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Measurements of traffic speed and travel times are needed for a wide range of practical applications (also development of simulated driving cycle). Road user‘s speed in traffic environments is very informative and thus widely used for driver and behavioural studies parameters. Methods used for data collection for input to micro-simulation study are given below.

(a) Local detectors approach

The current state-of-the-practice for data collection regarding traffic speeds relies mainly on local detectors that measure the speed at a specific point along the roadway.

One of the most widely used technologies for this purpose is magnetic loop detectors, installed underneath the roadway surface. Due to the cost of installation and maintenance of local detectors, they are typically installed only on a relatively small portion of the roadway system, thus providing limited coverage of the entire transportation network.

Most of the conventional instruments (e.g. radar guns or loop detectors) can provide only spot measurements of speed (Gera, 2007).

(b) Video filming approach

In addition, in an urban environment there are many traffic interruptions, particularly at intersections. These interruptions cause delays that are not depicted by measuring speeds at any specific point along the road. An alternative approach is to measure travel times of vehicles along a certain route or route segment. Use of video recordings can help to make such studies. The increased knowledge and experience in video analysis makes it practically possible to automate data collection process, thus allowing collection of continuous (as long as a road user is within the camera‘s view) speed data with reasonably low time consumption and costs (Almadani, 2003). The main

flow. Besides that, video camcorders are comparatively simple and affordable, compared to the cost for driver and petrol in the floating-car method for example. Therefore, the video recording method is employed in this study. Real-world driving data was collected once for the calibration and validation of the results.

A major advantage of video recording is that it can obtain all the trajectories and sizes of the vehicles in a traffic stream objectively. Another merit is that the video footage can be reviewed and examined repeatedly at later times. In addition, it is an un-intrusive and naturalistic observation, which ensures that the normal behaviour can be observed and the data collected are not affected by the presence of researchers. The following are the advantages of video recording methods:

However, extracting data from video footage is an extremely labour-intensive process, which is the main disadvantage of this method. According to Taylor and Young (1988), the analysis process can take up to six times long as the real time recording.

Another disadvantage of this method is the limited survey areas, around 200 m (Hidas and Wagner, 2004) to 400 m (Slinn et al., 1998), depending on the resolution of the images and the field of view of the camera. The requirement of an elevated position is also a limitation of this method

(c) Floating-car method

The advantage of the floating-car method is that the data processing is simpler and it can directly collect the useful parameters, depending on the sensors employed. The floating car method can be equipped with a wide range of sensors, including camcorders (for example, as used by Olsen and Wierwille [2001]). Floating-car method has some limitations because the data can only be collected from a limited number of instrumented vehicles. Driver‘s behaviour under surveillance and limitation of choices in sensor affect the range of data collection. In order to obtain a complete picture of the surroundings, the vehicles need to be well designed and well equipped, making the whole process expensive. Context of the experimental environment should be setup to avoid the surrounding environment (Hidas and Wagner, 2004).

For a small number of occasional needed measurements, dedicated ‗floating vehicles‘

can be used. The equipped floating vehicles with GPS can improve the accuracy of the measurements (Byon et al., 2006). Vehicle equipped with differential GPS (DGPS), was used and managed to match its route for 93% of the distance it travelled to examine the ability to meet location accuracy requirements. Equipping vehicles with GPS was feasible mainly when considering specific fleets (Reinhart and Schafer, 2006; Du and Aultman-Hall, 2006). GPS was equipped in trucks, cars and buses to find their routes and evaluate infrastructure performance improvements (Chakroborty and Kikuchi, 2004). GPS data from a fleet of equipped vehicles, typically limited in its size, was found to have limited coverage area in the real world. GPS provides data for specific fleets often have specific travel patterns that may not necessarily representative of the entire population.

(e) Cellular measurements approach

Cellular phones have reached extensive market penetration in many countries.

Cellular phones can be used to identify locations, since any cellular service system contains information about the locations of its users over time. Gera (2007) examined the performance of cellular phone service provider for measuring traffic speeds and travel times. Cellular measurements were compared with those obtained by dual magnetic loop detectors. A good match between data was found using the two measurement methods;

indicating that the cellular phone-based system is useful for other practical applications, such as advanced traveller information systems, and evaluating system performance for modelling and planning.

The continuous speed data from GPS, however, is more informative since it provides speed-based indicators that can be calculated (e.g. speeds variation over a distance, acceleration, travel time, etc.).

(f) Data collection from secondary sources

Secondary data is the data, which has been collected by individuals or agencies for purposes other than those of our particular research study. It is far cheaper to collect secondary data than to obtain primary data. For the same level of research budget, a

thorough examination of secondary sources can yield a great deal of more information than can be collected through a primary data collection exercise.

Whilst the benefits of secondary sources are considerable, their shortcomings have to be acknowledged. There is a need to evaluate the quality of both the source of the data and the data itself. The main problems may be categorised as follows:

Definitions: The researcher has to be careful, when making use of secondary data, of the definitions used by those responsible for its preparation.

Measurement error: When a researcher conducts fieldwork, she/he is possibly able to estimate inaccuracies in measurements through the standard deviation and standard error, but these are sometimes not published in secondary sources. The only solution is to try to speak to the individuals involved in the collection of the data to obtain some guidance on the level of accuracy of the data.

Source bias: Researchers have to be aware of vested interests when they consult secondary sources. Those responsible for their compilation may have reasons for wishing to present a more optimistic or pessimistic set of results for their organisation. It is not unknown, for example, for officials responsible for estimating food shortages to exaggerate figures before sending aid requests to potential donors. Similarly, and with equal frequency, commercial organisations have been known to inflate estimates of their market shares.

Reliability: The reliability of published statistics may vary over time. It is not uncommon, for example, for the systems of the collecting data to have changed over time but without any indication of this to the reader of published statistics. Geographical or administrative boundaries may be changed by government, or the basis for stratifying a sample may have altered. Other aspects of research methodology that affect the reliability of secondary data are the sample size, response rate, questionnaire design, and modes of analysis.

Time scale: Most census take place at 10-year intervals, so data from this and other published sources may be out of date at the time the researcher wants to make use of the statistics¹.

(g) Accuracy of the data

When evaluating data collection methods, the accuracy of the data acquired is an important issue to be considered. However, it is difficult, in general, to compare the accuracy of the data obtained from these categories because the accuracy of the data collected by the floating-car method (may include GPS or cellular-based data) depends on the equipment used. In addition, their purpose of use is limited to vehicles only.

Accuracy of the local detector depends on sensor quality and installation efficiency and detector is placed on limited stretch of section. Therefore, the comparison between these approaches cannot be made in this section.

The accuracy of the video recording method depends on the pixel resolution of the video images, so the trade-off between pixel resolution and field of view has to be considered. For example, a telephoto image provides a high resolution but has a limited survey area whereas a wide-angle image accommodates more information but has a limited resolution. Therefore, a camcorder with a higher definition or a larger focal length factor will be more flexible to provide data with higher accuracy. The literature shows that different extents of accuracy, from 0.3 m to 1.3 m, have been reported (Khan and Raksuntorn, 2001; Hasan et al., 2002; Hoogendoorn et al., 2003; Hidas, 2005). Also, data from secondary sources need to be validated by collection of real world data; if the data accuracy can reach such a standard, it should be sufficient for calibrating the models as proposed in Chapter 10.