195 research outputs found

    Application of Bluetooth Technology for Mode- Specific Travel Time Estimation on Arterial Roads: Potentials and Challenges

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    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

    Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates

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    Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor. The current paper describes an adaptive filtering algorithm for estimating bicycle travel times using Bluetooth data, with consideration of low sampling rates. The data for bicycle travel time using Bluetooth sensors has two characteristics. First, the bicycle flow contains stable and unstable conditions. Second, the collected data have low sampling rates (less than 1%). To avoid erroneous inference, filters are introduced to “purify” multiple time series. The valid data are identified within a dynamically varying validity window with the use of a robust data-filtering procedure. The size of the validity window varies based on the number of preceding sampling intervals without a Bluetooth record. Applications of the proposed algorithm to the dataset from Genshan East Road and Moganshan Road in Hangzhou demonstrate its ability to track typical variations in bicycle travel time efficiently, while suppressing high frequency noise signals.</p

    Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

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    The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

    Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

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    Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF

    Evaluating Bluetooth and Wi-Fi Sensors as a Tool for Collecting Bicycle Speed at Varying Gradients

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    AbstractThe wide applications of mobile units with communication technology opens up new possibilities in data collection among road users. Sensors detecting Bluetooth and WiFi units have been successfully applied in collecting travel times from motorized vehicles. The same technology could potentially be applied to bicycle transport. As part of a policy for facilitating sustainable transport modes, more knowledge about bicyclists is needed.This paper presents a study answering two research questions: 1. Will the use of Bluetooth and WiFi sensors provide reliable travel time data from bicyclists? 2. How are bicycle section speeds affected by different gradients?To answer the first research question a two step test was set up to validate the equipment. Each equipment set consisted of one Bluetooth sensor and one WiFi sensor. Two sets of equipment were placed along a bicycle path with a distance of 550 meters between them. There was no adjacent road, ensuring only bicyclists and pedestrians would be registered.Firstly, a controlled test was conducted with known Bluetooth and WiFi units brought by test cyclists. The test took place nighttime to avoid disturbance from other travelers. To validate the measured travel times, actual travel times were also registered manually. The test concluded that travel times based on Bluetooth were more accurate than travel times based on WiFi. However, both sensors provided travel times that were not significantly different from the actual ones.Secondly, an open test was conducted along the same cycle path section during rush hours to find the penetration rate (i.e. the number of registered travel times divided to the total number of passing bicyclists), and to manually register all travel times to see whether the registered travel times were representative of the travel times for all passing bicyclists. The test showed that to reduce the variance in the registered data in order to calculate a valid average travel time, a minimum of 20 observations were needed. The test further showed that more than 90% of the registered travel times were detected by the WiFi unit.The second research question was addressed by selecting 7 road sections with average gradients varying from 0.5% to 9.2%, using section lengths between 500 to 1500 meters. A total of approximately 2400 travel times were registered, giving the following main findings: The average speed varied from 22.4km/h to 8.6km/h uphill according to increasing gradient. The variance is reduced by increasing gradient. When cycling downhill the average speed varies between 21.3km/h and 36.5km/h, registering the highest speed at 5.4% gradient, showing that bicyclists choose lower speeds on steep downhills. The variance seems unaffected by the gradient.Based on this study, sensors using Bluetooth and WiFi technology can be recommended as tools to collect travel times from bicycle traffic. However, some important prerequisites are needed: the sensor locations have to be carefully chosen, as do appropriate filtering of data to avoid inclusion of pedestrians and motorists in the dataset

    Multimodal Traffic Speed Monitoring: A Real-Time System Based on Passive Wi-Fi and Bluetooth Sensing Technology

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    SED-000080This manuscript was originally printed in the IEEE Transactions on Intelligent Transportation Systems, Volume 9, Issue 14.Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors\u2019 detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy C-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network

    Estimating Transit Ridership Patterns through Automated Data Collection Technology: A Case Study in San Luis Obispo, California

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    Public transportation offers a crucial solution to the travel demand in light of national and global economic, energy, and environmental challenges. If implemented effectively, public transit offers an affordable, convenient, and sustainable transportation mode. Implementation of new technologies for information-harvesting may lead to more effective transit operations. This study examines the potential of automated data collection technologies to analyzing and understand the origin-destination flow patterns, which is essential for transit route planning and stop location placement. This thesis investigates the collection and analysis of data of passengers onboard San Luis Obispo Transit buses in February and March 2017 using Bluetooth (BT) and automatic passenger counter (APC) data. Five BlueMAC detectors were placed on SLO Transit buses to collect Bluetooth data. APC data was obtained from San Luis Obispo Transit. The datasets were used to establish a data processing method to exclude invalid detections, to identify and process origin and destination trips of passengers, and to make conclusions regarding passenger behavior. The filtering methods were applied to the Bluetooth data to extract counts of unique passenger information and to compare the filtered data to the ground-truth APC data. The datasets were also used to study the San Luis Obispo Downtown Farmer’s Market and its impact on transit ridership demand. The investigation revealed that after carefully employing the filters on BT data there were no consistent patterns in differences between unique passenger counts obtained from APC data and the BT data. As a result, one should be careful in employing BT data for transit OD estimation. Not every passenger enables Bluetooth or owns a Bluetooth device, so relying on the possession of Bluetooth-enabled devices may not lead to a random sample, resulting in misleading travel patterns. Based on the APC data, it was revealed that transit ridership is 40% higher during the days during which Higuera Street in Downtown San Luis Obispo is used for Farmer’s Market – a classic example of tactical urbanism. Increase in transit ridership is one of the aspects of tactical urbanism that may be further emphasized. With rapidly-evolving data collection technologies, transit data collection methods could expand beyond the traditional onboard survey. The lessons learned from this study could be expanded to provide a robust and detailed data source for transit operations and planning

    Improving Traffic Safety and Efficiency by Adaptive Signal Control Systems Based on Deep Reinforcement Learning

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    As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist\u27s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety

    Evaluation of the use of a City Center through the use of Bluetooth Sensors Network

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    In order to achieve the objectives of Smart Cities, public administrations need to take measures to regulate mobility, which undoubtedly requires a high level of information and sensorization. Until the implementation of the connected vehicle takes place, it is still necessary to install sensors to obtain information about mobility. Bluetooth sensors are becoming a useful tool due to the low cost of equipment and installation. The use of Bluetooth sensors in cities, with short distances between sensors, makes it necessary to propose new classification algorithms that allow the trips of pedestrians and vehicles to be differentiated. This article presents the study carried out in the city of Valencia to determine the use of motor vehicles in the historic center and propose a new classification algorithm to distinguish between an onboard Bluetooth device and the same device carried by a pedestrian when it is not possible to use the travel time for the classification due to the short distance between sensors. This causes very similar or even indistinguishable travel times for vehicles and for pedestrians. We also propose an algorithm that allows vehicles to be classified according to what type of trip is made always through the historical center of Valencia, whether it is to make a shorter itinerary through the city or to access the center for any type of business. This algorithm would enable the Origin-Destination matrix of an urban network with short distances between sensors if they are available in all entries and exits. Likewise, the results obtained have allowed to positively evaluate the algorithm defined to distinguish between trips made by a pedestrian or a vehicle in a city, using the MAC address of their mobile devices with very short distances among sensors. The results of this study show that it is possible to use Bluetooth technology, with low cost installations, to evaluate the use of the city by motor vehicles

    A robust method for real time estimation of travel times for dense urban road networks using point-to-point detectors

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    Data collection for the provision of real time traveller information services is a key issue, both for the travellers as well as for traffic managers. This paper presents a methodology for estimating travel times in dense urban road networks using point-to-point detectors. The aim is to fill in the gap of existing travel time estimation methodologies, which are based on point-to-point detection devices. Bluetooth (BT) is considered as one of the less expensive technologies for estimating travel times. Data filtering and data correction require rigorous methodologies, which if not correctly applied may result in inaccurate results as compared to other methods. The main difficulty of data processing is to identify the correct set of Media Access Control (MAC) addresses for estimating travel times, especially in dense urban road networks, where three main error sources exist: the co-existence of various transport modes (private vehicles, buses, pedestrians, bicycles etc.), the existence of more than one possible paths between two BT detectors and the existence of stops or trips ending between two BT detectors. These error sources create outliers that need to be identified and taken into account. The results of the proposed methodology confirm that outliers are eliminated, as shown by a case study with 10 BT detectors installed at major intersections of Thessaloniki’s Central Business District (CBD)
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