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Multi-Modal Traffic Signal Design under Safety and Operations Constraints


Multi-Modal Traffic Signal Design under Safety and Operations Constraints

Rouhieh, Behzad (2010) Multi-Modal Traffic Signal Design under Safety and Operations Constraints. Masters thesis, Concordia University.

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Currently, most transportation agencies design signal timing plans for intersection with the main objective of minimizing vehicular traffic delay while ensuring compliance with basic safety guidelines. Often times along urban roadways where automobiles share the space with large volumes of non-motorized users (i.e. pedestrians and cyclists), reaching a balance between delays and safety of all road users is a challenging task. In this thesis, different approaches are presented to address potential improvements on traffic operations and safety of intersections serving more than one mode of transportation.
The impact of tunnels on the pedestrian operations and the effect of applying different signal timing plans on the performance of an isolated intersection are being studied. A methodology is proposed to reach a desired compromise between the safety and efficiency of either an isolated intersection or a corridor of independent/coordinated intersections. An integrated delay-safety (DS) indicator is used in combination with a neural network based tool. The proposed methodology was applied to a real-world urban arterial in downtown Montreal, along which a bicycle path was recently built. The study area was evaluated using VISSIM, a microscopic traffic simulator, by coding traffic signal timing plans along the arterial to perform independently, or coordinated. The objective is to advance with minimum delay a specified transportation mode (i.e. automobiles or bicycles). A Multi-Layer Perceptron (MLP) neural-network was built to identify what type of signal timing plan yields the best tradeoff between automobile delay and safety of non-motorized users. Based on traffic data collected from real-world and from simulations, a large date set of input/output pairs was used to train and test the MLP neural network. It was found that for 99.8% of the tested cases the neural network identifies correctly the configuration of signal timing plan that yields the optimal DS value.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Rouhieh, Behzad
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Civil Engineering
Date:9 April 2010
Thesis Supervisor(s):Alecsandru, Ciprian
ID Code:6737
Deposited On:23 Jun 2010 18:44
Last Modified:18 Jan 2018 17:29
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