Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Travel time estimation is an integral part of Intelligent Transportation Systems, and has been an important component in traffic management and operations for many years. Travel time, being spatial in nature, requires spatial sensors to measure it accurately. Bluetooth is emerging as a promising technology for the direct measurement of travel time, and is reported in a few studies from homogenous traffic conditions. At the same time, there have been no studies on the applicability of Bluetooth for travel time estimation in heterogeneous traffic seen in Istanbul and even that Turkey. Bluetooth data collected from a busy urban road in Istanbul city have been analyzed and the penetration rate was found to be about 5 %. Two wheelers and light motor vehicles have been detected using the Bluetooth sensor and the data have been extrapolated to estimate travel times of other classes of vehicles. The study developed linear relationships between speeds of different classes of vehicles through weighted linear regression methods and were used for the estimation of stream travel time. The results obtained were promising and show that Bluetooth is a cost-effective technology for estimation of travel time for heterogeneous traffic conditions.

On the relationship between the activity at point of interests and road traffic

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green transportation.Therefore,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management concepts.In contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road traffic.The s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as well.People's general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed estimation.The method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.

Real-time urban traffic information estimation with a limited number of surveillance cameras

Constant traffic congestion consumes enormous amounts of energy and causes vastly increased journey times. Therefore, real-time traffic information is of great importance to the public because such information is invaluable to more efficient traffic control and travel planning. To obtain such information in metropolises like Shanghai, however, is very challenging due to the extraordinarily large scale and com- plexity of the underlying road network. In this paper, we pro- pose a novel traffic estimation scheme utilizing surveillance cameras pervasively deployed in cities. With only a limited number of roads with cameras, we adopt a measurement- based traffic matrix （TM） estimation method to infer the traf- fic conditions on those roads with no cameras. Extensively trace-driven simulations as well as field study results show that our scheme can achieve high accuracy with a very limited number of measurements. The accuracy of our measurement- based algorithm outperforms the traditional speed-based and model-based approaches by up to 50%.

Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost- effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R^2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

Estimation of vehicle queuing lengths at metering roundabouts

Signalized metering roundabouts are equipped with advanced loop detectors and traffic signals that can reduce vehicle queuing lengths, especially on the dominant approach, when unbalanced traffic flow conditions occur. At a metering roundabout, changeable queuing lengths and the location of detectors determine signal phase times, which in turn affect queuing length on each approach. To date, most studies have focused on perfor- mance comparisons between normal and metered roundabouts, but have failed to evaluate the effect of detector locations on queuing formations. In addition, no guidelines have been developed to enable practitioners to select the appropriate detector location that would lead to optimum roundabout performance. This study, therefore, formulated a numerical model for the estimation of queuing length at a metering roundabout. The model consists of advance vehicle detectors on two approaches and one traffic signal. In order to calibrate and verify the model, queuing lengths were recorded using two drones for the Old Belair Road metering roundabout in Adelaide, South Australia. In order to assess the fitness of the model, an R2 test was conducted, and the results showed that the numerical model can predict queuing lengths on the controlling and metered approaches with up to 83% of R2 value. Moreover, the estimated queuing lengths were compared against those predicted by the software AIMSUN for the same location and under the same conditions. It is expectedthat the model will assist and guide practitioners in determining the best detector locations for metering roundabouts.