scholarly journals Investigation with Bluetooth Sensors of Bicycle Travel Time Estimation on a Short Corridor

2012 ◽  
Vol 8 (1) ◽  
pp. 303521 ◽  
Author(s):  
Zhenyu Mei ◽  
Dianhai Wang ◽  
Jun Chen
Keyword(s):  
Travel Time ◽  
Information Acquisition ◽  
Sampling Error ◽  
Primary Source ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Time Data ◽  
Spatial Error ◽  
Travel Time Data

Accurate travel time information acquisition is essential to the effective planning and management of bicycle travel conditions. Traditionally, video camera data have been used as the primary source for measuring the quality of bicycle travel time. This paper deals with an investigation of bicycle travel time estimation on a short corridor, using Bluetooth sensors, based on field survey of travel time at one arterial road in Hangzhou. Usually bicycle travel time estimates with Bluetooth sensors contain three types of errors: spatial error, temporal error, and sampling error. To avoid these, we introduced filters to “purify” the time series. A median filtering algorithm is used to eliminate the outlier observations. The filtering scheme has been applied on Genshan East Road and Moganshan Road. Test data are used to measure the quality of bicycle travel time data collected by the Bluetooth sensors, and the results show that the new technology is a promising method for collecting high-quality travel time data that can be used as ground truth for evaluating other sources of travel time and other intelligent transportation system applications.

Travel time estimation in urban networks using limited probes data

2011 ◽  
Vol 38 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Mohamed El Esawey ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
Travel Time ◽  
Network Performance ◽  
Time Estimation ◽  
Performance Measure ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

Quality of Private Sector Travel-Time Data on Arterials

2016 ◽  
Vol 142 (4) ◽  
pp. 04016010 ◽  
Author(s):  
Jia Hu ◽  
Michael D. Fontaine ◽  
Jiaqi Ma
Keyword(s):  
Private Sector ◽  
Travel Time ◽  
Time Data ◽  
Travel Time Data

Estimating Travel Time Summary Statistics of Larger Intervals from Smaller Intervals Without Storing Individual Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett ◽  
Parichart Pattanamekar ◽  
Keechoo Choi
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Transportation Systems ◽  
Identification System ◽  
Time Interval ◽  
Travel Time Estimation ◽  
Time Data ◽  
Probe Vehicles ◽  
Mean And Variance ◽  
Link Travel Time

Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.

scholarly journals A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sunghoon Kim ◽  
Hwapyeong Yu ◽  
Hwasoo Yeo
Keyword(s):  
Travel Time ◽  
Polynomial Regression ◽  
Time Estimation ◽  
Superior Performance ◽  
Travel Time Estimation ◽  
Time Data ◽  
Modified Method ◽  
Road Section ◽  
Traffic Stream ◽  
Improved Performance

Travel time is valuable information for both drivers and traffic managers. While properly estimating the travel time of a single road section, an issue arises when multiple traffic streams exist. In highways, this usually occurs at the upstream of diverge bottleneck. The aim of this paper is to provide a new framework for travel time estimation of a diverging traffic stream using timestamp data only. While providing the framework, the main focus of this paper is on performing a few analyses on the stage of travel time data classification in the proposed framework. Three sequential steps with a few statistical approaches are provided in this stage: detection of data divergence, classification of divergent data, and outlier filtering. First, a divergence detection index (DDI) of data has been developed, and the analysis results show that this new index is useful in finding the threshold of determining data divergence. Second, three different methods are tested in terms of properly classifying the divergent data. It is found that our modified method based on the approach used by Korea Expressway Corporation shows superior performance. Third, a polynomial regression-based method is used for outlier filtering, and this shows reasonable performance even at a relatively low market penetration rate (MPR) of probe vehicles. Then, the overall performance of the travel time estimation framework is tested, and this test demonstrates that the proposed framework can show improved performance in distinctively estimating the travel times of two different traffic streams in the same road section.

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

2011 ◽  
pp. 252-273
Author(s):  
Vasileios Zeimpekis
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Real Life ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Time Data ◽  
Urban Freight ◽  
Real Time Traffic ◽  
Real Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Arterial Travel Time Validation and Augmentation with Two Independent Data Sources

2015 ◽  
Vol 2526 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Xuechi Zhang ◽  
Masoud Hamedi ◽  
Ali Haghani
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Multiple Sources ◽  
Time Data ◽  
Travel Time Data ◽  
Time Information ◽  
Fusion Framework ◽  
Validation Scheme

Travel time data are a key input to applications of intelligent transportation systems. Advancement in vehicle tracking and reidentification technologies and proliferation of location-aware and connected devices have made networkwide travel time data available to transportation management agencies. The trend started with data collection on freeways and has been quickly extended to arterials. Although the freeway travel time data have been validated extensively in recent years, the quality of arterial travel time data is not well known. This paper presents a comprehensive validation scheme for arterial travel time data based on GPS probe and Bluetooth data as two independent sources. Since travel time on arterials is subject to a higher degree of variation than that on freeways, mainly because of the presence of signals, a new validation methodology based on the coefficient of variation is introduced. Moreover, a context-dependent travel time fusion framework is developed to improve the reliability of travel time information by fusing data from multiple sources. All 2012 data from a busy arterial corridor in Maryland are used to demonstrate the proposed comparison and augmentation model.

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