Scientific Journal of Impact Factor (SJIF): 5.71 International Journal of Advance Engineering and Research Development Volume 5, Issue 02, February -2018 e-issn (O): 2348-4470 p-issn (P): 2348-6406 Characterization of Parameters to Mitigate Urban Traffic Congestion in Developing Countries A Case Study of Peshawar Pakistan. Manzoor Elahi 1, Rawid Khan 1, Anwar Ali 2, M Tariq Khan 1, Bashir Alam 1 1 Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan 2 Worley parsons Group Abstract: The operational quality of an urban road system in Pakistan steadily worsens due to increase in traffic volumes and an expectation of higher level of service (LOS). A well-planned, efficient and far-sighted scheme is required to guarantee a safe and smooth operational condition of road system at all times. This study aims to identify the optimization of various parameters such as road geometry, driver behavior, vehicle dimension, vehicle condition and pedestrian to mitigate urban traffic congestion in Peshawar. The aim of this research work is to study the current traffic condition and to model it in a micro simulation software PTV VISSIM.Then appropriately performing calibration and validation of the simulated model for accurate results. To achieve this goal, survey is conducted for micro simulation regarding classified traffic volume, vehicle specifications like its length and width, travel time data, and road geometric data of the research area. Then all the required data is compiled in VISSIM and simulation is run. But initially the results does not replicate the actual field conditions which was checked by statistical approach like paired T-test and Box plot comparison. Then a technique of calibration was performed and then model is validated as well, which gives accurate results for further analysis. Keywords: Urban Traffic, VISSIM, microsimulation, Paired t-testcalibration and validation of model travel time, Box plot. 1. Introduction The rapid increase of urbanization has influenced different sectors of the country amongst which the transportation sector is in the eminence due to urban vehicular growth and this effect is felt in terms of traffic congestion, delays, road safety, pollution, and transport efficiency. When compared to the rural areas the magnitude and nature of traffic issues of urban region are extremely different. Therefore urban regions are always lying at the center of the research domain of traffic engineering. Traffic flow in Peshawar urban roads is very exciting to be studied because of two main causes. First of all the traffic is highly heterogeneous having a blend of different types of vehicles like small-cars sedan- cars, pickups etc. and having high movement and heavy vehicles like trucks and buses. Secondly due to absence of proper lane discipline it leads to a blend of several issues like enforcement and education. During the rush hours the vehicles tend to take any lateral position along the width of roadway if they found some space which results almost in a diamond shaped queue. Traffic congestion results in time wastage, energy consumption, increased pollution and stress, the productivity is reduced and forces cost on people [2]. In Jordan median openings at various sections were chosen for study. It relates the capacity of the U-turns using the regression equations with the conflictingtraffic and total average delay. The result reveals that the gap of median opening are influenced by the total average delay and the speed of conflicting traffic flow. In this study at U-turns and intersections the travel time of vehicles were not determined [5]. Statistical approach used for evaluating thecapacity, delay and travel time for U-turn movements concluded simple equations / analysis may not be not correctly evaluate U- turning operational effects of large vehicles of the roadway [6,7]. VISSIM, CORISM or S-Paramics etc. are various traffic simulation software to address such limitations, for the evaluation of traffic flow capacity in developed countries [8]. VISSIM as microsimulation tool was applied in North America and Europe for the analysis of traffic congestion on freeway [9]. VISSIM have been used for modeling link travel times and link performance function based macroscopic models for comparing travel times [10]. 2. Methodology The GT road in Peshawar was selected as the pilot scale project. Initial inspection of the research site was done and it is initiated that total length of the section of G.T road stretches from Fort Balla hisar to Peer Zakori Bridge is 4.9 km. Main traffic flows through this corridor especially in morning and afternoon peak times due to the direct access to Business areas, educational institutes, Business areas and Motorway. Site data for road geometry, vehicles specification, percent turning vehicles and average spot speeds was collected. Details procedure is described in the following paragraphs. 2.1 Traffic volume: @IJAERD-2018, All rights Reserved 866
Classified traffic volume data was collected on Tuesday, Wednesday and Thursday.The timing of data counting was morning, afternoon and evening peak hours of the day. 2.2 Travel time: Using the standard traffic engineering techniques travel time survey was conducted on the same days and timing as done for traffic volume counting. During the survey the test vehicle technique was adopted [11] and the relevant parameters of urbandensity, type of facility and time elements were measured.. 2.3 Geometry of the road: The GT road varies at various segments thus for each segment the number of lanes, cross-section width, median width and length was determined. In the study area the selected road comprises total of six lanes, with three lanes in each direction. In addition to that a service lane in some regions on each side is also provided. 2.4 Vehicle specification survey: To accurately replicate the flow on GT road through VISSIM simulation model, the chacterisitics of local thevehicles weredetermined as shown in figure 1. 2.5 Preparation of the Model: The very first step of modelling is the base data preparation. VISSIM necessities input of base data before making a network for project area. The conceptual framework of VISSIM model is shown in Figure 2. During the travel time survey acceleration and decelerations values of vehicle types were measured and using the average values the statistical distributions were made. The model incorporates driving behavior parameter values like average standstill distance, minimum safety distance and lateral distance.figures 3illustrate some of the driver behavior parameters. Figure 1: Classes of vehicles used Define simulation parameters Link &connectors Desired speed distributions Desired acceleration and deceleration Driving behavior Minimum safety distance Min and max lateral gap Traffic volume and composition Vehicle dimensions Conflict areas Priority rules If error is within 5% Calibrated/Validated Simulation Results Output volume/travel time Comparison with field Volume/Travel time Figure 2: Conceptual framework of the model @IJAERD-2018, All rights Reserved 867
Figure 3: Description of driver behavior data collection illustration 3. Run the simulation In this step VISSIM will give the result in the form of video as in figure 4, from which the transportation engineering experts can have judgement of the site conditions without physically inspecting the site. Figure 4Traffic flow condition near Firdose at 9 A.M. 4.Calibration and validation procedure To address the unique characteristics of heterogeneous traffic flow calibration of microsimulation requires special procedures and steps. 4.1. Simulation Model Setup This is the very first step which contains selection of project site, determination measure of effectiveness i.e. (MOE), collection of data and system coding. The GT road from suri pul to peer zakori bridge network was coded into the VISSIM and traffic data were inputted to the model. The percentage of average traffic flow for overall network of different vehicle types under consideration is shown in table 4.1. Percentage of Modes vehicles Motorcycle 15.20 Car / Jeep 11.91 Taxi 15.83 Rickshaw 17.45 Suzuki Pickup 12.27 Wagon 11.20 Mazda Bus 9.15 Large Bus 6.98 Table 4. 1 Types of vehicle and their relative proportion on GT road. @IJAERD-2018, All rights Reserved 868
4.2. Preliminary Evaluation This step concludes whether default parameters of vissim on which the model is run represents the local field conditions or not. The resulting average travel time of filed and VISSIM are given in table 1.The box plot comparison of different vehicle types in figure 4 reveals that the model is not calibrated and it needs appropriate calibration. Figure 4 B0x plot comparison of un calibrated simulated Travel time and filed Travel time of different vehicle types. 4.3 Initial Calibration The step from where the actual model calibration start is initial calibration which consists of three main stages (1) Identification of calibration parameters and their acceptable ranges (2) Generation of reasonable number of parameter sets using statistical experimental design, and (3) Implementation of multiple runs with each parameter set. The initial calibration parameter and their ranges from the literature are nine in number but Literature reveals that the CC0 : average standstill distance ( 1.5 meters default value) and CC1: headway at a certain speed (0.9 secondsdefault value) parameters are very sensitive for simulation results but others parameters are not very effective to influence the capacity of the simulated section of the road network [4].In heterogeneous traffic flow conditions theses parameter values will not be the same as default values of these parameters. Solver was used get the most optimized values of theses parameters. The travel time was fixed and the optimized values of CCO and CC1 were obtained.then the same procedure was followed for the rest of vehicle and their appropriate values were obtained as in table 4.2. Vissim Simulated Car type Field volume (veh/hr) volume (veh/hr) CC0 CC1 Bus 1734 1458 4.2 1.7 Taxi 3694 3468 1.6 1.14 Rickshaw 4782 4213 1.7 1.13 Motor bike 10240 9876 1.3 0.3 Table 4. 2 VISSIM parameters based on homogenous simulated results Then, average weighted method is used to obtain its values for heterogeneous traffic flow. CCO heterogeneous=cc0 car x P car + CC0 taxi x P taxi + CC0 rickshaw x P rickshaw + CC0 motor bike x P motor bike. CCO heterogeneous= 1.13 m Similarly for CC1 CC1 heterogeneous= 0.58 s 4.4 Feasibility Test In this step the model and field results are checked using various statistical approaches i.e. Histogram, Box plot or paired t test [14]. @IJAERD-2018, All rights Reserved 869
4.5 Evaluation of the Parameter Set Outputs based on 100 simulation runs of calibrated simulation model is compared with field data, and the model is tested using paired t- test as shown in table4.6.the result shows that the model is calibrated. Vehicle type Mean of the differences Standard deviation of the differences number of observations Degrees of freedom ( n- 1) test-statistics Critical values of the t- distribution (two-tail), alpha=0.05 P- value (twotail) Rickshaw 0.05091 4.453425 8 7 0.0323-2.145, 2.145 0.975 Wagon -4.6323 4.900561 8 7 0.6274-2.145, 2.145 0.318 Taxi 2.25039 3.170104 8 7 2.0078-2.145, 2.145 0.085 Motor bike 2.72346 3.46921 8 7 1.9672-2.145, 2.145 0.092 Table 4. 2 Paired t-test of travel time for different vehicle types. 4.6 Validation of the model The final step of is actually the validation of the model in which the untried conditions are checked for the Calibrated parameter set. Thus the model is validated as well. Finally the model should be tested for visualization to better replicate the field scenario [3]. Figure 4.5 Traffic flow condition near Firdose at 9 A.M 5. Conclusions From the analysis of the data, it was found that: The parameters Average Standstill Distance and Headway Speed were found to be the most crucial with regard to calibration of the model. Simulation result shows Firdose section and Govt college Peshawar chowk section to be the most congested during the peak hours which is quite in agreement with the ground reality. Presence of conflict points was main factor that contributed to Queue Delay due to merging and diverging traffic points and U-TURNS. Auto rickshaw model was incorporated in the research for the first time, to create a more realistic simulation model for Peshawar. The traffic framework of GT road Peshawar can be precisely modeled in PTV VISSIM. Driving behavior was one of main contributing factor for the delay which ultimately results in an increased travel time on GT road. To reduce queue delay, conflict points need to be minimized. This could be achieved by reducing the number of access/exit points along the arterial road (GT road) especially near critical sections. @IJAERD-2018, All rights Reserved 870
References: [1].Maryam Akbar et.all, Methodology for Simulating Heterogeneous Traffic Flow at Intercity Roads in Developing Countries: A Case Study of University Road in Peshawar, Arabian Journal for Science and Engineering. [2]. Amudapuram Mohan Rao, Kalaga Ramachandra Rao, Measuring Urban Traffic Congestion A Review, International Journal for Tra-c and Transport Engineering, 2012, 2(4): 286 305. [3]. Byungkyu (Brian) Park and Hongtu (Maggie) Qi, Microscopic Simulation Model Calibration and Validation for Freeway Work Zone Network A Case Study of VISSIM,Conference, Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE.Febraury 2006. [4]. Arpan Mehar, Satish Chandra and S. Velmurugan, Highway Capacity Through Vissim Calibrated for Mixed Traffic Conditions,KSCE Journal of Civil Engineering (2014) 18(2):639-645. [5]. Hashem R. Al-Masaeid, Capacity of U-turn at median opening, Institute of Transportation Engineers,(ITE) Journal 69(6), 28-34 (1999). [6]. Lasmini Ambarwatia, Adam J.Pelb, Robert Verhaeghec, Bart Van Aremd, Empirical Analysis of Heterogeneous Traffic Flow, Proceedings of the Eastern Asia Society for Transportation Studies, (2013) 9, 11-17. [7]. Liu, Pan, "Evaluation of the operational effects of U-turn movement, University of South Florida, USA (2006). [8]. Rakha, H., et al. Systematic verification, validation and calibration of traffic simulation models. 75th Annual Meeting of the TRB, Washington, DC (1996). [9]. Tony Woody, calibrating freeway simulation models in VISSIM. Final Research Report University of Washington, Seattle, WA (2006). [10]. V. Thamizh Arasan, Shriniwas S. Arkatkar, Microsimulation study of vehicular interactions in heterogeneous traffic flow on intercity roads, European Transport, (2011), 48, 60-86. [11]. Topic No. 750-020-007 Travel Time and Delay Study (Manual on Uniform Traffic Studies). @IJAERD-2018, All rights Reserved 871