INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 5, No 2, 2014 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN 0976 4399 The impacts of traffic signal timings optimization on reducing vehicle emissions and fuel consumption by Aimsun and Synchro software's (Case study: Tehran intersections) Ali Mansour Khaki 1, Pegah jafari Haghighat Pour 2 1-Associate Professor, Department of Civil Engineering, University of Science and Technology, Tehran, Iran. 2- Ph.D Candidate in Transportation of Tehran Payame Noor University, Tehran, Iran st_p_jafari@azad.ac.ir doi: 10.6088/ijcser.2014050014 ABSTRACT Considering the fact that in recent years the transport sector allocated considerable fuel consumption and emissions, evaluating issues related to fuel consumption and air pollution is one of the most important issues of transport sector. Estimate of fuel consumption models and emissions are new issues that discussions of various models have been published in Iran and different countries. But assessing the impact of traffic signal timing optimization on fuel consumption and emissions is one of the new and important subjects that have significant effect on emissions and economics. Therefore, 5 Intersections of Tehran in 5 different areas selected for the case study and studied period in traffic and optimization to address problems related to emissions were in 2013 in this thesis. Traffic data and parameters that related to emissions and fuel consumption is obtained as ADT in days of October in studied year. After optimization, the validity and accuracy of project have been used by Aimsun software and it was observed that traffic signal timing optimization have significant reduction in emissions and fuel consumption. The considerable results of this research can be cited to Synchro software application in traffic signal timing optimization. For example, after optimization fuel consumption has been decreased about 0.25%, 0.17% in AM peak time and, 3.63%, 2.46%, in PM peak time at Mahalati-10 farvardin, Shariati-Dolat intersections respectively. Keywords: Intersections, optimization, reduction in vehicle emissions and fuel consumption, traffic signal timings. 1. Introduction The transportation sector in Iran accounts for about 35% of total energy consumption and nearly 70% of total oil consumption (Energy and Economic magazine). Heavy reliance on fossil fuels might have accelerated climate change, and threatened energy security and public health. Ever increasing concerns on energy and emissions have led the transportation sector to mitigate the adverse impacts on vehicle emissions and fuel consumption. It has been widely accepted that improving traffic flow has been one of the strategies to reduce vehicle emissions and fuel consumption. In urban areas, frequent stop-and-go driving and excessive speed variations contribute to higher fuel consumption and emissions. While traffic signal timing optimization can reduce the number of stops and maintain moderate vehicle speed, little is known of the impact of direct optimization for minimizing fuel consumption or emissions, especially using a transportation planning-level microscopic traffic simulator. Today, improving the flow of traffic is acceptable as one of the effective Received on September, 2014 Published on November 2014 144
methods of reducing emissions and fuel consumption. Therefore other methods, such as traffic management, timing optimization is important to reduce delays. High volume of stopped vehicles without engine operation cause to waste large amount of fuel and emissions. So traffic signal timing optimization by using and optimization software at the microscopic level transportation planning can make a direct impact to minimize fuel consumption and emissions.the objective of this article is to quantify the impact of traffic signal timing optimization on fuel consumption and emissions. The proposed method consists of the combination as traffic, timing optimization using the related software. Also comparison of different software to select the best optimization method is done. 2. Literature review L.Adacher has been provided the article, a global optimization approach to solve the traffic signal synchronization problem. In this study investigates the Traffic Signal Synchronization is a traffic engineering technique of matching the green light times for a series of intersections to enable the maximum number of vehicles to pass through, thereby reducing stops and delays experienced by motorists. Synchronizing traffic signals ensures a better flow of traffic and minimizes gas consumption and pollutant emissions. The objective function used in this work is a weighted sum of the delays caused by the signalized intersections. In this paper, they apply generalized 'surrogate problem' methodology that is based on an online control scheme which transforms the problem into a 'surrogate' continuous optimization problem and proceeds to solve the latter using standard gradient-based approaches while simultaneously updating both actual and surrogate system states. They extend a `surrogate problem' approach that is developed for a class of stochastic discrete optimization problems so as to tackle the traffic signal synchronization problem to minimize the total delay.numerical experiments conducted on a test and a real networks show that the surrogate method converges in a very small area (Adacher L, 2012). Li Jie, et al have been provided the article, Calibration of a microscopic model for emission calculation. In this article they indicated that Emissions by road traffic can be reduced by optimizing traffic control. The impact of this optimization on emission can be analyses by. The programs used for this analysis should be valid with respect to the traffic characteristics that determine the emissions. Thus calibration of the parameters is a prerequisite. In most cases, volumes, travel times and queues are used to calibrate models, rather than detailed driving characteristics such as speed and acceleration patterns. However, these driving behavior parameters determine the vehicular emissions to a great extent. A study was carried out in which the driving behavior parameters in a microscopic model (VISSIM) were calibrated using real trajectories collected by image processing at an intersection in Rotterdam. The sensitivity of the results for driving behavior parameters was investigated. The most influential parameters were identified and adjusted to ensure that the results were consistent with the observed traffic and could provide valid estimations of the total production of emissions (Henk Van Zuylen, 2012). 3. Methodology Several factors affect the traffic signal timing, fuel consumption and emissions at intersections. These parameters are increasing and decreasing of vehicle speed, the input vehicles volume, type of traffic signal phasing, type of lane, effective green time, cycle length and etc. International Journal of Civil and Structural Engineering 145
Traffic signal timing have optimized due to Iran standards after identifying the main factors affecting traffic signal timing and considering the software. Intersection s information related to traffic signal has been obtained from comprehensive traffic company. finally a comparison have done between the software and the appropriate software in terms of time, speed and accessibility that could reduce emission and fuel consumption well.5 intersections for case study are Golbarg-Dardasht, Kamali-Karegar, Mahalati-10farvardin, Shariati-Dolat, Zibadasht-Dehkadeh. Research process are presented in the following graph. 4. Case study Figure 1: Study methodology 5 intersections from Tehran city in 5 different areas consist of north, south, center, east, west have selected for giving conclusions and case study. We just introduce some information of 2 intersections because the traffic information and emission parameters for 5 intersections are enormous and cannot propose in this study. As noted, equivalent coefficients accordance with urban road regulations is used to convert public transport and motorcycles volumes to passenger vehicles. 4.1 Traffic information of Shariati-Dolat intersection (north Area) This intersection is shown in Figure 1.at this intersection Dolat Street is sideway and from East to West has 3 lanes and also from north to south and south to north has 3 lanes. Emamzadeh Street as two-lane Minor Street enters to intersection. Intersection angle is 90 degrees. And has an intelligent 2 phase signal traffic. It also has separate turn right and turn left from East to west. Cycle length in AM peak and PM peak time are 80 and 69 seconds respectively. Traffic volume in AM peak time in main road is 2202vehicles and 1993 vehicles in lateral direction and 2128 vehicles in main road and 1502 vehicles in PM peak hours along the lateral direction in 2013. 5.2 Second with Aimsun traffic simulator The proposed output in table 1 and 2 were obtain after optimizing and putting the traffic information into Aimsun simulator software by second in compare with first. As observed in tables with decreasing in cycle length, delay and traveled time, fuel consumption and emissions have been decreased and traffic flow, average speed increase. It is noteworthy that the estimated fuel consumption or emissions should be carried International Journal of Civil and Structural Engineering 146
out according to the amount of traffic flow. It can be seen that the overall amount of delay, fuel consumption and emissions have been effectively reduced with little accuracy in results of Secondary and compare them with traffic flow. Figure 2: Shariati-Dolat Intersection 4.3 Traffic information of Mahalati-10 farvardin intersection (south area) This intersection indicates in fig 2.at this intersection Mahalti Street from East to West has 5 lanes and 10 farvardin streets from north to south and south to north has 4 lanes. Intersection angle is not 90 degrees. And has an intelligent 3 phase signal traffic. It also has separate turn in 4 directions. Cycle length in AM peak and PM peak time are 85 and 98 seconds respectively. Traffic volume in AM peak time in main road is 2936 vehicles and 1516 vehicles in lateral direction and 3628 vehicles in main road and 1414 vehicles in PM peak hours along the lateral direction in 2013. 4.4. Simulation and optimization results Figure 3: Mahalati-10 farvardin Intersection 4.4.1First with Aimsun traffic simulator 5 intersections studied geometrical plan is the first and most important step to enter data into the Aimsun software. In this section the background of geometric design in AutoCAD format or the images from Google earth or Google maps put into the Aimsun software as layout before drawing the intersections plan. International Journal of Civil and Structural Engineering 147
4.4.2 Optimization with synchro software After the initial and obtain outputs such as delay time, traveled distance, traffic flow, vehicle average speed, travel time, fuel consumption and emissions like HC, CO, NOX by Aimsun software. Signal timing optimization is done by Synchro software.overall the rate of cycle length about 27.05,10 % have been decreased at Mahalti-10 farvardin and Shariati- Dolat intersections than previous case before optimization in AM peak time and also 41.83,31.88 % have been decreased in PM peak time respectively. Figure 4: Geometric plan of Mahalti-10 farvardin Intersection Figure 5: Geometric plan of Shariati-Dolat Intersection Figure 6: Cycle length and Geometric plan of Mahalti-10 farvardin Intersection before optimization in AM peak time by Synchro software International Journal of Civil and Structural Engineering 148
Figure 7: Cycle length and Geometric plan of Shariati-Dolat Intersection before optimization in AM peak time by Synchro software Figure 8: Cycle length and Geometric plan of Mahalti-10 farvardin Intersection before optimization in PM peak time by Synchro software Figure 9: Cycle length and Geometric plan of Shariati-Dolat Intersection before optimization in PM peak time by Synchro software International Journal of Civil and Structural Engineering 149
Figure 10: Cycle length and Geometric plan of Mahalti-10 farvardin Intersection after optimization in AM peak time by Synchro software Figure 11: Cycle length and Geometric plan of Shariati-Dolat Intersection after optimization in AM peak time by Synchro software By examining changes in tables and considering traffic flow, it can be seen that the amount of delay, fuel consumption, emissions such as HC, CO, NOX and travel time have been decreased with improving cycle length and the rate of traffic flow, vehicles average speed increased in both AM and PM peak hours at each intersection. Thus providing accurate traffic signal timing optimization algorithms at intersections in Synchro software and observing changes in Aimsun traffic simulator could provide significant reduction in fuel consumption and emissions.after second in Aimsun software the percent of flow /capacity in AM peak time are represent as sample in figure 13 and 14. Figure 12: Cycle length and Geometric plan of Mahalti-10 farvardin Intersection after optimization in PM peak time by Synchro software International Journal of Civil and Structural Engineering 150
Figure 13: Cycle length and Geometric plan of Shariati-Dolat Intersection after optimization in PM peak time by Synchro software Table 1: Output from second, compared with the first at Mahalati-10 farvardin intersection by Aimsun software Current case unit AM peak time in first AM Peak time in second PM peak time in first PM Peak time in second Delay time Traffic flow Average speed Travel time s/k m Veh /hr Km /hr s/k m 57 55.7 68.6 61.6 4614 4677 5050 5980 35.6 35.7 33.7 34.6 124 122.6 135.2 128.4 Traveled distance Fuel consumption km 1573 1562 1767.6 1741.8 liter 198 197.5 225.4 217.2 CO Kg 22.4 22.3 26.2 25.1 International Journal of Civil and Structural Engineering 151
HC Kg 1.75 1.2 2.1 1.9 NO Kg 0.36 0.30 0.42 0.39 Table 2: Output from second, compared with the first at Shariati-Dolat intersection by Aimsun software. AM AM Peak PM Peak PM peak Current uni peak time in time in time in time in first case t first second second Delay s/k time m 74.6 71.9 52.4 39.7 Traffic Ve flow h/hr 4332 4406 3619 3643 Average Km 31 31 35.9 37.2 speed Travel time Traveled distance Fuel consumption /hr s/k m 142.9 140.4 120.7 108.1 km 1032.6 1050.9 850.1 847.2 lite r 200.90 200.24 133.9 130.6 CO Kg 20.2 19.1 13.7 12.8 HC Kg 1.5 1.38 1 0.91 NO Kg 0.39 0.19 0.25 0.13 Figure 14: The percent of flow/capacity in AM peak time at Mahalati-10 farvardin intersection International Journal of Civil and Structural Engineering 152
Figure 15: The percent of flow/capacity in AM peak time at Shariati-Dolat intersection 5. Conclusions The review of previous studies in Iran and other countries indicated that extensive research on traffic signal timing optimization to reduce delay time, total travel time and queue length have done. However, few of these studies regarding the traffic signal timing optimization on reducing fuel consumption and emissions. As was pointed out during the study, traffic signal timing optimization and can influence to reduce fuel consumption and emissions, such HC, CO, NOX. So 5 intersections as case study have selected in this article. Traffic information, geometric design and traffic signal timing enter to Aimsun software as the first step for simulating. After primary output such as delays, traffic flow, traveled time, traveled distance, vehicles average speed,fuel consumption and emissions have acheived.then optimization has done with synchro and to observe changes, second considered in this study. For sample percentage changes of Mahalati-10 farvardin intersection are given in table 3. Table 3: percentage changes of Mahalati-10 farvardin intersection Current case unit Percentage changes in AM peak time percentage in changes PM peak time Delay time s/km -2.28% -10.20% Traffic flow Average speed Veh/ hr Km/ hr +1.36% +18.41% +0.28% +2.67% Travel time s/km -1.12% -5.02% Traveled distance Fuel consumption km -0.69% -1.45% liter -0.25% -3.63% International Journal of Civil and Structural Engineering 153
CO Kg -0.44% -4.19% HC Kg -31.42% -9.52% NO Kg -16.66% -7.14% Acknowledgement This research was taken from Pegah Jafari Haghighatpour thesis and supported by Department of Engineering, Islamic Azad University South Tehran Branch of Tehran, Iran. The authors are grateful to Tehran Traffic Control Company and Dr Ali Mansourkhaki of Civil Engineering department, University of Science and Technology for his help with this thesis. 6. References 1. Energy and Economic magazine, International number: 1123-15632012, Tehran. Iran. 2. Adacher L., (2012), A global optimization approach to solve the traffic signal synchronization problem, Procedia - Social and Behavioral Sciences, Published by Elsevier Ltd. 54, pp 1270-1277. 3. Henk Van Zuylen, et al, (2012), Calibration of a microscopic model for emission calculation, Published by Elsevier Ltd, Transportation Research Part C, 31 pp 172 184. 4. Tehran traffic control company, October of 2013. 5. Aimsun 6.0.5 Simulation traffic software. 6. Synchro 8, Optimization and traffic software. International Journal of Civil and Structural Engineering 154