Review of Data Collection Methods for Establishing the Capacity of Intercity Highway

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1 Review of Data Collection Methods for Establishing the Capacity of Intercity Highway S. Kalaanidhi a *, Dr. K. Gunasekaran b, M. Mohamed Badhrudeen c, Dr. S. Velmurugan d a Project Associate, Division of Transportation Engineering, Anna University, Chennai -25, India. b Associate Professor, Division of Transportation Engineering, Anna University, Chennai 25, India. c Project Officer, Division of Transportation Engineering, IIT Madras, Chennai 25, India. d Senior Principal Scientist, CRRI, New Delhi 25, India. Abstract The collection of high quality traffic data is one of the basic requirements for traffic studies. It is unquestionable that traffic data is the primary source for any kind of traffic and transportation projects and necessarily the data should be reliable and precise. The manual method of data collection involves deployment of huge number of man power and there are relatively more chances for errors in data collection. The technological advancements have brought out much advancement in the reign of traffic data collection. Available Automatic Data Collection (ADC) techniques are video graphic data collection, pneumatic tube based classified vehicle counter, infrared based vehicle recorder, radar based automatic traffic sensor and so on. The drawbacks reported on usage of various ADC techniques are high initial cost, maintenance requirements, time consuming initial calibrations, restrictions in terms of required road geometrics, varying performance under different weather conditions, difficulty in identifying the vehicle categories, etc. This paper attempts to review the performance of two types of traffic data collection techniques namely, video graphic method, and infrared traffic detector on Indian Highways. In this study, stretch on National Highway 45 near Acharapakkam (100 Km south of Chennai city) was chosen. Video graphic survey was carried out from a height of 12 meter above ground level with the help of a camera mounted on pneumatic mast to capture a minimum trap length of 75 meter in one direction of the stretch. Then the data extraction was done using software developed for classified volume count and speed measurement. Besides, an infrared based traffic recorder was placed to count the vehicles at the same stretch about 670 m away from camera location. The classified count and speed of the vehicles obtained from two methods were compared and traffic flow relationships were developed to estimate the capacity of the highway. In this paper the experience of using these two methods for data collection on Indian Highways is discussed The Authors. Published by Elsevier B. V. Selection and peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay. Keywords: Automatic Data Collection, Classified Volume Count, Highway Capacity 1. Introduction & Motivation Study of traffic flow characteristics requires precise count and speed measurements of vehicular movement. In early days, traffic data were collected manually. Manual method of data collection, many times, fails to satisfy data

2 requirements of advanced traffic analyses and requires huge manpower. With the aim of overcoming the consequences encountered in manual data collection, many developed and developing countries are employing innovative data collection techniques. Innovation of Intelligent Transportation Systems has brought the advantage of continuous data collection with acceptable level of accuracy. Highway Capacity is a recent research thrust in Traffic Engineering. Undeniably, the quality and amount of data required for capacity analysis is very large. Automatic data collection techniques are reported to be reliable for continuous, simple and accurate data collection. The advantages and disadvantages of such methods of data collection were addressed in earlier studies of Lopes el al., [1], Leduc [2], and Guy and Fricker [3]. Homogeneous traffic possesses less complication in data collections and in analysis (HCM, 1965) [4]. As the degree of heterogeneity increases, the complexity of data collection and analysis increases. The selection of particular data collection method is attributed to its suitability of usage and application. Evaluation of the suitability of two methods of data collection viz., Videography and Infrared based traffic counter (TIRTL), was attempted and the experience is reported in this paper. 2. Previous studies Data collection methods were reviewed by Hidas and Wagner [5] for the use of microscopic traffic simulation. The authors experimented three data collection methods viz., Videographic method, GPS instruments and instrumented vehicle fitted with SMS. The advantages and disadvantages of each method were discussed in the paper. In another study [6], the systems of data collection were evaluated on the basis of need, precision and best methods for collecting it. Different types of sensors are used for data collection and the problems in using them were discussed in the paper. Besides, this paper provides suggestions for effective data collection. Three kinds of traffic data collection were conducted by Tarefder and Brogan [7]. In this study, the data were collected by three methods, Automatic Traffic Recorders, Automatic Weight Recorder and ITS cameras, in order identify the current problems in data collection, and describe the possible opportunities. On based of the study results, recommendations for improving the processes were provided. Such as, in the case of suspicious or missing records, instead of recounting, engineering judgment was suggested. In a similar kind of study, carried out by Robichaud and Gordon [8], the accuracy of the data collection systems was compared. The comparison of accuracy of single long-term count with that of multiple short term counts were made and the results found that the multiple short term counts are accurate than the earlier. 3. Methods of Automatic Data Collection There are wide range of methods used across the world for traffic data collection based on its needs and applications. These methods are generally classified as three categories, Site based, Segment based and wide area based data collection [2]. Site based data collection techniques are the common kind of data collection technique. Inductive loops, Pneumatic tubes, Piezo Electric sensors, Microwave Radars, LIDAR/Infrared/Acoustic technologies, Video graphic techniques are some of the site based data collection techniques. Road based data collection techniques are generally used in developed countries, as it involve huge installation costs. License Plate readers, Transponders/Radio Frequency based detectors are the well-known road based data collection techniques. GPS and Airborne sensors are categorized as Wide area based data collection techniques. In this study, the following 2 site based data collection techniques were deployed and the results are discussed. 3.1 Video graphic Method

3 This technique is currently adopted in many of the developing countries as the cost incurred for this method is relatively low than all other methods and also the accuracy of the data can be ensured as the footage of data can be checked whenever needed. An appropriate vantage point for fixing camera has to be chosen for the best coverage of interested road stretch. The selected stretch is recorded for the required duration. Then the recorded video is processed either through some technologies, for example, image processing or by manual counting and sometimes quasi manual counting. This kind of approach holds the advantage of reducing the manpower to large extent and improves the accuracy of the data collection than the conventional method of manual data collection. 3.2 Infra-Red Based Data Collection Method It is a traffic surveillance system that works on the principle of intervention of Infra-Red beams. TIRTL [9] consists of 2 units, one is transmitter and another is receiver, which have to be placed at the edges of the carriageway so as the receiver unit receives the emitted infrared beams from trans mitter unit. It counts, classifies, determines the lanes and speed of passing vehicles using a novel light based technology. The beam overlap yields four different paths of light from the transmitter to the receiver lenses, two parallel beams and two crossed beams. As vehicles pass between the transmitter and receiver, each wheel interrupts each of the four beams. Breaking of these beams is known as beam event. The precise time detection of th e beam event allows the receiver to compute the exact velocity and lane of the vehicles as it passes. Based on the order in which the break events occur, the direction of the vehicles is detected. The classifications of vehicles are detected based on its axle width. In Indian traffic, TIRTL is capable of classifying the vehicles based on the following categorization. The categories are Bicycles, Cycle Rickshaws, 2 Wheelers, 3 Wheelers, Tractor, Tractor with trailers, SCV (2 axle Small Commercial Vehicles), LMV (2 axle Light Motor Vehicles), LCV (2 axle Light Commercial Vehicles), MCV (2 axle Rigid Truck or Bus), HCV (3 axle Rigid Truck or Bus), HCV (3 axle Articulated Truck or Bus), MAV (Rigid Truck), MAV (Articulated Truck) and OSV (Oversized Truck). Figure 1 represents the working principle of TIRTL. 4. Methodology The methodology adopted in this study involves selection of road stretch suitable for videography as well as TIRTL surveys, collection of data, preparation of data, data extraction and comparison of data obtained from the two methods mentioned. The total vehicular count per hour, classified hourly count and speed obtained from the two methods were compared. Each stage of the methodology was detailed below.

4 4.1 Study Stretch Fig. 1. Principle of TIRTL s vehicle detection and s peed estimation The National Highway 45 connects Chennai city with the southern part of Tamil Nadu. It is one of the two major highways which connect the southern parts of the state with the state s capital. It is a four lane divided highway with well-defined shoulder of 1.5 meter. It connects many towns and cities along its total length of 472 Km, namely Trichy, Madurai, Kanyakumari and so. A mid block section on this highway, 90 Km away from Chennai city (near Acharapakkam), was selected. The section is an ideal section free from side frictions, straight and on plain terrain (Figure 2). Fig. 2. Study Stretch 4.2 Instrumental Setup Video graphic camera was mounted on a pneumatic mast fitted in a vehicle. The midblock section was delineated with traffic cones for 75 m section. The camera was focused on the road from the height of about 12 m from the ground level whereas the TIRTL instrument was placed at the right-turn refuge of a minor uncontrolled intersection. The location of the TIRTL instrument and the vid eo camera is shown in Figure III. For safe positioning of the TIRTL units and to keep the units within the allowable camber difference of 15 cm, the refuge at the intersection was selected. The instrumental setup of video graphic survey and TIRTL survey are shown in the Figure IV and V. The snapshot of captured video is shown in Figure 6. One directional movement of vehicles was recorded. The timings of camera and TIRTL were synchronized prior to the start of survey.

5 4.3 Data Collection & Extraction Fig. 3. Instrumental setup at National Highway 45 Having carried out the initial settings, video graphic survey was conducted for 12 hours (7 AM to 7 PM) for the interest of a research project, Development of Indian Highway Capacity Manual, for which this survey was mainly aimed. Because of the battery limitations, TIRTL survey was deployed for 3 hours from 7:45 AM to 10:45 AM). The flow from and to the minor road was less than 20 two wheelers for the period of survey. The recorded video data was transferred to the hard disk and with the help of multimedia software, the trap lines were drawn in the video of both the sections. Then, the videos were imported to data extraction software developed specifically for video data extraction. Skilled enumerators were engaged to record the vehicle category, lane, time of entry and exit from the section with the aid of software. The video was run many times for extracting the count and speed of different type of vehicles lane wise. Whereas, the data obtained from TIRTL is tailor-made as it can be used for analysis directly. 4.4 Data Analysis & Comparison The data obtained from the two methods were analysed. The total volume of the vehicles, classified counts of vehicles, and speed of the vehicles obtained by both methods were compared as detailed below. a) Traffic Flow The total volume of the vehicles observed in both methods was compared hour wise (Table 1). The error in prediction of traffic count by TIRTL with respect to video graphic survey was found to be 2.56 %.

6 Fig. 4. Video camera mounted on vehicle using Pneumatic Mast Fig. 5. Receiver of Infrared based instrument placed on road side Fig. 6. Snapshot of captured video with section demarcation Table 1. Comparison of vehicle count obtained by Video graphic and TIRTL surveys b) Classified Vehicle Count Time Duration Flow (Veh) Difference From From % Video TIRTL 7:45 to 8: :45 to 9: :45 to 10: Average Error % Similarly, the accuracy of classified count was checked for the limited categories of vehicles, as TIRTL classifies the vehicle types with respect to the axle length and wheel base. In our traffic, the axle length of buses and heavy vehicles are almost same and thus it classifies the both types of vehicles to single category. Same is the case for small cars and 4 wheeled share autos. The difference in prediction of classified vehicle count is less than 5 % (Table 2) c) Speed Table 2. Comparison of Classified Vehicle Count of Video and TIRTL Vehicle Categories Flow (Veh) Difference in From From % Video TIRTL Two Wheelers Cars & Light Motor Vehicles Heavy Vehicles, Buses (including MAV)

7 The speed prediction of TIRTL was compared with that obtained from video records for four categories of vehicles (Table 3). The analysis reveals that the speed prediction of TIRTL is nearly 95 % accurate. However there were minor discrepancies experienced with TIRTL while predicting lower speeds. The cumulative distribution of the speed for selected categories of the vehicles was shown in Figure 7. Though two locations were nearly 670 meters apart as the TIRTL to ensure instrument safety was installed near a minor uncontrolled intersection with almost no cross flow. But the median opening would have a psychological impact on heavy vehicle (especially running at low speed) drivers and a minor reduction especially for HCV was observed. Hence the speeds of HCVs and Buses predicted by TIRTL were lesser than the speed obtained from the video records. It was understood that the difference in speed prediction of TIRTL and video was due to the above mentioned reason. The location of TIRTL was chosen near the intersection mainly due to the constraints in placing the equipment. Table 3. Comparison of different type of vehicle speed obtained from video graphic and TIRTL surveys Vehicle Type Two Wheeler Car & LMVs Bus & Trucks (2 Axle) MAV Speed (Kmph) Survey 85th method Max Min Average Percentile Video TIRTL Difference % Video TIRTL Difference % Video TIRTL Difference % Video TIRTL Difference % Capacity With the extracted video data of morning and evening, capacity of the selected highway was determined using Greenshields method. Data was aggregated for 5 minutes time interval. Dynamic PCU values of the vehicles were calculated for each lane using Prof. Satish Chandra s concept [10]. Space Mean Speeds were calculated for 5 minutes time interval by the harmonic average method. The Dynamic PCUs estimated by the above concept are given in Table 4.

8 Speed (Kmph) Fig. 7. Cumulative Distribution of S peeds (Video & TIRTL) - Vehicle category wise Table IV: Estimated Dynamic PCU values for all vehicle categories Sl. No Vehicle Type PCU Median Lane Shoulder Lane 1 3 Autos Autos Buses Car big Car small Hcv Lcv Mcv Two Wheeler Minibus Density was calculated as the ratio of flow rate and speed. The relationship of Speed Density and Speed - Flow were developed (Figure 8) and therefore the capacity of the highway was found to be 2602 PCUs/Hr/Lane Flow Rate (PCU/Hr/Lane) Fig. 8. Speed Flow relationship

9 6. Limitations of the methods Based on the experience gained from this study, the pros and cons of the two methods with respect to various aspects are tabulated below (Table 5). Sl. No 1 Setup 2 Table 5. Limitations of TIRTL and Video graphic surveys in various aspects Aspects TIRTL survey Video Graphic survey Data Preparation Camber must be less than 15 cm. Location must be safe as the instrument may be hit by vehicles No preliminary works required Location must be free from the visibility of drivers Lines to be drawn to indicate the trap length with the help of multimedia software or adhesive tapes must be used to demarcate before recording. 3 Data Storage It requires relatively less storage space Large volume of storage is required 4 Time of survey Suitable for dark light conditions Not suitable for dark light conditions 5 Data Processing Tailor made output Time consuming Requires skilled man power 7. Conclusion It was attempted to understand the usage of TIRTL & Videography for vehicular count and speed prediction. The limitations and advantages of the two methods are discussed in this paper. Though the two methods require safe positioning, TIRTL has several limitations in deployment as it has to be placed on the edges of the carriageway. With the help of pneumatic mast or several other technologies, videography can be deployed anywhere. It is easy to retrieve the data from TIRTL whereas it required skilled manpower to extract from video records. Installing TIRTL was quick and whereas video recording needed more manpower and energy. Acknowledgement The authors would like to thank Center of Excellence in Urban Transport (CoEUT) of IITM for providing TIRTL and Central Road Research Institute for their financial support through the research study Development of Indian Highway Capacity Manual (Indo-HCM). References Lopes, Bento, Huang, Antoniou, Ben-Akiva (2010), Traffic and Mobility Data Collection for Real-Time Applications (pp ), 13 th International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Portugal. Leduc G. (2008), Road Traffic Data: Collection Methods and Applications, European Commission, Joint Research Center, Institut e of Prospective Technological Studies, JRC Guy B.P and Fricker J. (2005), Guidelines for Data Collection Techniques and Methods for Roadside Station Origin -Destination Studies, Joint Transportation Research Program, Final Report, FHWA/IN/JTRP-2005/27. Highway Capacity Manual (1965), Transportation Research Board, Washington DC. Hidas P. and Wagner P. (2004), Review of Data Collection Methods for Microscopic Traffic Simulation, Proceedings of WCTR, World Conference on Transport Research (WCTR), Istanbul, Turkey.

10 Lyles R.W., and Wyman J.H. (1983), An Operational Review of Traffic Data Collection Systems, ITE Journal. Tarefder R., and Brogan J. (2014), A Review of statewide traffic data collection, processing, projection and quality control, Transportation Research Arena, Paris. Robichaud K., and Gordon M., Assessment of Data Collection Techniques for Highway Agencies, Transportation Research Record, Accessed on 1st August Chandra S., Kumar V., and Sikdar P.K. (1995), Dynamic PCU and Estimation of Capacity of Urban Roads, Indian Highways, Vol. 23, Issue 4.