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.

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream

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.

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.

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.

scholarly journals Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Cheng ◽  
Gen Li ◽  
Xianhua Chen
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Random Forests ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Time Estimation ◽  
Variable Importance ◽  
Estimation Accuracy ◽  
Travel Time Estimation

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.

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.

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