## 2 DPR for Roadway and Traffic Type Classification
### Data Analysis and Formation of RDPs  
As a first step, data are collected using an on-board diagnostic (OBD) device from a fixed route at different times of the day for a month. The selected route is a drive over a distance of 10.4 km. The differences in morning and evening average travel times are noted due to variations in traffic, as shown in Figure 2.  
The collected speed versus time data have been used to extract certain critical characteristic features, such as average speed within a window of 100 s (\(s^{100}\)), average positive and negative acceleration within a window of 10 s (\(a^{10}+\) and \(a^{10}-\)), and 5 s (\(a^{5}+\) and \(a^{5}-\)), respectively, along with attributes like number of stops, time of the day, and day of the week. For the classification of roadway type, a sampling time of 100 s is chosen to calculate the average speed. For the classification of traffic type, within the 100 s sampling interval, the data are further sub-sampled for intervals of 10 and 5 s.  
In the next step, target classes are defined and IF-ELSE rules are formulated for the classification of the target classes, as shown in Figure 3.  
Figure 2: Speed data collection along a route: (**a**) variations in average travel time, (**b**) route map, and (**c**) variations in driving profile.  
The six target classes considered are target class 1: highway high traffic (HHT); target class 2: highway low traffic (HLT); target class 3: suburban high traffic (SUHT); target class 4: suburban low traffic (SULT); target class 5: urban high traffic (UHT); and target class 6: urban low traffic (ULT). When sampled at 100 s, the first condition checked is that of average speed. The comparison of a 100 s average speed with a higher (\(s_{h}^{100}\)) and lower bound (\(s_{l}^{100}\)) gives an estimate about the roadway type. The next conditions which are checked are related to the traffic condition, namely, \(a^{10}+\), \(a^{10}-\), \(a^{5}+\), \(a^{5}-\) and number of stops (#stops).  
Case 1: If \(s_{h}^{100}\leq s^{100}\), then an estimate is made about the roadway type being a highway. If \(a_{h}^{10}+\leq a^{10}+\), \(a_{h}^{10}-\leq a^{10}-\), \(a_{h}^{5}+\leq a^{5}+\), \(a_{h}^{5}-\leq a^{5}-\), and #stops\({}_{l}\leq\)#stops, the traffic condition is considered to be high traffic, and otherwise low traffic type.  
Case 2: If \(s_{l}^{100}\leq s_{l}^{100}\leq s_{h}^{100}\) then the road condition is considered to be suburban. If \(a_{h}^{10}+\leq a^{10}+\), \(a_{h}^{10}-\leq a^{10}-\), \(a_{h}^{5}+\leq a^{5}+\), \(a_{h}^{5}-\leq a^{5}-\), and #stops\({}_{l}\leq\)#stops, then the traffic condition is considered to be high traffic, and otherwise low traffic type.  
Case 3: If \(s_{l}^{100}\leq s_{l}^{100}\) then the road condition is considered to be urban. If \(a_{h}^{10}+\leq a^{10}+\), \(a_{h}^{10}-\leq a^{10}-\), \(a_{h}^{5}+\leq a^{5}+\), \(a_{h}^{5}-\leq a^{5}-\), and #stops\({}_{l}\leq\)#stops, then the traffic condition is considered to be high traffic, and otherwise low traffic type.  
In each case, the upper and lower thresholds are defined separately by taking into consideration the values associated with the standard cycles. Further, an analysis of four standard driving cycles was carried out in terms of the characteristic features, and the results are listed in Table 1. The table reveals the characteristic features of New York city cycle (NYCC), Artemis urban, urban dynamometer driving schedule (UDDS), and highway fuel economy test cycle (HWFET).  
Based on the data collected under real driving conditions and the standard cycle data, synthetic datasets were generated and utilized to create RDPs. RDPs represent different driving conditions for roadway and traffic type and have been developed based on comparisons of characteristic features of real-time driving and standard cycles. On the basis of the characteristic features, four standard cycles were compared with the real data points to synthesize the pattern of RDPs. The six RDPs are shown in Figure 4.  
Figure 3: Flow diagram describing different target classes.