Seq2Img-DRNET: A travel time index prediction algorithm for complex road network at regional level

2021 ◽  
pp. 115554
Author(s):  
Xiujuan Xu ◽  
Yuzhi Sun ◽  
Yulin Bai ◽  
Kai Zhang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Regional Level ◽  
Prediction Algorithm ◽  
Time Index ◽  
Travel Time Index

scholarly journals Empirical Analysis for Measuring Travel Time Reliability on Road Network

2021 ◽  
Vol 23 (2) ◽  
pp. 100-107
Author(s):  
Muhammad Karami ◽  
Dwi Herianto ◽  
Siti A. Ofrial ◽  
Ning Yulianti
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Effects ◽  
Travel Time Reliability ◽  
Planning Time ◽  
The Road ◽  
Time Index ◽  
Buffer Time ◽  
Time Buffer ◽  
Travel Time Index

This research analyses the characteristics of travel time reliability for the road network in Kota Bandar Lampung. Therefore, travel time consists of access, wait and interchange time, while its reliability deals with variations of in-passenger/private cars time. Survey of travel time on each road was carried out for 12 hours (from 06.00 to 18.00) for five working days. Furthermore, the buffer time method was used to measure the characteristics of time travel reliability consisting of five measuring tools, namely planning time, planning time index, buffer time, buffer time index and travel time index. This research found that the temporal effects are the main factor that tends to affect travel time, whereas network effects are the second factor that tends to affect travel time. Furthermore, the regression equation was developed to express the effect of planning time (TPlan) and free-flow travel time on average travel time .

scholarly journals A weighted travel time index based on data from Uber Movement

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Renato S. Vieira ◽  
Eduardo A. Haddad
Keyword(s):  
Travel Time ◽  
Time Index ◽  
Travel Time Index

scholarly journals Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Haji Said Fimbombaya ◽  
Nerey H. Mvungi ◽  
Ndyetabura Y. Hamisi ◽  
Hashimu U. Iddi
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Moving Average ◽  
Wireless Sensor ◽  
Decision Algorithm ◽  
Low Speed ◽  
Congested Traffic ◽  
Flow Monitoring ◽  
Time Index ◽  
Travel Time Index

Traffic flow monitoring using magnetic wireless sensor networks in chaotic cities of developing countries represents an emergent technology. One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. The proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). The novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. The improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). The performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. The results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state.

scholarly journals TRAVEL TIME RELIABILITY INDICES FOR URBAN ROUTES IN BAGHDAD CITY

2021 ◽  
Vol 25 (5) ◽  
pp. 1-14
Author(s):  
Estabraq F. Alattar ◽  
◽  
Zainab A. alkaissi ◽  
Ali J. Kadem ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Reliability Indices ◽  
Time Index ◽  
North Direction ◽  
Buffer Time ◽  
Gps Devices ◽  
Travel Time Index ◽  
Areas Of Interest

Reliability is one of the main metrics of transport system efficiency and quality of service. For both travelers and transport management organizations, the high variance of road travel times has become a problem. Reliability has been identified as one of the main areas of interest of the Strategic Highway Research Plan II. In order to evaluate congestion and unexpected changes in travel time, reliability metrics are increasingly used. GPS devices provide for exact assessment of travel time for each connection along the routes used for this research. (14 Ramadan arterial street, Al-Karada arterial street and Damascus arterial street). A GPS-equipped instrumented car was used to gather 50 test runs at peak and off peak times. At peak and off peak hours, 50 test runs were obtained using a GPS-equipped instrumented car. Raising the buffer time index results in inferior conditions for reliability. A buffer index of AL- Karada street was created about 53% and 30% for Damascus street and finally for 14 Ramadan street which present a 29% buffer index for north direction. As for its southern direction 14 Ramadan street created a buffer index of about 65% and 33% for AL- Karada street and finally for Damascus street which present a 29% buffer index. In addition, travel time index for (14 Ramadan street, AL- Karada street and Damascus street) respectively is about 2.8 %, 3.3% and 2.6% for north direction, as for its southern direction the travel time index is obtained for (14 Ramadan street, AL- Karada street and Damascus street) respectively were a 3%,3.7%, and 2.5%. Finally, the 95% percentile travel time for observed three selected routes in this study, the extra delay was felt on each route (1627, 2212, and 1192) sec. for (14 Ramadan street, AL- Karada street and Damascus street) for north direction, as for its southern direction the extra delay that perceived on each route (2221, 2132, and 975) sec. for (14 Ramadan street, AL- Karada street and Damascus street) respectively.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Sign in / Sign up

Export Citation Format

Share Document