from these similarities, these two models also have significant variations with respect to modeling short congested sections of the network as well as modeling mixed traffic including different vehicle classes, pedestrians, and cyclists.From the case study, TRANSYT showed longer cycle time compared to LinSig in both scenarios as its optimization objectives include both delay and stops while LinSig accounts for only delay. The Allocation of phase green splits and individual junction delay was comparable for undersaturated junctions while congested network sections had significant differences. Total network delay was, however, less in LinSig compared to TRANSYT. This could be attributed to different modeling criteria for mixed traffic and congested network in addition to the fact that cyclists were not modeled in TRANSYT. VISSIM simulation of the two-signal time plans showed that network delay and queue lengths from TRANSYT signal timings are much less compared to LinSig time plans. A strong indication of better signal coordination.