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.

Advanced Techniques for Travel Time Data Collection

1996 ◽  
Vol 1551 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Shawn M. Turner
Keyword(s):  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Cellular Phone ◽  
License Plate ◽  
Time Data ◽  
Video Imaging ◽  
Advantages And Disadvantages ◽  
Travel Time Data ◽  
Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

scholarly journals Reanalysis of the 1761 transatlantic tsunami

2018 ◽  
Author(s):  
Martin Wronna ◽  
Maria Ana Baptista ◽  
Jorge Miguel Miranda
Keyword(s):  
Travel Time ◽  
18Th Century ◽  
Kinematic Model ◽  
Time Data ◽  
North East ◽  
Tsunami Observations ◽  
Travel Time Data ◽  
Tsunami Travel Time ◽  
Study Support ◽  
Time Information

Abstract. The tsunami catalogues of the Atlantic include two transatlantic tsunamis in the 18th century the extensively studied 1st November 1755, and 31st March 1761. The latest event struck Portugal, Spain, and Morocco around noontime. Several sources report a tsunami following the earthquake as far as Cornwall (United Kingdom), Cork (Ireland) and Barbados (Caribbean). An earlier analysis of macroseismic information and its compatibility with tsunami travel time information located the epicentre circa 34.5° N 13° W close to the Ampere Seamount at the eastern end of the Gloria Fault (North East Atlantic). The estimated magnitude of the earthquake is 8.5. In this study, we propose a tectonic source for the 31st March 1761 earthquake compatible with the tsunami observations in the Atlantic. We revisit the tsunami observations, reevaluate tsunami travel time data, and include a report from Cadiz not used before. The global plate kinematic model NUVEL 1A computes a convergence rate of 3.8 mm/y in the area of the presumed epicentre. We propose a source mechanism for the parent earthquake compatible with the geodynamic constraints in the region capable of reproducing most of the tsunami observations. The results of our study support the hypothesis that the 1761 event took place in the area of Coral Patch and Ampere seamounts, SW of the 1st November 1755, mega-earthquake source. Finally, this study shows the need to include the 1761 event in all seismic and tsunami hazard assessments in the Atlantic Ocean.

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

scholarly journals Ann Modeling For Predicting Car Travel Time using Bus As Probe

2019 ◽  
Vol 8 (12) ◽  
pp. 3260-3265
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Critical Issue ◽  
Transportation Systems ◽  
Travel Times ◽  
Time Data ◽  
Comparison Results ◽  
Greater Cairo ◽  
Artificial Neural Network Ann ◽  
Car Travel

The critical issue of Intelligent Transportation Systems (ITS) applications is obtaining the near real time information of travel times. This paper proposes a dependable model for predicting car travel time on urban roads in Greater Cairo using buses as probes. The GPS receivers, which are installed on test vehicles and buses, used to collect real travel time data along the urban roads. The travel times of bus and car are compared in order to recognize similarities and differences between the trip profiles of test vehicles and buses. According to the comparison results, the model is developed and validated using Artificial Neural Network (ANN) for estimating car travel time using buses’ travel time with acceptable level of accuracy equals 10.53%.

scholarly journals Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

2019 ◽  
Vol 48 (3) ◽  
pp. 276-289
Author(s):  
Akhilesh Jayan ◽  
Sasidharan Premakumari Anusha
Keyword(s):  
Travel Time ◽  
Performance Measure ◽  
Mixed Traffic ◽  
Travel Time Prediction ◽  
Time Data ◽  
Traffic Conditions ◽  
Time Prediction ◽  
Match Rate ◽  
Travel Time Data ◽  
Time Information

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.

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.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

Depth solution from borehole and travel time data using three-variable hypersurface splines

2001 ◽  
Vol 46 (3) ◽  
pp. 201-211 ◽  
Author(s):  
P.F. Xu ◽  
Z.W. Yu ◽  
H.Q. Tan ◽  
J.X. Ji
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data
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