**2.2. Data collection**

Traffic weight data is traditionally based on roadside truck surveys, or, more recently, WIM measurements. High-speed WIM stations are able to weigh axles and collect time stamps without stopping vehicles. Axle time headways can be then computed. *Double loop detectors* are used for supplementing information regarding speed. This enables the distance between axles to be computed and to reconstruct the vehicle configuration. The overall vehicle length can also be detected.

Data from paired WIM and loop stations has been widely used. Single-vehicle data is normally available for those stations, although sometimes only for heavy vehicles. Unfortunately, many WIM and loop detectors are not currently reliable at very low speeds [14]. As a consequence, data is largely collected during free-flowing traffic conditions, which also occur more frequently than congested conditions, whereas data about slow-moving vehicles is generally lacking.

The recorded traffic data may be directly used for subsequent structural analysis. Nevertheless, since the recorded dataset is rarely sufficiently large, it is preferable to use it as a basis to generate additional artificial traffic by means of common Monte Carlo simulations for further use in structural analysis – see for instance [25].

Except for very short spans, the next step is to reconstruct the spatial distribution of vehicles from the recorded point measurements. This is equivalent to find the *headways* or gaps between vehicles.3 When using individual recorded speeds, the headway *h* between the current vehicle *i* crossing the detector at a time stamp *t*<sup>i</sup> and the leading vehicle *i*-1 (that crossed the detector at a time stamp *t*i-1 and with speed *v*i-1) can be estimated as follows:

$$h\_i = \nu\_{i-1} \cdot (t\_{i-1} - t\_i) \tag{2}$$

thereby assuming that the leading vehicle is keeping its speed *v*i-1 . 4 As seen in Section 2.1, this is an acceptable assumption only in free-flowing traffic. However, when load effects are calculated during congestion, large variations in speed may result in unrealistic spatial distributions or even vehicle overlapping. This aspect is particularly significant for long spans [15].

<sup>3</sup> Here the headway is intended as the distance between same points of two consecutive vehicles, for instance, the front axle or the front bumper; the gap is the bumper-to-bumper distance between two vehicles. They can also be intended as time distances, and when so it will be specified. Note that different interpretations of these terms may be found.

<sup>4</sup> Other assumptions on the speed are also possible, and these will clearly affect the estimated headway. For instance, some studies assume a constant speed for all the vehicles in the traffic stream [26, 27], although the recorded speeds were different. This implies that vehicles are passed on the bridge with the same time headways as were recorded, but not with the same space headways, which depend on the assumed speed. For a more detailed discussion, see [15].

In theory, the use of cameras over a stretch of road would provide accurate information about vehicle positions. However, there are several practical issues which make a camera-based approach difficult, such as sensitivity to lighting conditions or heavy post-processing requirements [14]. Cameras have been deployed for research purposes [21, 22, 28–30] and are becoming increasingly popular, thanks to the recent technological advances. In bridge-related studies, only a few studies report that cameras were used to collect traffic data [7, 11, 31–33].
