Energy consumption in the transportation sector in Iran is significantly higher than global norms and standards which caused some issues including wasting national resources, deteriorating air quality, greenhouse gas (GHG) emissions etc. In Iran, liquid fuels including gasoline and gasoil are dominant sources of energy in the transportation sector. In this context, the road transport has the major share in consuming liquid fuel amongst others i.e. the rail, marine and air transportations. Energy consumption in road transport in Iran has to be decreased in order to reduce dependency on fossil fuels as well as negative environmental consequences. One solution is to adopt demand-side strategies. The overall aim of this study is to estimate the total fuel consumption for road transport in Iran in 2025 under a new scenario in which demand-side approach has been adopted. The aim of this study is to illustrate and discuss to what extent implementing some middle-term demand-side strategies could decrease fuel consumption in the road transport system in Iran by 2025.
Bluetooth sensors have a large detection zone compared with other static vehicle reidentification systems. A larger detection zone increases the probability of detecting a Bluetooth-enabled device in a fast-moving vehicle, yet increases the probability of multiple detection events being triggered by a single device. The latter situation could lead to location ambiguity and could reduce the accuracy of travel time estimation. Therefore, the accuracy of travel time estimation by Bluetooth technology depends on how location ambiguity is handled by the estimation method. The issue of multiple detection events in the context of travel time estimation by Bluetooth technology has been considered by various researchers. However, treatment of this issue has been simplistic. Most previous studies have used the first detection event (enter–enter) as the best estimate. No systematic analysis has been conducted to explore the most accurate method of travel time estimation with multiple detection events. In this study, different aspects of the Bluetooth detection zone, including size and impact on the accuracy of travel time estimation, were discussed. Four methods were applied to estimate travel time: enter–enter, leave–leave, peak–peak, and combined. These methods were developed on the basis of various technical considerations related to multiple detection events. A controlled field experiment was conducted to evaluate the accuracy of the methods through comparison with the ground truth travel time data measured by Global Positioning System technology. The results showed that the accuracy of the combined and peak–peak methods was higher than that of the other methods and that the employment of the first detection event did not necessarily yield the best travel time estimation.
Abstract One of the crucial factors to relieve the potential danger of rear-end collision is to synchronize the behavior of the lead and following vehicle on target lane, which includes the relative speed to the leading vehicle on the target lane and the time headways to both the leading and following vehicles on the target lane as the explanatory variables. In view of the rear-end conflicts importance, here it is tried to find out the critical headway of the vehicles in which vehicles encounter with this kind of Traffic Conflicts, based on Traffic Conflict Technique. It was also aimed at introducing a model to estimate the number of vehicles with rear-end conflict potential on the basis of physical and traffic equations in signalized intersections. Moreover rear-end potential conflicts have been calculated by modeling in MATLAB, then by comparison of the field observation and outcome of the model the accuracy of the model has been estimated.
The motivation behind this paper is to enhance the reliability of in-vehicle navigation systems by predicting the duration of incidents that cause congestion. The main objective of this paper is to develop a methodology for predicting incident duration using broadcast incident data and evaluate the performance of k-NN and hazard-based duration models for predicting incident duration; both of the models are presented in this paper. An incident dataset from the BBC for the Greater London area is used to evaluate the accuracy of both models so that the results give a direct comparison between the models. The strengths and weaknesses of the models are discussed in the paper based on this analysis. Results show that both k-NN and hazard based models have the potential to provide accurate incident duration prediction. While k-NN based models provided marginally more accurate prediction than hazard-based models, the hazard-based duration models can provide additional information such as delay probabilities that can be used by advanced routing and navigation algorithms. Results also show that traffic information incident feeds, such as the tpegML feed from the BBC or TMC information, can be used as a potential data source for incident duration prediction in vehicle navigation systems.
One of the crucial factors to relieve the potential danger of rear-end collision is to synchronize the behavior of the lead and following vehicle on target lane, which includes the relative speed to the leading vehicle on the target lane and the time headways to both the leading and following vehicles on the target lane as the explanatory variables. In view of the rear-end conflicts importance, here it is tried to find out the critical headway of the vehicles in which vehicles encounter with this kind of Traffic Conflicts, based on Traffic Conflict Technique. It was also aimed at introducing a model to estimate the number of vehicles with rear-end conflict potential on the basis of physical and traffic equations in signalized intersections. Moreover rear-end potential conflicts have been calculated by modeling in MATLAB, then by comparison of the field observation and outcome of the model the accuracy of the model has been estimated.
The tendency to use Bluetooth Technology (BT) for travel time estimation is increasing due to growing number of Bluetooth-enabled devices among road users, anonymity of BT detections, flexibility of deployment and maintenance of Bluetooth sensors etc. Although Bluetooth has been demonstrated as a promising technology, there remain problems which affect the accuracy of the estimation such as difficulty of distinguishing between multiple travel modes (e.g. motor vehicles, bicycles and pedestrians). This study aims to examine the feasibility of estimating mode-specific travel time using BT data under uncongested traffic conditions. In this context, three clustering methods Hierarchical, K-means and Two-Step are used as the core techniques for classification. The results show that the methods can successfully distinguish between motor vehicles and bicycles from BT detection events, resulting in accurate travel time estimation for motor vehicles.
A unique Bluetooth-enabled device may be detected several times or not at all when it passes a sensor location. This depends mainly on the strength and speed of a transmitting device, discovery procedure, location of the device relative to the Bluetooth sensor, the Bluetooth sensor's ping cycle (0.1 s), the size and shape of the sensor's detection zone, and the time span for which the Bluetooth-enabled device is within the detection zone. The influences of size of Bluetooth sensor detection zones and Bluetooth discovery procedure on multiple detection events have been mentioned in previous research. However, their corresponding impacts on accuracy and reliability of estimated travel time have not been evaluated. In this study, a controlled field experiment is conducted to collect both Bluetooth and global positioning system (GPS) data for 1000 trips to be used as the basis for evaluation. Data obtained by GPS logger are used to calculate actual travel time, referred to as ground truth, and to geo-code the Bluetooth detection events. In this setting, reliability is defined as the percentage of devices captured per trip during the experiment. It is found that, on average, Bluetooth-enabled devices will be detected 80% of the time while passing a sensor location. The impact of location ambiguity caused by the size of the detection zone is evaluated using geo-coded Bluetooth data. Results show that more than 80% of the detection events are recorded within the range of 100 m from the sensor center line. It is also shown that short-range antennas detect Bluetooth-enabled devices in a location closer to the sensor, thus providing a more accurate travel time estimate. However, the smaller the size of the detection zone, the lower is the penetration rate, which could itself influence the accuracy of estimates. Therefore, there has to be a trade-off between acceptable level of location ambiguity and penetration rate for configuration and coverage of the antennas.
AbstractThe problem of mode-specific travel time estimation is mostly relevant to arterials with different travel modes, including cars, buses, cyclists, and pedestrians. Traditional travel time measurement systems such as automated number plate recognition (ANPR) cameras detect only motor vehicles and provide an estimate of their travel times. Bluetooth technology has been used as an alternative to more expensive ANPR for travel time measurements in the recent past. However, Bluetooth-sensors detect discoverable electronic devices used by all travel modes. Bluetooth-based systems currently use the time stamp of device detection events by two sensors to estimate the travel time, and there is no direct way to estimate mode-specific travel times using this approach. Hence, estimating travel time using Bluetooth technology on urban arterials without classifying the modes of detected devices could provide a biased estimate. A novel method to estimate mode-specific travel times using Bluetooth technology that is capable of estimating mode-specific travel times, specifically distinguishing between the travel time of motor vehicles and bicycles, is presented in this article. The proposed method uses information about type of detected device (class of device, CoD) and radio signal strength indication (RSSI). The proposed method also uses the travel time of the detected device and its detection pattern across the road network by multiple Bluetooth sensors to estimate the travel mode of each detected device. The accuracy of the proposed method was evaluated against the ground truth obtained by manual transcription of traffic video recordings, and was compared against travel times obtained from ANPR, a commercially deployed Bluetooth-based method, and a clustering method. The results show that the proposed method provides travel time estimates using Bluetooth with almost the same level of accuracy as ANPR under mixed traffic conditions.Keywords: Automated Screening AlgorithmBluetooth TechnologyMode IdentificationMode-Specific Travel Time Estimation
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