On an efficient and effective Intelligent Transportation System (ITS) using field and simulation data

Abstract

Intelligent transportation system (ITS) applications are expected to provide a more efficient, effective, reliable, and safe driving experience, which can minimize road traffic congestion resulting in a better traffic flow management. To efficiently manage traffic flows, in this paper, we compare the effectiveness of two well-known vehicle routing algorithms: the Dijkstra's shortest path algorithm and the A * (Astar) algorithm in terms of the total travel time and the travel distance. To this end, we built a generic ITS test-bed and created several real-world driving scenarios using field and simulation data to evaluate the performance of these two routing algorithms. The dataset used in our simulation is six weeks traffic volume data from 08/01/2012 to 09/27/2012 in the Maryland (MD)/Washington DC and Virginia (VA) area. Our simulation data shows that an increase in network size results in scalability problems as the efficiency and effectiveness of these algorithms diminishes in larger road networks with greater traffic volume densities, flow rates, and congested conditions. In addition, the imprecision of the road network increases as the network size and the traffic volume density increases. Our study shows that the ability of these vehicular routing algorithms to adaptively route traffic depends on the size and type of road networks, and the current roadway conditions.

An evaluation into the efficiency and effectiveness of machine learning algorithms in realistic traffic pattern prediction using field data

“…MATLAB R2013a, and Microsoft's Excel 2013 were used in data processing, computations, evaluation of algorithms performance results, and visualizations [1]. Tables 1 and 2 with respect to classification, our dataset consists of 1330 samples with five features and predictors as input vectors namely: zone_id, speed, date/time, occupancy, and quality, and one target vector element, namely congested, which has two possible outcomes: yes = 1 (for speed less than the default speed limit of the roadway, e.g., 65km/h indicating the presence of congestion on the roadway [I-270]) or no = 0 (for speed greater than or equal to 65km/h that indicating the absence of congestion on I-270) as shown in Figure 6 …”

On an efficient and effective intelligent transportation system (ITS) safety and traffic efficiency application with corresponding driver behavior

“…Specifically, using our realistic field data, road network, and simulation, we investigated the impact of driver distraction levels on the two popular age groups of drivers: young and middle-age drivers. Our choice of this age group was motivated by the fact that, as aforementioned, according to the National Highway Traffic Safety Administration (NHTSA), the age distribution of drivers most prone to engage in an in-vehicle distracting activity was recorded at between 16 to 25 years with the highest distraction propensity of 6.6% [1-3, 22, 24] [25]. Figure 1 shows our reference study area in OpenStreetMap.…”

Vehicle routing in urban areas based on the Oil Consumption Weight ‐Dijkstra algorithm

“…Furthermore, we add the factor of dynamic time [13–15]. We applied a local map of Changchun as the sample for more practical significance and making process more efficient [16, 17]. At the crossroads, we choose a diffident value to measure the oil consumption from driving condition [18].…”