Assessment of Urban Transportation Networks by integrating Transportation Planning and Operational Methods /Tools Presentation by: Sabbir Saiyed, P.Eng. Program Manager, York Region & Dr. J. A. Stewart Dean, Engineering, RMC I am Sabbir Saiyed, Program Manager at Regional Municipality of York, Canada. My co author is Dr. Allen Stewart, Dean of Engineering at Royal Military College of Canada. Unfortunately, he is unable to attend this conference. I will be making a presentation on Assessment of Urban Transportation Networks by integrating transportation planning and operational methods and tools. 12th TRB Transportation Planning Application Conference Houston, Texas

Overview Introduction Study area Regional travel demand forecasting model Transportation networks and data Types of signal control Experimental methodology Experimental results and discussions Conclusions This is an overview of my presentation. I will introduce the topic and provide background information on Regional travel demand forecasting model. I will briefly discuss transportation networks and data and types of signal control. I will discuss experimental methodology that I followed in conducting the analysis. I will present and discuss experimental result and finally conclusions.

Introduction Transportation – vital service Traffic congestion Budget constraints Performance of traffic systems Integration – planning/operational analysis Use of micro-simulation Transportation systems provide vital service to our communities by moving people and goods. As we all know, traffic congestion is a big issue in most cities and towns. Several municipalities or local governments do not have sufficient funds to meet growing travel demands. The operation of traffic systems is an important concern for elected officials and transportation professionals. The emphasis is to improve performance of traffic systems. One of the solutions is to improve performance by integrating planning and operational analysis. This presentation describes the process of integrating Regional travel demand model with micro-simulation models.

Study area is part of Greater Toronto Area…. The study area is a Region of Peel - part of Greater Toronto Area in Canada and covers City of Toronto and adjacent Regional Municipalities. The study area is 1 ½ hour drive from Buffalo, New York. Total population of Greater Toronto Area is more than 5 million.

Structure of Regional Model Trip Generation External Trips Airport Trips Trip Distribution Apply Growth Factors Apply Growth Factors Modal Split The structure of Regional Model is typical four stage model – consisting of: Trip generation Trip distribution Model split Trip assignment Trip distribution is carried out using Gravity Model. Growth factors are used for external and airport trips. Modal split uses aggregate logit model and policy modal shares. The model simulates morning peak hour auto and transit trips. Auto Occupancy Trip Assignment

Transportation Network and Data Transportation network - auto and transit Transportation data source: Cordon count Turning movement counts O-D survey Data issues especially for micro-simulation Transportation network consists of auto and transit network of entire Greater Toronto Area. Transportation data source consists of: Cordon Count Turning movement counts O-D Survey Cordon Count is conducted every two years. The program involves counting of vehicles and occupancies. Turning movements are counted at selected intersections every two or four years. I will discuss O-D survey in next slide. Not all intersections or links are counted consistently. Therefore, the data needs to be adjusted accordingly for micro-simulation.

Transportation Tomorrow Survey (TTS) Largest O-D survey in Canada Partnership between 21 municipalities, transit agencies and Province Collected every 5-year Household trip data Geocoding Correlation with census data Transportation Tomorrow Survey is the largest O-D Survey in Canada and one of the largest in North America. It is a partnership between 21 Municipalities, transit agencies and the Province. Survey is conducted every 5 years since 1986. Most recently it was conducted in 2006 and planning has begun for 2011. In the survey, randomly selected households are interviewed by telephone and trip data is collected for persons 11 years or older. Data is geocoded and is available for input into Emme/2 and other models. Survey data is correlated with Census data as well.

Types of Signal Control There are three types of signal control Pre-timed Actuated Adaptive Performance of traffic signal Cycle length Offsets Phases Transportation network analysis has been conducted for three types of signal controls In this experiment, I have considered three types of signal controls: For pre-timed signal controls – there are fixed signal timing plans The actuated signal controls – operate on traffic demands based on actuation of vehicles and pedestrians. For adaptive signal controls – there are no preset plans – new signal timing plans are computed dynamically based on traffic conditions. Traffic engineers can maximize performance of traffic signals by changing cycle lengths, offsets or phases.

Less traffic on side street yet Main Street traffic facing red signal This is a common situation on most arterial roads: There is heavy traffic on main street and less traffic on side street. Less traffic on side street yet Main Street traffic facing red signal

Transportation Software Packages Emme/2 TRANSCAD Synchro Sim-Traffic INTEGRATION Transportation software packages used in the study are: Emme/2 TRANSCAD Synchro Sim-traffic INTEGRATION Transportation network was used to created using Emme/2 and TRANSCAD software. Synchro and Sim-traffic has been used to model pre-timed and actuated signal control INTEGRATION has bee used to simulate traffic adaptive control. When you use 5 different software, there is a lot of data – I call it Data Tsunami and one has to be careful in comparing the data. Data Tsunami

Developed by late Dr. M. Van Aerde Mesoscopic model INTEGRATION Model Developed by late Dr. M. Van Aerde Mesoscopic model Dynamic traffic assignment Vehicle probe Uses O-D data INTEGRATION model has been used to asses performance of traffic adaptive control You may know about other models, but it is possible you may not know about INTEGRATION. Therefore, I have included this slide: INTGRATION was developed by Dr. M. Van Aerde, Professor of Virginia Tech University. It is a mesoscopic model and uses dynamic traffic assignment in addition to static assignments. It also provides vehicle probe for individual analysis. Finally it requires O-D data.

Experimental Design Real transportation network Experiment conducted in stages Small network (9 intersections) Medium network (over 125 intersections) Downtown network (medium size city) Current focus on medium network The transportation network considered in this experiment are all real transportation network. Experiment has been conducted in three stages: Small network (3x3 arterial grid of 9 intersections) Medium network (10x10 arterial grid of over 125 intersections) Downtown network (Medium size city) Current focus is on medium network.

Arterial Network Large arterial network Over 125 intersections Several unsignalized intersections Congested conditions during peak periods The arterial network consists of large network covering over 125 intersections. There are several unsignalized intersections. I must say that congested traffic conditions exist during peak periods.

Experimental Methodology Development of auto and transit network Extraction of O-D matrix from TTS Survey O-D matrix estimation Trip assignments Data validation Generation of turning movements Data files for micro-simulation The arterial network was extracted from Regional Model. O-D matrix was extracted from TTS Survey. This O-D matrix was adjusted using cordon count data and actual turning movements using TRANSCAD procedures. Trip assignment was carried out using new O-D matrix. Data validation exercise was carried out and new Turning movements were generated. Data files were prepared for micro-simulation.

Experimental Methodology Process This is entire experimental methodology process that I followed for this experiment. I don’t expect you to read this; however the point is that I have followed a systematic method for the entire experiment.

Optimization improves signal delays This slide shows results of the optimization process for the entire network for three types of signal control: Pre-timed Actuated Traffic adaptive Total signal delays are shown for scenarios before optimization, cycle optimization, offsets optimization and cycle/offsets optimization.

Total number of stops are lower for traffic adaptive signal control This slide shows total number of stops for: Pre-timed Actuated Traffic adaptive As you see here, total number of stops are lower for traffic adaptive signal controls after cycle/offsets optimization.

Shorter cycle lengths produces better results This slide shows that shorter cycle lengths i.e. between 70 sec to 100 sec produces better results.

Actuated signal shows similar results; however delays are lower than pre-timed signal This slide shows similar results for actuated signal controls. Results are similar to pre-timed signal controls but with lower delays.

Signal delays are lower at lower mid-block traffic This slide shows the effect of mid-block percentages on total stops, fuel and delays. Signal delays are lower at lower mid-block traffic.

Actuated signal shows similar results; however delays are lower than pre-timed signal This slide shows results for actuated signal controls at various percentage of mid-block traffic.

Conclusions The experiment demonstrates that Synchro, Sim-Traffic and INTEGRATION could be used to analyze three types of traffic signal controls Optimization improves the performance of the arterial network Shorter cycle lengths produces better results compared to longer cycle lengths. Actuated signal addresses demands of mid-block traffic better than pre-timed signal The experiment demonstrates that transportation planning/operational tools can be integrated for effective micro-simulation exercise. The experiment demonstrates that optimization improves performance of arterial network. Shorter cycle lengths produces better results compared to longer cycle lengths. Actuated signal control provides better results to address demands of mid-block traffic.

Thank you