Modal congestion management strategies and the influence on operating characteristics of urban corridor

Fatima, K 2015, Modal congestion management strategies and the influence on operating characteristics of urban corridor, Masters by Research, Civil, Environmental and Chemical Engineering, RMIT University.

Document type: Thesis
Collection: Theses

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Title Modal congestion management strategies and the influence on operating characteristics of urban corridor
Author(s) Fatima, K
Year 2015
Abstract In recent years, traffic congestion has become a major problem in transportation networks particularly in peak periods. The congestion problem has become worse in large cities, particularly in the CBDs over the last few decades and the problem intensifies every year. Traffic congestion results in longer travel times, larger delays, more fuel consumptions and more emission. Australian Bureau of Infrastructure, Transport and Regional Economics estimates wastage of a total of $9.4 billion as the social cost of congestion for the year 2005 in the major Australian cities and the cost will be around that the avoidable social costs of congestion will be more than doubled by 2020.

Traffic congestion problem in Australia may be the consequence of many factors. Congestion is generally worse in inner suburbs than outer areas. A large number of commuters travel to/from work and school on same time intervals representing the most significant contributors of congestion. Rapid increases in population and number of cars are the key reasons of traffic congestion. Other key causes of congestion relating to both supply and demand sides include under-pricing of road use, lack of proper road infrastructure design/operation, travel demand growth and inadequate public transport alternatives. Traffic congestion is a consequence of all these factors. Therefore, effective demand management can reduce traffic congestions in urban areas.

This research intends to propose a solution to reduce traffic congestion and consequently traffic delay for a congested corridor in Melbourne metropolitan area. To achieve this, a number of strategies are introduced and their influence on traffic congestion is evaluated. Dedicated bus/tram lane, Bus/tram stop location change, Parking restriction, Bicycle lane restriction, Passenger car movement restriction, Lane configuration, advanced green signal and extended green signal will be applied to reduce congestion strategies.

This research models the selected Melbourne corridor using SIDRA software with real time datasets (collected from VicRoads, Site Survey, Google maps, Bus network and Tram network data). Different parameters of the data sets such as speed limit, traffic volume (number of tram, bus and passenger cars), signal timing (red, yellow and green times), number of lanes, direction of lane traffic movement and parking restrictions are collected from data sources. Datasets are used to model each intersection of the study corridor. The model of each intersection is calibrated and validated with collected datasets from site survey. After validation the intersections are added as a network or corridor. The corridor also needed to be calibrated and validated using collected site survey datasets. The variables used for calibration are traffic blockage, delay times, average speeds and queue lengths which are experienced by different modes of transport. After the model validation, modal management strategies are applied to the corridor to reduce traffic congestion. Three main parameters including average speed, delay time and queue length experienced by different modes of transport are compared to identify strategies to control congestion. Modifying the order of signal phases and cycle length is the best simulated strategy for this corridor. Besides that applying parking restriction to this corridor during peak period also reduces congestion.
Degree Masters by Research
Institution RMIT University
School, Department or Centre Civil, Environmental and Chemical Engineering
Keyword(s) Multi-modal management
Urban corridors
Microscopic traffic simulation
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Created: Mon, 09 Nov 2015, 08:52:44 EST by Denise Paciocco
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