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 A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

2018 ◽  
Vol 45 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Hang Yang ◽  
Yajie Zou ◽  
Zhongyu Wang ◽  
Bing Wu
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Prediction Accuracy ◽  
Travel Time Prediction ◽  
Time Data ◽  
Short Term ◽  
Time Prediction ◽  
Travel Time Data ◽  
Forecasting Performance ◽  
Fitting Problem

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

Travel time prediction in transport and logistics

2019 ◽  
Vol 49 (3) ◽  
pp. 277-306 ◽  
Author(s):  
Xia Li ◽  
Ruibin Bai ◽  
Peer-Olaf Siebers ◽  
Christian Wagner
Keyword(s):  
Travel Time ◽  
Model Fitting ◽  
Real Life ◽  
Gps Data ◽  
Travel Time Prediction ◽  
Time Data ◽  
Content Type ◽  
Time Prediction ◽  
Travel Time Data ◽  
Real Life Data

Purpose Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions. Design/methodology/approach The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case. Findings The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper. Research limitations/implications The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances. Practical implications The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions. Originality/value This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

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.

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.

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.

scholarly journals BUS TRAVEL TIME PREDICTION USING SUPPORT VECTOR MACHINES FOR HIGH VARIANCE CONDITIONS

Transport ◽  
2021 ◽  
Vol 36 (3) ◽  
pp. 221-234
Author(s):  
Anil Kumar Bachu ◽  
Kranthi Kumar Reddy ◽  
Lelitha Vanajakshi
Keyword(s):  
Support Vector Machines ◽  
Travel Time ◽  
Filter Method ◽  
Support Vector ◽  
Travel Time Prediction ◽  
Traffic Conditions ◽  
Time Prediction ◽  
Temporal Models ◽  
Vector Machines ◽  
Bus Travel Time

Real-time bus travel time prediction has been an interesting problem since past decade, especially in India. Popular methods for travel time prediction include time series analysis, regression methods, Kalman filter method and Artificial Neural Network (ANN) method. Reported studies using these methods did not consider the high variance situations arising from the varying traffic and weather conditions, which is very common under heterogeneous and lane-less traffic conditions such as the one in India. The aim of the present study is to analyse the variance in bus travel time and predict the travel time accurately under such conditions. Literature shows that Support Vector Machines (SVM) technique is capable of performing well under such conditions and hence is used in this study. In the present study, nu-Support Vector Regression (SVR) using linear kernel function was selected. Two models were developed, namely spatial SVM and temporal SVM, to predict bus travel time. It was observed that in high mean and variance sections, temporal models are performing better than spatial. An algorithm to dynamically choose between the spatial and temporal SVM models, based on the current travel time, was also developed. The unique features of the present study are the traffic system under consideration having high variability and the variables used as input for prediction being obtained from Global Positioning System (GPS) units alone. The adopted scheme was implemented using data collected from GPS fitted public transport buses in Chennai (India). The performance of the proposed method was compared with available methods that were reported under similar traffic conditions and the results showed a clear improvement.

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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