No abstract

No abstract

Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

No abstract

Traffic density is an indicator of congestion and the present study explores the use of data-driven techniques for real time estimation and prediction of traffic density. Data-driven techniques require large database, which can be achieved only with the help of automated sensors. However, the available automated sensors developed for western traffic may not work for heterogeneous and lane-less traffic. Hence, the performance of available automated sensors was evaluated first to identify the best inputs to be used for the chosen application. Using the selected data, implementation was carried out and the results obtained were promising, indicating the possibility of using the proposed methodology for real time traveller information under such traffic conditions. Keywords: Automated traffic sensors, artificial neural network, k-nearest neighbour, traffic density.WITH the fast growing urban population and increasing vehicle population, it is becoming difficult to implement efficient traffic management for Indian roads. Intelligent transportation systems (ITS) is viewed as an option to handle some of these issues and is becoming more popular under this scenario. ITS enable gathering of data and providing timely feedback to traffic managers and roadusers based on the real time data. Advanced traveller information system (ATIS) is a major functional area of ITS and it deals with providing real time traffic information to travellers for making informed travel decisions. The information provided can include expected travel time, congestion condition, locations of incidents, weather and road conditions, optimal routes, recommended speeds and lane restrictions.Traffic congestion information is most sought after, followed by travel time and travel speed information. However, congestion being qualitative in nature, there is a need to identify the best measure to quantify it. As per the Highway Capacity Manual (HCM) 1 , traffic density on freeways, delays at signalized intersections and walking speed for pedestrians are examples of measures of effectiveness that characterize traffic conditions on a facility.Of these, traffic density is the primary measure for quantifying congestion on roadways, other than the intersection areas. Traffic density is the number of vehicles occupying a given length of roadway. Density being a spatial variable makes it difficult to carry out direct measurements. Aerial photography is the primary approach to measure density directly from field, which is very difficult to implement. Hence, it is usually estimated from other location-based parameters such as speed, flow or occupancy, making it a challenging research problem. This problem of estimation of density from location based parameters such as volume and time mean speed (TMS) obtained using selected sensors is considered in this study. This becomes more challenging under Indian conditions, with heterogeneous and lane less-traffic, leading to high variability and randomness. In addition, these traffic conditions make automated data collection ...